Reviewerly/exHarmony huggingface dataset:
Implementation of Text Classification Model Based on Recurrent Neural Networks

In this report, a deep learning network architecture, which combined with the architecture of convolutional neural network, the architecture of recurrent neural network is proposed and implemented to do the text classification with the pre-trained word vectors. The proposed method set the word vector be as a static lookup table without updating, and the network still can ignore the noise which caused by missing words. The experimental results show that the accuracy of this study is consistent with the accuracy of other studies. It is shown the feasibility of this architecture. And has the following advantages: the accuracy rate of this architecture is higher than that of recurrent neural network only; compared with the convolutional neural network, the accuracy results are more stable; and less epoch is used to get stable results. But the shortcoming of this proposed architecture is that the training time will consumed much time.
An implementation method for Arabic keyword tendency using decision tree

Generally, keyword recurrences change over certain periods of time. Traditional approaches estimated classes (increasing, relatively constant and decreasing) that indicate keywords' attribute changes in a document over certain periods of time using a decision tree. Furthermore, all earlier approaches are based on keywords in English and French languages. Therefore, the extension of keywords to other languages such as Arabic could strengthen further researches in this domain. This paper introduces new method to extract Arabic keywords from corpora based on their recurrences changes in a document over given periods of time using a decision tree. The new approach is applied on new data set field (computer science) which makes it different to traditionally used methods. The comparison between the manually classified results and the evaluation of the decision tree results reveals that F-measures of decreasing, relatively constant and increasing classes were 0.811, 0.917, and 0.897 which indicates effectiveness of our method.
Sketching the Concert for Piano and Orchestra

This chapter discusses Cage’s sketches for the <italic>Concert for Piano and Orchestra</italic>. It outlines both what can be said about Cage’s compositional process on the basis of sheets which directly detail aspects of it <italic>and</italic> on the basis of the traces of his process which can be found in the manuscript of the <italic>Solo for Piano</italic>. It details his work with jazz musicians in developing the instrumental parts for the piece, as well as unpacking further aspects of the actual process with reference to <italic>Music for Piano</italic>.
Representation-Based Completion of Knowledge Graph with Open-World Data

The method of knowledge graph completion (KGC) by adding external knowledge with new entities was discussed in this paper. Adopting the TransE-based representation of relations and triples in Knowledge Graph, we extract triples from open-world data and evaluate their correctness to fulfill KGC, where vectors are used for similarity evaluation. From the “structural” point of view, triples were first built from open-world data according to the similarity between TransE-based representation of pairs of entities and that of relations in KG. From the “semantic” point of view, the correctness of each external triple was evaluated by measuring the distance in the triple locally and ranking in the entire KG globally. ON the FreeBase and DBPedia KGs by different KG representation models and KGC methods, experimental results show that our proposal outperforms some state-of-the-art methods.
PEMANFAATAN DATA INSTAGRAM UNTUK MENGETAHUI REPUTASI TEMPAT WISATA DI LOMBOK

Social media currently has an extraordinary influence in decision making, including in the tourism industry to find out the reputation and popularity of a tourist destination. At present the use and use of social media is not only limited to entertainment media but also as a medium or a place to share information both information that is considered important or not important and neglected. In this study, we utilize data scattered through Instagram posts related to tourist destinations to be used as references to find out the reputation of tourist destinations on the island of Lombok. The data used comes from Instagram with a total of 600 datasets, using three keywords namely beach, dyke and hill. The method used is the Naive Bayes Classifier which will be used to classify postings and Instagram comments by classifying into positive, negative and neutral posts to tourist destinations. The results of this study can show that the accuracy of naive bayes is 59%, while tourist attractions or tourist destinations which are categorized as popular for the beach are Kuta Beach with 85% percentage and for the Gili category that is 47% Gili Air namely, mountain Mount Rinjani with a percentage of 60%.
Random number generators produce collisions: Why, how many and more

It seems surprising that when applying widely used random number generators to generate one million random numbers on modern architectures, one obtains, on average, about 116 collisions. This article explains why, how to mathematically compute such a number, why they often cannot be obtained in a straightforward way, how to numerically compute them in a robust way and, among other things, what would need to be changed to bring this number below 1. The probability of at least one collision is also briefly addressed, which, as it turns out, again needs a careful numerical treatment. Overall, the article provides an introduction to the representation of floating-point numbers on a computer and corresponding implications in statistics and simulation. All computations are carried out in R and are reproducible with the texttt included in this article.
ASSESSMENT OF FACILITIES MANAGEMENT PERFORMANCE ON OPERATION AND MAINTENANCE ASPECTS IN MALAYSIAN TECHNICAL UNIVERSITIES NETWORK

This paper recommends discourse feeling acknowledgment from discourse signal dependent on highlights examination and PNN-classifier. The arrangement of acknowledgment incorporates discovery of discourse feelings, extraction and determination of highlights, lastly characterization. These highlights are valued to segregate the greatest number of tests precisely and the PNN classifier dependent on discriminant investigation is utilized to characterize the six distinctive articulations. The reproduced outcomes will be indicated that the channel occupied component extortion with utilized distribution presents much better exactness with less algorithmic unpredictability than other discourse feeling articulation acknowledgment draws near.
A Multi-objective Optimization Scheduling Algorithm in Cloud Computing

Task scheduling plays a major role in cloud computing that creates a direct impact on performance issues and reduces the system load. In this paper, a novel task scheduling algorithm has proposed for the optimization of multi-objective problem in the cloud environment. It addresses a model to define the demand of resources by a job. It gives a relationship between the resources and costs within a project. The scheduling of multi-objective problem is optimized with the use of ant colony optimization algorithm. The evaluation of the cost and performance of the task has two major constraints considered as makespan and budget’s cost. The two considered constraints will make the algorithm to achieve the optimal result within time and enhance the quality of performance of the system considered. This method is very powerful than other methods with single objectives considered such as makespan, utilization of resources, violation of deadline rate and cost.
Ranking Georeferences for Efficient Crowdsourcing of Toponym Annotations in a Historical Corpus of Alpine Texts.

This paper presents a simple method to rank georeference candidates to optimally support the workflow of a citizen science web application for toponym annotation in historical texts. We implement the general idea of efficient crowdsourcing based on human and artificial intelligence working hand in hand. For named entity recognition, we apply recent neural pretraining-based NER tagger methods. For named entity linking to geographical knowledge bases, we report on georeference ranking experiments testing the hypothesis that textual proximity indicates geographic proximity. Simulation results with online reranking that immediately integrates user verification show further improvements.
Normalizing Flow Model for Policy Representation in Continuous Action Multi-agent Systems

Neural networks that output the parameters of a diagonal Gaussian distribution are widely used in reinforcement learning tasks with continuous action spaces. They have had considerable success in single-agent domains and even in some multi-agent tasks. However, general multi-agent tasks often require mixed strategies whose distributions cannot be well approximated by Gaussians or their mixtures. This paper proposes an alternative for policy representation based on normalizing flows. This approach allows for greater flexibility in action distribution representation beyond mixture models. We demonstrate their advantage over standard methods on a set of imitation learning tasks modeling human driving behaviors in the presence of other drivers.
Measuring the Value of Data Curation: Preliminary Results from the Data Curation Network

April 2019 poster presented at the Research Data Alliance 13th Plenary in Philadelphia, Pennsylvania.
Q-PD: query graph extension framework using predicate-based RDF on linked open data

Linked open data (LOD) has emerged as a new platform for sharing and integrating information about not only web resources but also physical resources. However, as the volume of LOD increases explosively, it becomes difficult to query the LOD to discover the high-quality information. To obtain the desired information from the LOD, we propose the query graph extension framework (Q-PD framework) that can extend the users' queries. To do so, the Q-PD framework identifies the entity graph (EG), which contain the LOD resources directly related to the users' queries. As a next, it performs predicate-based RDF clustering to find the topic graph patterns (TGPs), which are the EGs to be reconstituted with information on the specific topics. Finally, the Q-PD framework extends the RDF graph patterns related to the users' queries using the TGPs. To prove the excellence of the Q-PD framework, we performed the three kinds of experiment with 681.9 entities and 51,577.1 RDF triples collected from DBpedia. Experimental results show that the Q-PD framework is superior to the existing bottom-up approach in terms of completeness and accuracy.
Social Media Helps Reveal Cause of 2018 Indonesian Tsunami

Videos from Twitter and YouTube helped scientists tease out the physical mechanisms that generated the large tsunami in Palu Bay after a magnitude 7.5 earthquake.
Quantum Error-Correcting Codes

In this chapter we introduce the concept of quantum error-correcting codes and show several examples chronologically. This chapter is fundamental for the development of this work, since the main aim of this book is to present several types of different constructions of quantum block and convolutional codes, as well as constructions of asymmetric quantum codes.
AspeRa: Aspect-Based Rating Prediction Based on User Reviews

We propose a novel Aspect-based Rating Prediction model (AspeRa) that estimates user rating based on review texts for the items. It is based on aspect extraction with neural networks and combines the advantages of deep learning and topic modeling. It is mainly designed for recommendations, but an important secondary goal of AspeRa is to discover coherent aspects of reviews that can be used to explain predictions or for user profiling. We conduct a comprehensive empirical study of AspeRa, showing that it outperforms state-of-the-art models in terms of recommendation quality and produces interpretable aspects. This paper is an abridged version of our work (Nikolenko et al., 2019)
Turbocharging Treewidth-Bounded Bayesian Network Structure Learning

We present a new approach for learning the structure of a treewidth-bounded Bayesian Network (BN). The key to our approach is applying an exact method (based on MaxSAT) locally, to improve the score of a heuristically computed BN. This approach allows us to scale the power of exact methods—so far only applicable to BNs with several dozens of random variables—to large BNs with several thousands of random variables. Our experiments show that our method improves the score of BNs provided by state-of-the-art heuristic methods, often significantly.
The University of Helsinki Submission to the IWSLT2020 Offline SpeechTranslation Task

This paper describes the University of Helsinki Language Technology group's participation in the IWSLT 2020 offline speech translation task, addressing the translation of English audio into German text.In line with this year's task objective, we train both cascade and endto-end systems for spoken language translation.We opt for an end-to-end multitasking architecture with shared internal representations and a cascade approach that follows a standard procedure consisting of ASR, correction, and MT stages.We also describe the experiments that served as a basis for the submitted systems.Our experiments reveal that multitasking training with shared internal representations is not only possible but allows for knowledge-transfer across modalities.
Competitive and Cooperative Heterogeneous Deep Reinforcement Learning

Numerous deep reinforcement learning methods have been proposed, including deterministic, stochastic, and evolutionary-based hybrid methods. However, among these various methodologies, there is no clear winner that consistently outperforms the others in every task in terms of effective exploration, sample efficiency, and stability. In this work, we present a competitive and cooperative heterogeneous deep reinforcement learning framework called C2HRL. C2HRL aims to learn a superior agent that exceeds the capabilities of the individual agent in an agent pool through two agent management mechanisms: one competitive, the other cooperative. The competitive mechanism forces agents to compete for computing resources and to explore and exploit diverse regions of the solution space. To support this strategy, resources are distributed to the most suitable agent for that specific task and random seed setting, which results in better sample efficiency and stability. The other mechanic, cooperation, asks heterogeneous agents to share their exploration experiences so that all agents can learn from a diverse set of policies. The experiences are stored in a two-level replay buffer and the result is an overall more effective exploration strategy. We evaluated C2HRL on a range of continuous control tasks from the benchmark Mujoco. The experimental results demonstrate that C2HRL has better sample efficiency and greater stability than three state-of-the-art DRL baselines.
Focusing on Detail: Deep Hashing Based on Multiple Region Details (Student Abstract)

Fast retrieval efficiency and high performance hashing, which aims to convert multimedia data into a set of short binary codes while preserving the similarity of the original data, has been widely studied in recent years. Majority of the existing deep supervised hashing methods only utilize the semantics of a whole image in learning hash codes, but ignore the local image details, which are important in hash learning. To fully utilize the detailed information, we propose a novel deep multi-region hashing (DMRH), which learns hash codes from local regions, and in which the final hash codes of the image are obtained by fusing the local hash codes corresponding to local regions. In addition, we propose a self-similarity loss term to address the imbalance problem (i.e., the number of dissimilar pairs is significantly more than that of the similar ones) of methods based on pairwise similarity.
Investigation of the Eﬀect of Noise on Tracking Objects using Deep Learning

Nowadays, tracking objects has become one of the basic needs of security systems. Deep learning based methods has dramatically improved results in tracking objects. Meanwhile, the quality of the videos captured by camera is eﬀective on the accuracy of the trackers. All images captured by camera inevitably contain noise. The noise is usually created due to various reasons such as the underlying media, weather condition, and camera vibrations in the wind and so on. This paper deals with the issue. In this paper, tracking objects is performed by Yolu 3 architecture in deep learning. Cycle spinning method is also employed to eliminate noise.
Community-Developed Infrastructure for Curating Research Data: DCN Update

May 2019 poster presented at the Research Data Access and Preservation Summit in Coral Gables, Florida.
The Impact of Marketing Deception on CustomerConfidence in the Brand of the Jordanian TravelAgengies: A Field Study

The study aimed at identifying the level of marketing deception practiced in the field of servicedelivery, service promotion, service pricing, and the physical setting in the Jordanian travel and tourism companiesfrom the point of view of customers. The researcher relied on analytical descriptive approach as it suits the natureof the study. To achieve the objectives of the study and test its hypotheses, a questionnaire was constructedconsisting of five domains. A simple random sample of 350 customers was selected from those who deal withtravel and tourism companies in Amman. The findings of the study showed that there is a statistically significantimpact at (α = 0.05) for marketing deception in its five fields on the consumer confidence in the brand of theJordanian travel and tourism companies in the city of Amman to a large extent. The study has also shown thedimesions of the customers confidence in the brand and marketing deception in the promotion achieved a highdegree of appreciation, while the marketing deception in service provided, pricing, distribution, and the physicalsetting was moderate. The impact of customer confidence on the brand was ranked first while the impact ofmarketing deception on the customers confidence in the brand was ranked last.
The Evaluation Method of English Teaching Efficiency based on Language Recognition Technology

At present, there are some problems in the evaluation methods of English teaching efficiency, such as poor significance of indicators, low evaluation efficiency and low evaluation accuracy. This paper proposes an evaluation method of English teaching efficiency based on language recognition technology. Language recognition technology is used to identify English sentences through error detection, eigenvalue extraction, pronunciation evaluation, phoneme association and phoneme recognition. The overall structure of English teaching efficiency evaluation is constructed by B/S structure, and the weight of evaluation indicator is calculated by AHP combined with hierarchical relevance theory. The degree of relevance is obtained, and the evaluation of English teaching efficiency is completed by ranking the degree of relevance. The experimental results show that the proposed method has high significance, high evaluation efficiency and high evaluation accuracy.
An Update on Data Curation Primers: Collaborative Resources for Data Curators

March 2020 poster presented at the Research Data Access and Preservation Summit in Santa Fe, New Mexico.
Digital contact tracing for COVID-19: a primer for policymakers

Recent modelling studies have shown that timely isolation of contacts can be effective in reducing the COVID-19 reproduction rate. The epidemiological features of COVID-19 put it in the region where contact tracing is a viable public health strategy but it needs to occur at exceptional speed to control transmission. This need for speed has led policymakers to consider adopting contact tracing apps that enable rapid digital contact tracing. The rapidity and perfect scalability of digital contact tracing make it superior to manual contact tracing for an infection such as COVID-19. However, digital contact tracing has to enable notification of contacts the instant a user becomes positive. Ideally, with a rapid follow-up call by public health officials. A number of contact tracing apps currently deployed – such as the Australian COVIDSafe app – do not allow instantaneous notification and are therefore very limited in their ability to achieve control. Digital tracing apps also require sufficient uptake to realise the benefits. This requires building and maintaining strong social licence for use by demonstrating that the benefits to the user are high and that privacy and security risks are low. Like many medical devices and therapies, contact tracing apps are “credence goods”, and claims of efficacy and safety must be largely taken on trust. Therefore Governments ought to subject these apps to robust and transparent evaluation so that users can judge for themselves whether downloading and use are worth the effort. This paper is intended for policymakers and provides an introduction to how digital contact tracing works, including a simplified model of its epidemiological foundations, and explores how to obtain enhanced uptake by achieving social licence for the use of the technology within the context of a public health emergency.
Architecture modelling of parametric component-based systems

Component-based design is a rigorous method for modelling large systems by coordinating multiple components. Communication principles can be described efficiently by architectures that characterize the permissible interactions and define the topology of the systems. In this paper we study formal modelling of architectures applied on parametric component-based systems consisting of an unknown number of instances of each component. Architecture modelling in this work is achieved by means of logics. Specifically, we introduce an extended propositional interaction logic and investigate its first-order level which serves as a formal language for the interactions of parametric systems. Our logic effectively describes the execution order of interactions which is a main feature in several important architectures. We state the decidability of equivalence, satisfiability, and validity of first-order extended interaction logic formulas, and provide several examples of formulas describing well-known architectures.
Goal Formation through Interaction in the Situation Calculus: A Formal Account Grounded in Behavioral Science

Goal reasoning has been attracting much attention in AI recently. Here, we consider how an agent changes its goals as a result of interaction with humans and peers. In particular, we draw upon a model developed in Behavioral Science, the Elementary Pragmatic Model (EPM). We show how the EPM principles can be incorporated into a sophisticated theory of goal change based on the Situation Calculus. The resulting logical theory supports agents with a wide variety of relational styles, including some that we may consider irrational or creative. This lays the foundations for building autonomous agents that interact with humans in a rich and realistic way, as required by advanced Human-AI collaboration applications.