Reviewerly/exHarmony huggingface dataset:
Operational Semantics and Program Verification Using Many-Sorted Hybrid Modal Logic

We propose a general framework to allow: (a) specifying the operational semantics of a programming language; and (b) stating and proving properties about program correctness. Our framework is based on a many-sorted system of hybrid modal logic, for which we prove its completeness results. We believe that our approach to program verification improves over the existing approaches within modal logic as (1) it is based on operational semantics which enables a more natural description of the execution than Hoare-style weakest precondition used by dynamic logic; (2) since it is multi-sorted, it allows for a clearer encoding of semantics, with a smaller representational distance to its intended meaning.
Gan-Based Video Super-Resolution With Direct Regularized Inversion of the Low-Resolution Formation Model

While high and ultra high definition displays are becoming popular, most of the available content has been acquired at much lower resolutions. In this work we propose to pseudo-invert with regularization the image formation model using GANs and perceptual losses. Our model, which does not require the use of motion compensation, utilizes explicitly the low resolution image formation model and additionally introduces two feature losses which are used to obtain perceptually improved high resolution images. The experimental validation shows that our approach outperforms current video super resolution learning based models.
Honkling: In-Browser Personalization for Ubiquitous Keyword Spotting

Used for simple commands recognition on devices from smart speakers to mobile phones, keyword spotting systems are everywhere.Ubiquitous as well are web applications, which have grown in popularity and complexity over the last decade.However, despite their obvious advantages in natural language interaction, voice-enabled web applications are still few and far between.We attempt to bridge this gap with Honkling, a novel, JavaScript-based keyword spotting system.Purely client-side and cross-device compatible, Honkling can be deployed directly on user devices.Our inbrowser implementation enables seamless personalization, which can greatly improve model quality; in the presence of underrepresented, non-American user accents, we can achieve up to an absolute 10% increase in accuracy in the personalized model with only a few examples.
Knowledge States in the Learning of “Time”: Comparison of Primary Six Pupils’ from Different Types of Schools in Penang

The findings in this paper were a part of a bigger study which aimed to develop a cognitive diagnostic assessment [CDA] for primary mathematics learning of "Time".This paper aimed to discuss and compare the knowledge state of primary pupils from different types of schools in Penang.Knowledge state represented a pupil's mastery level on a defined set of attributes.These attributes were assessed by using a set of items and the responses were observed to find out a pupil's knowledge state.Two hundred and sixty-nine Primary Six pupils from 11 primary schools in Penang involved in the study.A set of CDA with 35 items consisting of four cognitive models were administered to the pupils.However, only three cognitive models were discussed in this paper.Students' responses were analysed by using Artificial Neural Network [ANN] to find the attribute probability of every pupil on each attribute.This probability was then classified and this set of classified attribute probability represented knowledge state of a pupil.Finding out the knowledge state of pupils helps teacher to investigate pupils' learning progress, identify pupils' misconception and thus make effective instructional decision.Results showed that Chinese Vernacular School pupils performed better than National School and Tamil Vernacular School pupils.Besides, the most common knowledge state in each cognitive model for each type of school was quite similar.The findings of this paper could be a preliminary step to demonstrate the usability and practicality of using CDA to obtain meaningful instructional inferences.
Powerset Convolutional Neural Networks

We present a novel class of convolutional neural networks (CNNs) for set functions, i.e., data indexed with the powerset of a finite set. The convolutions are derived as linear, shift-equivariant functions for various notions of shifts on set functions. The framework is fundamentally different from graph convolutions based on the Laplacian, as it provides not one but several basic shifts, one for each element in the ground set. Prototypical experiments with several set function classification tasks on synthetic datasets and on datasets derived from real-world hypergraphs demonstrate the potential of our new powerset CNNs.
Unsupervised System Combination for Set-Based Retrieval with Expectation Maximization

System combination has been shown to improve overall performance on many rank-based retrieval tasks, often by combining results from multiple systems into a single ranked list. In contrast, set-based retrieval tasks call for a technique to combine results in ways that require decisions on whether each document is in or out of the result set. This paper presents a set-generating unsupervised system combination framework that draws inspiration from evaluation techniques in sparse data settings. It argues for the existence of a duality between evaluation and system combination, and then capitalizes on this duality to perform unsupervised system combination. To do this, the framework relies on the consensus of the systems to estimate latent “goodness” for each system. An implementation of this framework using data programming is compared to other unsupervised system combination approaches to demonstrate its effectiveness on CLEF and MATERIAL collections.
Fast Convergence of Belief Propagation to Global Optima: Beyond Correlation Decay

Belief propagation is a fundamental message-passing algorithm for probabilistic reasoning and inference in graphical models. While it is known to be exact on trees, in most applications belief propagation is run on graphs with cycles. Understanding the behavior of ``loopy'' belief propagation has been a major challenge for researchers in machine learning, and several positive convergence results for BP are known under strong assumptions which imply the underlying graphical model exhibits decay of correlations. We show that under a natural initialization, BP converges quickly to the global optimum of the Bethe free energy for Ising models on arbitrary graphs, as long as the Ising model is \emph{ferromagnetic} (i.e. neighbors prefer to be aligned). This holds even though such models can exhibit long range correlations and may have multiple suboptimal BP fixed points. We also show an analogous result for iterating the (naive) mean-field equations; perhaps surprisingly, both results are ``dimension-free'' in the sense that a constant number of iterations already provides a good estimate to the Bethe/mean-field free energy.
Key Distribution Based on Survival Life Time with Y-00 Protocol in Optical Fiber Link

We propose a survival life time-based key distribution scheme with Y-00 protocol in optical fiber physical layer. Simulation results shows that with the proposed scheme, the bit-error rate of keys can reach 1.3%.
Adoption of Cloud Computing in Government Institutions in Nigeria

The study is on how to increase uptake of cloud computing in government institutions in Nigeria.The study sought to answer three research questions for this study:1. What are the requirements for increasing the uptake of cloud computing in government institutions in developing countries?2. How can the requirements for increasing the uptake of cloud computing in government institutions in developing countries be realized?3. How can improvement in the uptake of cloud computing in government institutions in developing countries be tested and validated?Cloud computing is changing how technology as we know it.It is changing the way that organizations operate.It gives institutions small access to the best technology without having to invest much in it.It solves the problems related with the traditional computing.Cloud computing has opened new ways of using computing resources and capabilities.The research was aimed at finding out uptake of the cloud computing within government institutions in Nigeria, the expected benefits, challenges faced when implementing the technology and the solutions to these problems.Primary data was collected from institutions that use cloud technology.The data collected was analyzed and collaborated against the secondary literature.Though cloud computing solves problem of owning your own infrastructure but Internet connection is needed to have access to the intended service.In Nigeria, the communication infrastructure is a major challenge and it hinders the growth of the technology.Lack of stable power, wreckage of infrastructure, insecurity and lack of awareness are among the challenges that were highlighted as the major problems.These problems can be addressed by having qualified personnel to handle security challenges, investing more on communication infrastructure and awareness on what the technology is about among others.The future of cloud computing in Nigeria is bright
Adaptive Near Duplicate Image Retrieval Using SURF and CNN Features

In this paper, we present an adaptive approach in order to match and retrieve near duplicate images at different scales.Matching only local Features does not necessarily identify visually similar images.Global features are fast at matching but less accurate.Many existing methods either use local features or CNN features for image or video retrieval task.In this paper, we combined the use of SURF local points and CNN features extracted around SURF points in order to match near duplicate image pairs.Image pairs are segmented into blocks and CNN features of the image block containing matched SURF features are extracted and matched.Regions around matched image blocks are grown adaptively and matching is carried out until CNN mismatch is observed.To verify our proposed approach, experiments are carried out on benchmarking California-ND and Holiday dataset.Compared to traditional approaches for image retrieval, our approach not only retrieves relevant images but also provides detail of localized matched patch.For California-ND dataset and Holiday dataset, we achieve remarkable mAP (mean average precision) score up to 0.86 and 0.74 respectively.
Application of Ontology in the Web Information Retrieval

In this paper, the research advances of ontology and its application are reviewed firstly. With the development of ontology technology, subject-oriented web information retrieval technology combining ontology has been becoming one of the hot scientific issues. The innovative method of the semantic web technology combined with the traditional information retrieval technology is put forward, and the related algorithm based on ontology for judging the relevancy with different topics is also represented, and has proved to be effective in given experiments.
Implementation of Smart Learning in Korean Education Using the Smart Classroom Response System

Journal of the International Network for Korean Language and Culture 16-2, 93-119. This paper suggests a practical way to implement smart learning in Korean language classes: smart classroom response system. A Classroom response system is a pedagogical tool that enables students to use a keypad in order to respond to questions posed in class through the use of a digital presentation. The Smart Classroom Response System is a smartphone-based Classroom Response System. The rapid development of Information Communication Technology(ICT) has changed educational concepts and methods of learning. Korean language learners who are digital natives prefer digital devices such as computers, smartphones, tablets and personal computers to textbooks and lectures. It is necessary to look for an alternative teaching-learning tool that meets learners’ needs. This paper provides an overview of the Smart Classroom Response System, including its definition, functions, selection criteria, and expected pedagogical effectiveness. Further, it proposes a teaching-learning model for intermediate level Korean reading classes using Kahoot, a Smart Classroom Response System tool.(Sangmyung University)
INTEREST: INteractive Tool for Exploring REsults from Simulation sTudies

Simulation studies allow us to explore the properties of statistical methods. They provide a powerful tool with a multiplicity of aims; among others: evaluating and comparing new or existing statistical methods, assessing violations of modelling assumptions, helping with the understanding of statistical concepts, and supporting the design of clinical trials. The increased availability of powerful computational tools and usable software has contributed to the rise of simulation studies in the current literature. However, simulation studies involve increasingly complex designs, making it difficult to provide all relevant results clearly. Dissemination of results plays a focal role in simulation studies: it can drive applied analysts to use methods that have been shown to perform well in their settings, guide researchers to develop new methods in a promising direction, and provide insights into less established methods. It is crucial that we can digest relevant results of simulation studies. Therefore, we developed INTEREST: an INteractive Tool for Exploring REsults from Simulation sTudies. The tool has been developed using the Shiny framework in R and is available as a web app or as a standalone package. It requires uploading a tidy format dataset with the results of a simulation study in R, Stata, SAS, SPSS, or comma-separated format. A variety of performance measures are estimated automatically along with Monte Carlo standard errors; results and performance summaries are displayed both in tabular and graphical fashion, with a wide variety of available plots. Consequently, the reader can focus on simulation parameters and estimands of most interest. In conclusion, INTEREST can facilitate the investigation of results from simulation studies and supplement the reporting of results, allowing researchers to share detailed results from their simulations, readers to explore them freely.
Mathematical Methods of Image Processing

The purpose of the study is to study the specificity of image encoding by spline interpolation, and the equation of the indicated method with other mathematical methods of encoding and image processing.Research methods. The mathematical and algorithmic models and methods of solving the problem of smoothing on the basis of spline approximation, as well as the possibility of using an appropriate mathematical apparatus for encoding and image processing.The novelty of the research is the isolation of the compression algorithm of images based on the methods of spline approximation. This approach to image processing can not only reduce the size of image files, but also choose the desired quality of recovery, depending on the further use of the image.Conclusions. The work compares existing image coding methods and points out the benefits of using spline interpolation when encoding and decoding images.
Cost-Bounded Active Classification Using Partially Observable Markov Decision Processes

Active classification, i.e., the sequential decision making process aimed at data acquisition for classification purposes, arises naturally in many applications, including medical diagnosis, intrusion detection, and object tracking. In this work, we study the problem of actively classifying dynamical systems with a finite set of Markov decision process (MDP) models. We are interested in finding strategies that actively interact with the dynamical system, and observe its reactions so that the true model is determined efficiently with high confidence. To this end, we present a decision-theoretic framework based on partially observable Markov decision processes (POMDPs). The proposed framework relies on assigning a classification belief (a probability distribution) to each candidate MDP model. Given an initial belief, some misclassification probabilities, a cost bound, and a finite time horizon, we design POMDP strategies leading to classification decisions. We present two different approaches to find such strategies. The first approach computes the optimal strategy “exactly” using value iteration. To overcome the computational complexity of finding exact solutions, the second approach is based on adaptive sampling to approximate the optimal probability of reaching a classification decision. We illustrate the proposed methodology using two examples from medical diagnosis and intruder detection.
Data Glove for American Sign Language Alphabet and Numbers (1-9) Translation System

A data glove, installed with flex sensors, force sensitive sensors and accelerometer, is built to translate the sign language to text and speech using Arduino Mega as the processing unit and SpeakerJet as voice and sound synthesizer.It is designed not only to assist a hearing-impaired person in conveying messages but also to other people who want to learn the sign language.As an initial operation, the system is designed to translate American Sign Language alphabet and numbers (1-9) to understand how each signing affect the sensors and hence the accuracy of the translation.
Intelligent control systems : Learning, interpreting, verification

Automatic control is a technique about designing control devices for controlling ma- chinery processes without human intervention. However, devising controllers using conventional control theory requires first principle design on the basis of the full under- standing of the environment and the plant, which is infeasible for complex control tasks such as driving in highly uncertain traffic environment. Intelligent control offers new op- portunities about deriving the control policy of human beings by mimicking our control behaviors from demonstrations. In this thesis, we focus on intelligent control techniques from two aspects: (1) how to learn control policy from supervisors with the available demonstration data; (2) how to verify the controller learned from data will safely control the process.
AGRR 2019: Corpus for Gapping Resolution in Russian

This paper provides a comprehensive overview of the gapping dataset for Russian that consists of 7.5k sentences with gapping (as well as 15k relevant negative sentences) and comprises data from various genres: news, fiction, social media and technical texts.The dataset was prepared for the Automatic Gapping Resolution Shared Task for Russian (AGRR-2019) -a competition aimed at stimulating the development of NLP tools and methods for processing of ellipsis.In this paper, we pay special attention to the gapping resolution methods that were introduced within the shared task as well as an alternative test set that illustrates that our corpus is a diverse and representative subset of Russian language gapping sufficient for effective utilization of machine learning techniques.
Algorithm of image classification based on Atrous-CNN

An image classification algorithm based on atrous convolution neural network (Atrous-CNN) is proposed in this paper, in which atrous convolution is used to replace the convolution layer in the traditional convolutional neural network. When the receptive field and other network parameters are the same, the training accuracy and testing accuracy of the two algorithms are tested. Mnist handwritten digital recognition data set was adopted as the training set and test set of the network model. The three network models were trained 500,1000,5000, 10000 times respectively, and their accuracy and training duration were recorded. Through experiments, compared with the traditional convolutional neural network, the Atrous-CNN has less time consuming and better performance than the traditional convolutional neural network with the same accuracy.
Towards interactive recommending in model-based collaborative filtering systems

Numerous attempts have been made for increasing the interactivity in recommender systems, but the features actually available in today's systems are in most cases limited to rating or re-rating single items. We present a demonstrator that showcases how model-based collaborative filtering recommenders may be enhanced with advanced interaction and preference elicitation mechanisms in a holistic manner. Hereby, we underline that by employing methods we have proposed in the past it becomes possible to easily extend any matrix factorization recommender into a fully interactive, user-controlled system. By presenting and deploying our demonstrator, we aim at gathering further insights, both into how the different mechanisms may be intertwined even more closely, and how interaction behavior and resulting user experience are influenced when users can choose from these mechanisms at their own discretion.
Historical Models and Serial Sources

Serial sources such as records, registers, and inventories are the ‘classic’ sources for quantitative history. Unstructured, narrative texts such as newspaper articles or reports were out of reach for historical analyses, both for practical reasons&amp;nbsp;— availability, time needed for manual processing — and for methodological reasons: manual coding of texts is notoriously difficult and hampered by low inter-coder reliability. The recent availability of large amounts of digitized sources allows for the application of natural language processing, which has the potential to overcome these problems. However, the automatic evaluation of large amounts of texts — and historical texts in particular — for historical research also brings new challenges. First of all, it requires a source criticism that goes beyond the individual source and also considers the corpus as a whole. It is a well-known problem in corpus linguistics to determine the ‘balancedness’ of a corpus, but when analyzing the content of texts rather than ‘just’ the language, determining the ‘meaningfulness’ of a corpus is even more important. Second, automatic analyses require operationalizable descriptions of the information you are looking for. Third, automatically produced results require interpretation, in particular, when — as in history — the ultimate research question is qualitative, not quantitative. This, finally, poses the question, whether the insights gained could inform formal, i.e., machine-processable, models, which could serve as foundation and stepping stones for further research.
Conditional Adaptation Deep Networks for Unsupervised Cross Domain Image Classifcation

Unsupervised domain adaptation aims at improving the performance on an unknown target domain by utilizing the knowledge learned from a related source domain. In this paper, we proposed a conditional adaptation deep network (CADN) to deal with the unsupervised domain adaptation problem. CADN is implemented based on a deep neural network and attempts to learn domain invariant features. We present a conditional adaptation strategy to reduce the domain distribution discrepancy and to solve the category mismatch and class prior bias, which are usually ignored in marginal adaptation approaches. We perform a set of comparative experiments on the standard domain adaptation benchmarks. The results demonstrate that the proposed CADN outperforms previous adaptation methods.