Reviewerly/exHarmony huggingface dataset:
Designing Better Questions for Complex Concepts with Reflective Indicators

There are many concepts in the social sciences that are measured using multiple indicators. Such concepts have been called by Blalock (1968) concepts-by-postulation because one needs a theoretical argument to define them. Within the set of concepts-by-postulation, a distinction has been made between concepts with reflective indicators and concepts with formative indicators. This distinction refers to the assumption whether the latent concepts determine the observed indicators (reflective) or that the indicators together determine the latent concept of interest (formative). Blalock complained that developing measurement procedures for complex concepts, researchers think mainly about questions not about concepts that these questions measure. In this way, the questions used contain unique components which reduce the quality of the composite score based on these questions as measure for the complex concept of interest. Saris and Gallhofer have shown how alternative formulated questions can be developed to measure so-called concepts-by-intuition. In this paper, we will show that the same procedure can be used to avoid unique components in the measurement of complex concepts with reflective indicators and that in this way the quality of the composite score for complex concepts can considerably be increased.
Dependencies of Agile Teams – An Analysis of the Scaled Agile Framework

Abstract Context: Agile teams are small teams with 3 to 9 members. In complex development endeavors such as systems engineering, an agile team has many dependencies, since it is not possible to incorporate all specialist skills into one team. Frameworks like the Scaled Agile Framework (SAFe) describe how agile teams operate in a larger setting. Objective: The aim of this study is to analyze how agile teams collaborate with their organizational environment. Method: We analyzed SAFe to investigate how much guidance it provides concerning the collaboration between agile teams and their environment. Results: The results show that many different organizational parts exist with which agile teams have to collaborate. SAFe mentions concepts like shared services, system teams, or business teams, but there is no further guidance on collaboration with the agile team. Conclusion: We motivate future research into guidelines for efficient collaboration of agile teams with their organizational environment.
GLOBAL-MANAGER

The emergence of Global Software Development (GSD) has led to certain difficulties in the life cycle of global projects, in addition to the traditional challenges of collocated development, particularly as regards Project Management (PM). These difficulties are caused by the geographical, linguistic and cultural distance among the members of the team, signifying that the project manager requires special skills with which to mitigate these issues. Bearing this in mind, this paper describes a serious game (SG), denominated as GLOBAL-MANAGER, whose objective is to provide training in the management of GSD projects. The game attempts to develop several skills in its players whilst simultaneously providing them with an immersive, pleasant and attractive experience. The skills developed are related to coordination, communication and control, which are three of the principal challenges in GSD.
Applied Exponential Smoothing Holt-Winter Method for Predict Rainfall in Mataram City

Weather conditions in the city of Mataram tend to be erratic and difficult to predict, such as the condition of rainfall data in 2018 which changes over a certain period of time so that the weather is difficult to predict accurately. In this study, we propose the Exponential Smoothing Holt-Winter method to forecast rainfall in the city of Mataram, so that it can be a decision support for various interested sectors. This method has been tested using secondary data from the Mataram City Central Bureau of Statistics for the period January 2014 to 2018 and evaluated using Mean Absolute Deviation (MAD), Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE). The results of this study indicate that using the Exponential Smoothing Holt-Winter method yields better results, each of which is MAPE 142.3, MAD 95.6 and MSD value 24988.7 and the data smoothing value is obtained for the smallest combination value of α 0.2, β 0.1, and γ 0.1. It can be concluded that the proposed method can provide better information and can be used to predict rainfall in Mataram City for the next 12 periods.
Practical Imperfections Affecting the Performance of CV-QKD Based on Coherent Detection

Continuous variable-quantum key distribution (CV-QKD) based on coherent detection has potential to be a cost-effective solution to deploy secure keys over optical networks. This is due to its compatibility with mature optical communication technologies, such as homodyne and heterodyne detection schemes. Nevertheless, realistic implementations of CV-QKD systems suffer from imperfections in the practical devices used to generate and detect the coherent states. Those imperfections deteriorate the performance of the CV-QKD system and can be exploited by an eavesdropper to steal key information without being detected. In this work, we discuss the impact of practical device imperfections on the excess noise of the CV-QKD and on the achievable transmission distance. We also analyze the impact of detection imperfections on the measurement of the shot noise, and its impact on CV-QKD system security.
On theoretical aspects of using the concept of digital footprint in forensics

This article examines the theoretical problem of formulating a definition for new type of traced manifested in computer space. The subject of this research is concept of digital footprint. Special attention is paid to different approaches of forensic specialists towards interpretation of trace pattern formed as a result of a crime committed with the use of technical devices, including the Internet. Analysis is conducted on various opinions of forensic specialists dealing with this problem. The author provides and substantiates the original opinion on introduction of the term &amp;ldquo;digital footprint&amp;rdquo;. The conclusion is formulated on the need to accept digital footprint as most appropriate term for describing any type of trace manifested in computer space, including the Internet. The article also presents an original definition of such type of trace intelligible to general audience. The author indicates the trends and prospects in formation and further development of the new branch of forensic technology &amp;ndash; &amp;ldquo;forensic examination of digital footprint". The proposed conclusions may be valuable in scientific and educational activity.
Deep Reinforcement Learning for On-line Dialogue State Tracking

Dialogue state tracking (DST) is a crucial module in dialogue management. It is usually cast as a supervised training problem, which is not convenient for on-line optimization. In this paper, a novel companion teaching based deep reinforcement learning (DRL) framework for on-line DST optimization is proposed. To the best of our knowledge, this is the first effort to optimize the DST module within DRL framework for on-line task-oriented spoken dialogue systems. In addition, dialogue policy can be further jointly updated. Experiments show that on-line DST optimization can effectively improve the dialogue manager performance while keeping the flexibility of using predefined policy. Joint training of both DST and policy can further improve the performance.
The importance of higher education in training of academic personnel

The authors scrutinized a system of factors that clinch the
Pre-training for Abstractive Document Summarization by Reinstating Source Text

Abstractive document summarization is usually modeled as a sequence-to-sequence (Seq2Seq) learning problem. Unfortunately, training large Seq2Seq based summarization models on limited supervised summarization data is challenging. This paper presents three pre-training objectives which allow us to pre-train a Seq2Seq based abstractive summarization model on unlabeled text. The main idea is that, given an input text artificially constructed from a document, a model is pre-trained to reinstate the original document. These objectives include sentence reordering, next sentence generation, and masked document generation, which have close relations with the abstractive document summarization task. Experiments on two benchmark summarization datasets (i.e., CNN/DailyMail and New York Times) show that all three objectives can improve performance upon baselines. Compared to models pre-trained on large-scale data (more than 160GB), our method, with only 19GB text for pre-training, achieves comparable results, which demonstrates its effectiveness.
Local Minima Structures in Gaussian Mixture Models

We investigate the landscape of the negative log-likelihood function of Gaussian Mixture Models (GMMs) with a general number of components in the population limit. As the objective function is non-convex, there can be multiple local minima that are not globally optimal, even for well-separated mixture models. Our study reveals that all local minima share a common structure that partially identifies the cluster centers (i.e., means of the Gaussian components) of the true location mixture. Specifically, each local minimum can be represented as a non-overlapping combination of two types of sub-configurations: fitting a single mean estimate to multiple Gaussian components or fitting multiple estimates to a single true component. These results apply to settings where the true mixture components satisfy a certain separation condition, and are valid even when the number of components is over- or under-specified. We also present a more fine-grained analysis for the setting of one-dimensional GMMs with three components, which provide sharper approximation error bounds with improved dependence on the separation.
Blockchain-Based Data Integrity for Collaborative CAD

Distributed and collaborative computer-aided design (CAD) environments include building information modeling (BIM) and geographical information systems (GISs) in civil engineering and architecture, or product data management/product life cycle management (PDM/PLM) in mechanical engineering. It is essential to keep the data integrity in these computer applications as it contributes to buildingusers’ confidence in CAD/BIM/PDM data. Blockchain technology, the core foundation of cryptocurrencies, is increasingly being used for other purposes and could solve the data integrity issue in collaborative CAD environments. However, it has some disadvantages such as the transparency of data and the slowness of storing data in the blockchain due to distributed consensus. Increasing demand by the Industry 4.0, IoT, Smart Cities, and other initiatives could foster the best what blockchain has to offer: data integrity, reliability, and traceability. This chapter explains how blockchain works, how can it be utilized in distributed CAD environments, what are the major challenges for implementation, and how CAD vendors could use it to increase CAD data integrity.
Ralegh Radford Rome Awards: Restoring ancient text using machine learning: a case-study on Greek and Latin epigraphy

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Unsupervised Pre-training for Biomedical Question Answering

We explore the suitability of unsupervised representation learning methods on biomedical text -- BioBERT, SciBERT, and BioSentVec -- for biomedical question answering. To further improve unsupervised representations for biomedical QA, we introduce a new pre-training task from unlabeled data designed to reason about biomedical entities in the context. Our pre-training method consists of corrupting a given context by randomly replacing some mention of a biomedical entity with a random entity mention and then querying the model with the correct entity mention in order to locate the corrupted part of the context. This de-noising task enables the model to learn good representations from abundant, unlabeled biomedical text that helps QA tasks and minimizes the train-test mismatch between the pre-training task and the downstream QA tasks by requiring the model to predict spans. Our experiments show that pre-training BioBERT on the proposed pre-training task significantly boosts performance and outperforms the previous best model from the 7th BioASQ Task 7b-Phase B challenge.
Towards Adaptive Semantic Segmentation By Progressive Feature Refinement

As one of the fundamental tasks in computer vision, semantic segmentation plays an important role in real world applications. Although numerous deep learning models have made notable progress on several mainstream datasets with the rapid development of convolutional networks, they still encounter various challenges in practical scenarios. Unsupervised adaptive semantic segmentation aims to obtain a robust classifier trained with source domain data, which is able to maintain stable performance when deployed to a target domain with different data distribution. In this paper, we propose an innovative progressive feature refinement framework, along with domain adversarial learning to boost the transferability of segmentation networks. Specifically, we firstly align the multi-stage intermediate feature maps of source and target domain images, and then a domain classifier is adopted to discriminate the segmentation output. As a result, the segmentation models trained with source domain images can be transferred to a target domain without significant performance degradation. Experimental results verify the efficiency of our proposed method compared with state-of-the-art methods.
A Geometry-Inspired Attack for Generating Natural Language Adversarial Examples

Generating adversarial examples for natural language is hard, as natural language consists of discrete symbols, and examples are often of variable lengths. In this paper, we propose a geometry-inspired attack for generating natural language adversarial examples. Our attack generates adversarial examples by iteratively approximating the decision boundary of Deep Neural Networks (DNNs). Experiments on two datasets with two different models show that our attack fools natural language models with high success rates, while only replacing a few words. Human evaluation shows that adversarial examples generated by our attack are hard for humans to recognize. Further experiments show that adversarial training can improve model robustness against our attack.
Voyager: Tracking with a Click

Attribution, the ability to match events on the Internet to actors who caused them, is a difficult problem in Cybersecurity and Digital Forensics. The Internet was not designed to track behavior of users [1]; instead, it is a common open platform that is propitious to anonymity. Different actors take advantage of this anonymity, and if they break the law, it is up to law enforcement to track them, i.e., to attribute actions to actors in way that would stand up in court. In this paper we present a tool, called Voyager, based on the idea of a tracking pixel, which can help investigators with attribution.
Chaotic time series prediction by noisy echo state network

We have applied noisy echo state networks to the short-term forecasting of hyperchaotic and chaotic time series. The hyperchaotic time series were generated using the augmented Lorenz equations as a star network of Q nonidentical Lorenz systems and a four-dimensional Lorenz system. The echo state networks were used mainly in the recursive forecasting mode, wherein the output value of the network, i.e., the predicted value, at the current time step was recursively fed back to the input node at the next time step of prediction. The addition of external noise to the reservoir network has been found to considerably improve the fidelity of the geometrical structures of the chaotic attractors reconstructed from the predicted time series. We discuss these observations on the basis of Ueda's theory of chaos.
Elastic collisions visualization using OpenCV object motion tracking

Robotics is rapidly evolving scientific and engineering interdisciplinary field applied in numerous aspects of everyday life. Computer vision is a domain of robotics which primarily involves scientific knowledge of mathematics, electrical engineering and computing. Practical demonstration of computer vision use in the physical experiment of the elastic collisions is presented in this paper. Open source framework OpenCV on Raspberry Pi 4 platform was used for object motion tracking on specifically constructed elastic collision experimental setup. The experiment was used in course of Physics, Numerical Mathematics and Introduction to Robotics within our institution.
Aspect and Opinion Term Extraction for Hotel Reviews using Transfer Learning and Auxiliary Labels

Aspect and opinion term extraction is a critical step in Aspect-Based Sentiment Analysis (ABSA). Our study focuses on evaluating transfer learning using pre-trained BERT (Devlin et al., 2018) to classify tokens from hotel reviews in bahasa Indonesia. The primary challenge is the language informality of the review texts. By utilizing transfer learning from a multilingual model, we achieved up to 2% difference on token level F1-score compared to the state-of-the-art Bi-LSTM model with fewer training epochs (3 vs. 200 epochs). The fine-tuned model clearly outperforms the Bi-LSTM model on the entity level. Furthermore, we propose a method to include CRF with auxiliary labels as an output layer for the BERT-based models. The CRF addition further improves the F1-score for both token and entity level.
Website Usability Measurement: A Case of Fatih University / Website Usability Measurement: A Case of Fatih University

The usability evaluation of a website has been found using quantitative metrics. A case study has been used to collect quantitative data from participants to determine and measure 3 main factors of website usability; effectiveness, efficiency, and satisfaction. A questionnaire has been delivered to 32 participants who were asked to complete 8 different tasks throughout the website. The results of the study produced an enriched perspective to measure the website usability. We measured the effectiveness, efficient, and satisfaction of the website with quantitative data and explored the details of each factor from user experience perspective.
DIGITALIZATION OF THE POLYCULTURAL ENVIRONMENT OF THE UNIVERSITY IN THE CONDITIONS OF VUCA: MODELLING STRATEGIES

The article presents the results of the scientific discourse analysis of modern studies of digitalization of the university multicultural environment under VUCA conditions, which are presented in three segments: research on a multicultural environment; studies on the digitalization of the educational activities of the university and studies on the digitalization of the multicultural environment of the university. The authors highlighted the features of teaching a foreign language taking into account the challenges of the education system in the era of digital economy and simulated the systems of subprocesses and their meaningful content. Particular emphasis is placed on the specifics of the VUCA conditions (volatility, uncertainty, complexity and ambiguity), which consist of stress, changing nature of the world, in which huge amounts of information must be quickly and adequately processed. This article attempts to develop and describe current digitalization trends in VUCA. The purpose of this study is to find effective strategies for the development of the multicultural environment of the university, one of which is digitalization. To determine the essence of the search for strategic solutions for digitalization of the multicultural environment of the university, theoretical, empirical and diagnostic methods were implemented: review of the scientific literature, analysis of innovative strategic practices, observation, statistical methods. As a result, modeling of digitalization strategies of the university’s multicultural environment under VUCA conditions is considered as the development and creation of a formal model of strategic decisions aimed at implementing the digitalization process of the university’s multicultural environment. The authors analyzed the programs of partner universities and organized international webinars and internships for bachelors and masters abroad. The experimental data shows the effectiveness of the formation of multicultural competences among students.
A Study for Universal Adversarial Attacks on Texture Recognition

Given the outstanding progress that convolutional neural networks (CNNs) have made on natural image classification and object recognition problems, it is shown that deep learning methods can achieve very good recognition performance on many texture datasets. However, while CNNs for natural image classification/object recognition tasks have been revealed to be highly vulnerable to various types of adversarial attack methods, the robustness of deep learning methods for texture recognition is yet to be examined. In our paper, we show that there exist small image-agnostic/univesal perturbations that can fool the deep learning models with more than 80\% of testing fooling rates on all tested texture datasets. The computed perturbations using various attack methods on the tested datasets are generally quasi-imperceptible, containing structured patterns with low, middle and high frequency components.
Learning Knowledge Bases with Parameters for Task-Oriented Dialogue Systems

Task-oriented dialogue systems are either modularized with separate dialogue state tracking (DST) and management steps or end-to-end trainable. In either case, the knowledge base (KB) plays an essential role in fulfilling user requests. Modularized systems rely on DST to interact with the KB, which is expensive in terms of annotation and inference time. End-to-end systems use the KB directly as input, but they cannot scale when the KB is larger than a few hundred entries. In this paper, we propose a method to embed the KB, of any size, directly into the model parameters. The resulting model does not require any DST or template responses, nor the KB as input, and it can dynamically update its KB via fine-tuning. We evaluate our solution in five task-oriented dialogue datasets with small, medium, and large KB size. Our experiments show that end-to-end models can effectively embed knowledge bases in their parameters and achieve competitive performance in all evaluated datasets.
$f$-GAIL: Learning $f$-Divergence for Generative Adversarial Imitation Learning

Imitation learning (IL) aims to learn a policy from expert demonstrations that minimizes the discrepancy between the learner and expert behaviors. Various imitation learning algorithms have been proposed with different pre-determined divergences to quantify the discrepancy. This naturally gives rise to the following question: Given a set of expert demonstrations, which divergence can recover the expert policy more accurately with higher data efficiency? In this work, we propose $f$-GAIL, a new generative adversarial imitation learning (GAIL) model, that automatically learns a discrepancy measure from the $f$-divergence family as well as a policy capable of producing expert-like behaviors. Compared with IL baselines with various predefined divergence measures, $f$-GAIL learns better policies with higher data efficiency in six physics-based control tasks.