Reviewerly/exHarmony huggingface dataset:
Improving the Cybersecurity and Information Assurance Pipeline

Over a two year period, Pennsylvania College of Technology faculty participated in an NSF funded grant to extend the cybersecurity pipeline into the high schools by delivering cybersecurity concepts in an after school program where students earn college credit. This poster explores the profile of incoming students and the impact of the program.
Kernel-Based and Bayesian Methods for Numerical Integration

Kernel-based methods provide a flexible toolkit for approximation of linear functionals. Importantly, these methods carry a probabilistic interpretation: a worst-case optimal method in the reproducing kernel Hilbert space induced by the kernel being used can be equivalently formulated as the posterior mean of a Gaussian process (GP) with the same covariance kernel; the worst-case error corresponds to the GP posterior standard deviation. This connection makes it possible to speak of and quantify, in a statistically principled way, uncertainty in the approximation provided by a kernel method. Consequently, these methods can be viewed as probabilistic numerical methods that interpret numerical approximation as a statistical inference problem and attempt to endow the solution of a numerical problem with a full non-degenerate posterior probability distribution. Unfortunately, both the kernel and GP formulations suffer from cubic computational and quadratic memory cost in the number of data points. Furthermore, because standard versions of kernel-based methods do not typically coincide with any ''classical'' method of numerical analysis in a way that would give rise to a corresponding non-degenerate GP posterior (with the exception of spline methods), there has been no straightforward way to interpret classical methods as useful statistical inference procedures within the GP framework. This thesis studies numerical approximation of analytically intractable integrals by the means of kernel and Bayesian cubature rules, the former worst-case optimal cubature methods and the latter posteriors of GPs. The first contribution is the development of algorithms that significantly reduce the computational cost of these cubature methods by employing point sets that can be expressed as unions of fully symmetric sets. The resulting algorithm is non-approximate, computationally competitive and scalable, flexible, and algorithmically simple, enabling application of kernel and Bayesian cubature rules to integration problems involving up to millions of points. Additionally, a closed-form approximation linked to the Gauss–Hermite quadrature is proposed for the special case of one-dimensional integration against the standard Gaussian measure. The second main contribution is the study of different kernels and GP modelling choices that yield Bayesian cubature rules corresponding to classical cubature methods, such as Gaussian cubatures and uniformly weighted Monte Carlo rules. The proposed Bayes–Sard framework is general and capable of probabilistic reproduction of virtually any cubature rule.; Ydinperusteiset menetelmat ovat joustava joukko lineaarifunktioiden approksimointia varten kehitettyja algoritmeja. Mika tarkeinta, nailla menetelmilla on todennakoisyysteoreettinen tulkinta: menetelma, joka on pahimmassa tapauksessa optimaalinen reproduktiivisen ytimen Hilbertin avaruudessa, on mahdollista ilmaista taysin ekvivalentista gaussisen prosessin posteriorikeskiarvona ja sen pahimman tapauksen virhe vastaavana posteriorikeskihajontana.…
Using Aspect-Based Analysis for Explainable Sentiment Predictions

Sentiment Analysis is the study of opinions produced from human written textual sources and it has become popular in recent years. The area is commonly divided into two main tasks: Document-level Sentiment Analysis and Aspect-based Sentiment Analysis. Recent advancements in Deep Learning have led to a breakthrough, reaching state-of-the-art accuracy scores for both tasks, however, little is known about their internal processing of these neural models when making predictions. Aiming for the development of more explanatory systems, we argue that Aspect-based Analysis can help deriving deep interpretation of the sentiment predicted by a Document-level Analysis, working as a proxy method. We propose a framework to verify if predictions produced by a trained Aspect-based model can be used to explain Document-level Sentiment classifications, by calculating an agreement metric between the two models. In our case study with two benchmark datasets, we achieve $$90\%$$ of agreement between the models, thus showing the an Aspect-based Analysis should be favoured for the sake of explainability.
Rate of divergence of the nonparametric likelihood ratio test for Gaussian mixtures

We study a nonparametric likelihood ratio test (NPLRT) for Gaussian mixtures.It is based on the nonparametric maximum likelihood estimator in the context of demixing.The test concerns if a random sample is from the standard normal distribution.We consider mixing distributions of unbounded support for alternative hypothesis.We prove that the divergence rate of the NPLRT under the null is bounded by log n, provided that the support range of the mixing distribution increases no faster than (log n/ log 9) 1/2 .We prove that the rate of √ log n is a lower bound for the divergence rate if the support range increases no slower than the order of √ log n.Implications of the upper bound for the rate of divergence are discussed.
Dynamic Label Correction for Distant Supervision Relation Extraction via Semantic Similarity

It was found that relation extraction (RE) suffered from the lack of data. A widely used solution is to use distant supervision, but it brings many wrong labeled sentences. Previous work performed bag-level training to reduce the effect of noisy data. However, these methods are suboptimal because they cannot handle the situation where all the sentences in a bag are wrong labeled. The best way to reduce noise is to recognize the wrong labels and correct them. In this paper, we propose a novel model focusing on dynamically correcting wrong labels, which can train models at sentence level and improve the quality of the dataset without reducing its quantity. A semantic similarity module and a new label correction algorithm are designed. We combined semantic similarity and classification probability to evaluate the original label, and correct it if it is wrong. The proposed method works as an additional module that can be applied to any classification models. Experiments show that the proposed method can accurately correct wrong labels, both false positive and false negative, and greatly improve the performance of relation classification comparing to state-of-the-art systems.
Exploring the Basic Concepts of HDR: Dynamic Range, Gamma Curves, and Wide Color Gamut

Abstract High dynamic range (HDR) offers a wealth of opportunities, in terms of immersion and innovation in storytelling and special effects. But what does it entail, exactly? Here, in the second of a two‐part series, we consider the concepts, tools, and specifications that are (and are not yet) in place to generate and broadcast visually compelling HDR content.
Context Enrichment Model Based Framework for Sentiment Analysis

Online medium has become popular among knowledgeable society as a new emerging platform of information gathering and sharing where their thoughts and opinions are considered important in many aspects of nation building. The overwhelmed of online opinionated data has created a great challenge for researchers to mine sentiments accurately. Sentiment analysis or also known as opinion mining helps to understand and analyse those opinions due to the explosively growths of online opinionated data. This refers to the process of identifying and classifying the sentiments into several orientations, whether positive or negative opinions. Classifying sentiments based on the correct context remains a challenge, as the orientation of sentiments could change when the context changes. Existing sentiment analyser tend to classify sentiments based on keywords rather that the meaning of words. The real meaning of sentiment texts is often interpreted differently and consequently lead to poor classification results. Context plays an important role in analysing and classifying sentiment texts. This paper proposes a context enrichment model based framework for sentiment analysis in predicting the orientation of sentiments. The framework aims to provide a workflow for sentiments classification by considering the context and semantic information. There are four components in this framework: pre-processing, context enrichment model, classification and performance evaluation. This study is expected to create a new technique of classifying sentiments with more enriched semantic information.
USE OF DIGITAL TECHNOLOGIES IN THE EDUCATIONAL ENVIRONMENT OF HIGHER EDUCATION

The article systematized regulatory documents that characterize the need for transformation processes in education, which include updating the knowledge system and the need for adapting the teacher of higher education institutions to changes in interpersonal interaction with the use of digital technologies. The notions of "digital environment", "digital technologies", "digitalization" are disclosed. It was clarified that there was no single approach to the definition of "digital technologies" in scientific circles and national legislative documents. The peculiarities of the digital technologies implementation created by the local network of higher educational institutions through the module of administration and adjustment of the users rights, which ensures the implementation of participants activities in the educational process without territorial, geographical, time constraints, are described. The essential characteristics of educational tasks and digital technologies used for their representation are described that allows a teacher to create conditions for active student learning activities in the educational electronic environment. Possibilities of updating forms of interpersonal interaction in the context of educational tasks are generalized. The digital technologies providing feedback are characterized, in particular, through the final and formative assessment. There are three criteria for selection of digital means and technologies: provision of interactive information interaction in the learning process; adaptation to the individual characteristics of students; possibility of synchronous or asynchronous interaction of an educational activity participants. The effectiveness of using digital technologies in the educational process of the higher institution education has been revealed according to the survey of students and teachers. It is revealed that the efficiency of digital technologies' use depends and is determined by a higher educational institution teacher’s readiness for this process .
3DPlasticToolkit: Plasticity for 3D User Interfaces

The goal of plasticity is to ensure usability continuity whatever the context of use. This context must be modeled into the system and possibly taken into account to adapt the final application. The difficulty to handle plasticity for 3D applications comes from the lack of solutions for developers and designers to model and take these constraints into account. This paper introduces new models designed to deal with plasticity for Virtual Reality (VR) and Augmented Reality (AR). These models are implemented in a software solution: 3DPlasticToolkit. It aims to provide a solution for developing 3D applications that can automatically fit any context of use. This context of use includes a set of 3D hardware and environmental constraints, such as user preferences and available interaction devices. 3DPlasticToolkit includes tools for modeling this context and for creating application components independently from it. We propose an adaptation engine based on a scoring algorithm to dynamically create the most suited 3D user interfaces according to the context of use at runtime. We use a furniture planning scenario to show how these adaptations can impact interactions and content presentation.
Improved Mix Column Computation of Cryptographic AES

With today's development and expansion of networks and internet-connected devices, information security is an issue of increasing concern. Confidentiality is one of the focuses in network security for digital communication systems, where large data blocks go through a cryptographic algorithm with a cipher key that increases the security and complexity of the output ciphertext. For the past several years, multiple security algorithms have been developed and utilized in the data encryption process, such as the Data Encryption Standard (DES), Triple Data Encryption Standard (3DES), and the current one, designated by the U.S. National Institute of Standards and Technology (NIST), the Advanced Encryption Standard (AES). AES is a symmetric encryption algorithm that has a minimum input data block size of 128-bits which undergo a series of permutations, substitutions, and digital logic operations over several rounds. Encryption algorithms are always improving on ciphertext complexity, required hardware storage allocation, and execution time. Field Programmable Gate Arrays (FPGA's) are a hardware alternative for encryption algorithm implementation because, although the logic units in it are fixed, the functions and interconnections between them are based on the user's design which allow for improvement. The research presented focuses on the development and analysis of an efficient AES-128 Mix Columns algorithm implementation, utilized in the data block encryption rounds, on an Altera Cyclone IV FPGA using the Intel Quartus II software and Verilog Hardware Description Language.
Peculiarities of Application of Technologies of Distributed Registers and Blockchain (Blockchain) for the Popular Vote

The modernization of democratic institutions has been greatly influenced by the intensive development of technology. Various innovations in the field of digital communications have affected a rather traditional sphere of popular votings. The widespread introduction of the distributed ledger technology has enormously changed approaches to organizing them. Distributed registers gained the widest popularity after the technology of the chain of blocks (blockchain) was introduced. Despite the fact that initially this technology was considered exclusively as an element of the development of information, and later financial technologies, at the present stage it is gradually becoming increasingly common in other areas of human activity due to a high degree of security and confidentiality. The paper deals in detail with the world practice concerning using this technology in popular voting. Also, the author analizes the technical solutions applied in the most actively developing projects aimed at developing a software used to conduct electronic voting with the use of blockchain technology. The article investigates some problems of voting with the use of blockchain technology, such as identification and secrecy of the vote.
Face Age Transformation with Progressive Residual Adversarial Autoencoder

Face age transformation is an important issue in many applications. While unidirectional and short-span face ageing has achieved remarkable progress, it remains a challenging problem to generate both younger-look and older-look face images over long age span. In this paper, we present a progressive residual adversarial autoencoder (PRAA) model for bidirectional and long-span face age transformation. Given an input face image, our model aims to synthesize face images of its younger looks (face rejuvenation) and older looks (face ageing). The PRAA contains adversarial generators and discriminators where age information is encoded as latent features. It adopts a residual-encoding strategy by which the original face images and the residual face images are jointly encoded. We adopt a progressive multi-scale method to train the network, by which our model can capture both the global structure and the local detail changes in face age transformation. We test our model on challenging data and the experimental results prove the strength of our method.
Utilizing Observed Information for No-Communication Multi-Agent Reinforcement Learning toward Cooperation in Dynamic Environment

This paper proposes a multi-agent reinforcement learning method without communication toward dynamic environments, called profit minimizing reinforcement learning with oblivion of memory (PMRL-OM). PMRL-OM is extended from PMRL and defines a memory range that only utilizes the valuable information from the environment. Since agents do not require information observed before an environmental change, the agents utilize the information acquired after a certain iteration, which is performed by the memory range. In addition, PMRL-OM improves the update function for a goal value as a priority of purpose and updates the goal value based on newer information. To evaluate the effectiveness of PMRL-OM, this study compares PMRL-OM with PMRL in five dynamic maze environments, including state changes for two types of cooperation, position changes for two types of cooperation, and a combined case from these four cases. The experimental results revealed that: (a) PMRL-OM was an effective method for cooperation in all five cases of dynamic environments examined in this study; (b) PMRL-OM was more effective than PMRL was in these dynamic environments; and (c) in a memory range of 100 to 500, PMRL-OM performs well.
Text Classification Using Gated and Transposed Attention Networks

Text classification models based on recurrent neural networks have poor parallel processing ability, as they need to be trained word by word. The model proposed in this paper has increased parallel computing capability compared to the one based on the recurrent neural networks. Text classification models based on convolution neural network struggle to obtain contextual information as they are limited by the size of the convolutional kernel. In this paper, we propose an attention network based on a gated control and transposed structure, which makes the network able to understand context and more efficiently extract important features from text. Experiments are carried out on six commonly used text classification datasets. The experimental results show that the model presented in this paper achieves the state of the art performance on four datasets and is very competitive on the other two. In addition, this paper also explores the effects of gate control and transposition on the whole model.
Digital Flipbook Media as a Media for Health Promotion in Youth: Research and Development

Digital Flipbook is one of the multimedia-based media that is recommended to be developed because it can be accessed with gadgets or laptops. This research is Research and Development level 4, which is researching and testing in creating new products. Researchers developed the Digital Flip Book with the title Me: Teenagers and My Health. The purpose of developing this media is to increase the knowledge and attitudes of adolescents after reading the Digital Flipbook. This media design is a cover page, content (introduction to adolescent reproductive health situations, adolescent definitions, risk behaviors in adolescents which consists of understanding, impact, and prevention of premarital sex, violence, and early marriage), and improving adolescent reproductive health. The results of media development from media experts said that based on the assessment aspects, namely simplicity, cohesiveness, emphasis, balance, shape and color, the results obtained 91.67%.The media to be used must be simple, easy to use on a gadget or laptop and balanced color combination. Material expert assesses 97.22%.The language used in media must be easily understood, not a textbook, by the target of adolescents. Thus, a digital flipbook entitled "Me: Teenagers and My Health" is suitable for use in health promotion among adolescents and furtherfield testing. This media has been tested on target, with the result that knowledge is good as much as 51.9% and a positive attitude as much as 59%. Thus, the media has been used. However This process affects the output of learning using the application as a medium for health promotion for adolescents.
Detecting Alzheimer's Disease by estimating attention and elicitation path through the alignment of spoken picture descriptions with the picture prompt

Cognitive decline is a sign of Alzheimer's disease (AD), and there is evidence that tracking a person's eye movement, using eye tracking devices, can be used for the automatic identification of early signs of cognitive decline. However, such devices are expensive and may not be easy-to-use for people with cognitive problems. In this paper, we present a new way of capturing similar visual features, by using the speech of people describing the Cookie Theft picture - a common cognitive testing task - to identify regions in the picture prompt that will have caught the speaker's attention and elicited their speech. After aligning the automatically recognised words with different regions of the picture prompt, we extract information inspired by eye tracking metrics such as coordinates of the area of interests (AOI)s, time spent in AOI, time to reach the AOI, and the number of AOI visits. Using the DementiaBank dataset we train a binary classifier (AD vs. healthy control) using 10-fold cross-validation and achieve an 80% F1-score using the timing information from the forced alignments of the automatic speech recogniser (ASR); this achieved around 72% using the timing information from the ASR outputs.
USE OF MOBILE DEVICE SENSORS TO CONDUCT THE PHYSICAL EXPERIMENT

The concept of "sensor" in the system of physical experiment at school is considered in the article. The possibility of using sensors in physics lessons is substantiated: transformation of an input signal into an output is accompanied by transformation of one type of energy into another (according to the law of conservation of energy), and the functioning of the sensors are based on physical phenomena (physical effects or principles), which are described by the relevant physical laws. The article deals with the methodical aspects of using the Google Science Journal mobile application in physics lessons. This application allows you to use the sensors of your mobile device for a physical experiment. As an example we consider the frontal laboratory work "Determination of the period of oscillation of the mathematical pendulum". The method of its carrying out is offered in two approaches: the first one involves the traditional technique of conducting the experiment, and the second approach is using the mobile application Google Science Journal. The article shows that the use of smartphone sensors in physics lessons has perspectives in the context of STEM education. Thus, the use of the considered application is of current importance and requires further scientific and methodological research on its use in the high school physical experimentation system. The Science Journal mobile application can be used to connect external sensors, which will have a positive impact on the introduction of STEM education, and to use Arduino in the demonstration of physical experiments by a physics teacher. Connecting sensors using an Arduino microcontroller is particularly promising in creative lab work on physics.