Miracle-dz/UniFAR-FTUs huggingface dataset:
The acceleration of evolutionary computations using fitness estimation

Evolutionary computation (EC) is widely applied to various kinds of combinatorial optimization problems. ECs are generally time-consuming because they need much trial and error. To accelerate ECs, some modification methods of the genetic operator have been proposed, such as improving mutation and recombination of chromosomes and/or their control parameters and so on. Through these modifications, ECs can find the suboptimal solutions in the relatively early generations. In spite of these improvements, ECs still require much time to obtain the solution. In many engineering applications of ECs, fitness evaluation spent the most computational time. This paper presents a new approach for the acceleration of ECs by reducing the time for fitness evaluation. Saving the time for fitness evaluation results in accelerating the ECs in the time domain. In the proposed method, only one individual of the population is actually evaluated in each generation. Fitness values for the rest of the population are estimated with simple calculation. Although the errors of estimation may decelerate the ECs in the generation domain, saving time in the evaluation scheme will exceed the deceleration. As a result, we can obtain a suboptimal solution relatively faster. The simulation results of designing the fuzzy logic controller using GA shows the effectiveness of the proposed method to accelerate the evolution in the time domain using estimated evaluation.
Three-phase training to address data sparsity in Neural Machine Translation

Data sparsity is a key problem in contemporary neural machine translation (NMT) techniques, especially for resource-scarce language pairs. NMT models when coupled with large, high quality parallel corpora provide promising results and are an emerging alternative to phrase-based Statistical Machine Translation (SMT) systems. A solution to overcome data sparsity can facilitate leveraging of NMT models across language pairs, thereby providing high quality translations despite the lack of large parallel corpora. In this paper, we demonstrate a three-phase integrated approach which combines weakly supervised and semi-supervised learning with NMT techniques to build a robust model using a limited amount of parallel data. We conduct experiments for five language pairs (thereby generating ten systems) and our results show a substantial increase in translation quality over a baseline NMT model trained only on parallel data.
A Deep Dive into Identification of Characters from Mahabharata

The present paper describes the identi-ﬁcation of story Characters from Indian Mythological text "The Mahabharata". It is observed that these Characters can be found at word level and phrase level in a sentence with some distinct patterns. In order to ﬁnd the Characters from the text, two sets of features are considered at both levels. Using a semi-supervised learning approach we have prepared the training data sets. Later on, we have employed Chi-squared statistic to ﬁnd the important features, which is followed by the associa-tivity analysis of those selected features. After that, we developed training models using NeuralNet and KNN classiﬁers for both word and phrase levels and tested the models. Our observation shows that NeuralNet performs better than KNN with 88% and 76% accuracy at word and phrase level respectively.Next, we have analyzed different error measures followed by visualization of co-occurred story Characters of most frequent Characters.
Metamorphic Relation Patterns for Query-Based Systems

Searching and displaying data based on user queries is a key feature of most software applications such as information systems, web portals, web APIs, and data analytic platforms. The large volume of data managed by these types of systems, henceforth called query-based systems (QBS), makes them extremely hard to test due to the difficulty to assess whether the output of a query is correct, the so-called oracle problem. Metamorphic testing has proved to be a very effective approach to alleviate the oracle problem in QBS, enabling the detection of bugs in data repositories, large e-commerce sites, and some of the most used software applications worldwide such as Google Search and YouTube. We have observed, however, that the metamorphic relations used to test different types of QBS are very similar, regardless of their domain, since all of them exploit standard query features such as filtering and ordering. Inspired by this finding, in this paper we present a catalogue of metamorphic relation patterns to assist testers in the identification and inference of metamorphic relations in QBS. For the definition of the patterns we resorted to the root of most query languages: relational algebra. We show how the proposed patterns help in the identification of metamorphic relations in the e-commerce platform PrestaShop, the email service Gmail, and the mobile application of video streaming HBO.
On Learning Word Embeddings From Linguistically Augmented Text Corpora

Word embedding is a technique in Natural Language Processing (NLP) to map words into vector space representations. Since it has boosted the performance of many NLP downstream tasks, the task of learning word embeddings has been addressing significantly. Nevertheless, most of the underlying word embedding methods such as word2vec and GloVe fail to produce high-quality embeddings if the text corpus is small and sparse. This paper proposes a method to generate effective word embeddings from limited data. Through experiments, we show that our proposed model outperforms existing works for the classical word similarity task and for a domain-specific application.
Gender Differences in Self and Peer Assessment in a Software Engineering Capstone Course

Context: Women are generally underrepresented in software development and probably their behavior is biased by the fact that they are usually a minority within teams. The Engineering School at the Universidad de Chile has put in practice a strong women recruitment program. This brought that, for the first time, women reached 20% of the students enrolled in the fifth year software engineering capstone course. Problem: More women are entering the work force but there is still certain prejudice about women performance in STEM in general, and in software development in particular, since it is perceived as a man’s activity. Method: In the context of the fifth year capstone course at the CS Department of the Universidad de Chile we conducted a field study in order to analyze the progression of self and peer assessment of men and women students along one semester. Results: We found that, even though peer assessment is similar for women and men, self assessment tends to be lower for women. Also, peer assessment does not vary much along the semester, neither for men nor for women. Conclusions: Women performance in software development teams is highly regarded by teammates. However, women do not seem to be willing to acknowledge their own performance. More research is needed in order to understands the causes of this behavior.
Adopting Conversational Interfaces for Exploring OSGi-Based Software Architectures in Augmented Reality

We propose conversational interfaces as a convenient and complementary way for users to explore OSGi-based software architectures in immersive Augmented Reality (AR). By providing a conversational interface we aim to remedy some peculiarities of AR devices, but also enhancing the exploration task at hand. We exemplify a use case and sketch how different user utterances can be used to retrieve information about the to-be-explored OSGi-based software architecture. We identify crucial components such as natural language generation and intent recognition which are required to implement the user story and we outline the status of our implementation.
Information Theoretic Evaluation of Logic Styles to Counteract Side-Channel Attacks

We propose to apply an information theoretic metric to the evaluation of side-channel resistant logic styles. Due to the long design and development time required for the physical evaluation of such hardware countermeasures, our analysis is based on simulations. Although they do not aim to replace the need of actual measurements, we show that simulations can be used as a meaningful first step in the validation chain of a cryptographic product. For illustration purposes, we apply our methodology to gate-level simulations of different logic styles and stress that it allows a significant improvement of the previously considered evaluation methods. In particular, our results allow putting forward the respective strengths and weaknesses of actual countermeasures and determining to which extent they can practically lead to secure implementations (with respect to a noise parameter), if adversaries were provided with simulation-based side-channel traces. Most importantly, the proposed methodology can be straightforwardly adapted to adversaries provided with any other kind of leakage traces (including physical ones).
Correcting Whitespace Errors in Digitized Historical Texts

Whitespace errors are common to digitized archives. This paper describes a lightweight unsupervised technique for recovering the original whitespace. Our approach is based on count statistics from Google n-grams, which are converted into a likelihood ratio test computed from interpolated trigram and bigram probabilities. To evaluate this approach, we annotate a small corpus of whitespace errors in a digitized corpus of newspapers from the 19th century United States. Our technique identifies and corrects most whitespace errors while introducing a minimal amount of oversegmentation: it achieves 77% recall at a false positive rate of less than 1%, and 91% recall at a false positive rate of less than 3%.
Correcting General Purpose ASR Errors using Posteriors

Speech based interfaces have gained popularity because of the advances in automatic speech recognition (ASR) technology, more recently, triggered by the use of deep neural networks for acoustic and language modeling. However, the performance of any general purpose ASR (gpASR) is poor especially for low resource languages and the performance deteriorate further for natural conversational speech. In this paper, we propose a statistical approach to learn the corrections to enable the use of a general purpose ASR for domain speciﬁc use. The proposed idea is based on the observation that there are three types of errors that occur in an ASR output, namely, insertion, deletion or sub-stitution of a word or a phoneme. We pro-pose to model these errors statistically and use these posteriors to develop a scheme that is able to repair the ASR output. The proposed system is able to handle ASR errors spread across lexical words also.
Semantic Enrichment Across Language: A Case Study of Czech Bibliographic Databases

This paper deals with semantic enrichment of textual resources by means of automatically generated named entity recognizers-linkers and advanced indexing and searching mechanisms that can be integrated into various information retrieval and information extraction systems. It introduces a new system transforming Wikipedia and other available sources into task-speciﬁc knowledge bases and employs contextual information to build state-of-the-art entity disambiguation components. Although some components are language-dependent (for example, that responsible for the morphology analysis or the semantic role identiﬁcation), they can be easily replaced by existing tools providing speciﬁc functions. As a case study, we demonstrate an instantiation of the sys-tem for the task of semantic annotation of Czech bibliographic databases in the context of the CPK project. We particularly stress the role of problem-speciﬁc knowledge sources that can be easily integrated into our system and play a key role in the success of the tool in real applications.
Retrieving Similar Lyrics for Music Recommendation System

Presently, millions of music tracks are available on the web. Therefore, a music recommendation system can be helpful to ﬁlter and organize music tracks according to the need of users. To develop a recommendation system, we need an enormous amount of data along with the user preference information. However, there is a scarcity of such dataset for Western as well as Hindi songs. This paper presents a similar lyrics retrieval system for Hindi songs using features collected from lyrics. A romanized Hindi lyric dataset is collected from the web. The collected data-set is noisy, and several forms of a single word are present in it, thus an unsupervised stemming algorithm is proposed to reduce the size of N-grams. The Self-Organizing Feature Maps (SOFMs) based similar lyrics retrieval system achieves the maximum F-measure of 0.749.
Development of Amharic Grammar Checker Using Morphological Features of Words and N-Gram Based Probabilistic Methods

Amharic is one of the most morphologically complex and under-resourced languages which effectively hinder the development of efficient natural language processing applications. Am-haric words, especially verbs, are marked for a combination of several grammatical functions which makes grammar checking complex. This paper describes the design and development of statistical grammar checker for Amharic by treating its morphological features. In a given Amharic sentence, the morphologies of individual words making up the sentence are analyzed and then n-gram based probabilistic methods are used to check grammatical errors in the sentence. The system is tested with a test corpus and experimental results are reported.
Changing the paradigm: Simulation, now a method of first resort

Decades ago, simulation was famously characterized as a “method of last resort,” to which analysts should turn only “when all else fails. ” In those intervening decades, the technologies supporting simulation—computing hardware, simulation‐modeling paradigms, simulation software, design‐and‐analysis methods—have all advanced dramatically. We offer an updated view that simulation is now a very appealing option for modeling and analysis. When applied properly, simulation can provide fully as much insight, with as much precision as desired, as can exact analytical methods that are based on more restrictive assumptions. The fundamental advantage of simulation is that it can tolerate far less restrictive modeling assumptions, leading to an underlying model that is more reflective of reality and thus more valid, leading to better decisions. Published 2015 Wiley Periodicals, Inc. Naval Research Logistics 62: 293–303, 2015
Language-Agnostic Model for Aspect-Based Sentiment Analysis

In this paper, we propose a language-agnostic deep neural network architecture for aspect-based sentiment analysis. The proposed approach is based on Bidirectional Long Short-Term Memory (Bi-LSTM) network, which is further assisted with extra hand-crafted features. We define three different architectures for the successful combination of word embeddings and hand-crafted features. We evaluate the proposed approach for six languages (i.e. English, Spanish, French, Dutch, German and Hindi) and two problems (i.e. aspect term extraction and aspect sentiment classification). Experiments show that the proposed model attains state-of-the-art performance in most of the settings.
Examining the Technical Adequacy of Second-Grade Reading Comprehension Measures in a Progress Monitoring Assessment System. Technical Report # 08-08.

This technical report describes the development of reading comprehension assessments designed for use as progress monitoring measures appropriate for 2 grade students. Reading passages were created by elementary and secondary teachers, as well as graduate students. Corresponding items were written by an assessment design expert, and grouped according to cognitive attribute and degree of difficulty. Twenty alternate forms were piloted using a common person/ common item design, in which groups of students took five forms, with overlapping forms across groups. Content and bias reviews were conducted. Additionally, a Rasch analysis was used to determine the approximate difficulty of each item, based on the estimated ability level of each test taker. Items that did not appear to be functioning appropriately were retained and will be examined in a future study with a larger sample size. Grade 2 Comprehension – Page
Novice-Friendly Multi-Armed Robotics Programming

Collaborative robots are being applied in a growing number of usage scenarios, but their adoption is slowed down by the high complexity of robot programming. As previous prototype studies have shown, block-based programming environments can enable novice or end users to program industrial single-armed robots. Some existing block-based tools support parallel programming and therefore show potential to be used for multi-armed robot programming as well. We analyze their designs and argue how improved abstractions and visualizations could make multi-armed parallelism accessible to novice users. Based on this analysis, we then extract a list of features that a block-based environment designed for multi-armed robot programming should provide. Finally, we present our design vision for a novel programming environment for two-armed robots, show how it provides these features and discuss how it can enable both novices and experienced intermediate users to perform parallelized programming tasks.
Linelists for NH3, NH2D, ND2H, and ND3 with quadrupole coupling hyperfine components

Linelists for the ammonia molecule and its three deuterated variants NH 2 D, ND 2 H, and ND 3 are built taking hyperfine quadrupole coupling effects into account. For each hyperfine component, the line frequency, the line intensity, and the lower level energy are calculated.
SPECmarks are leading us astray

It is suggested that success at designing systems to satisfy latency-sensitive requirements has not yet been achieved, and that it is necessary to develop benchmarks that explicitly measure system latency, rather than measuring it indirectly through its effect on throughput. If both throughput-sensitive and latency-sensitive benchmarks are required, the result might be the creation of systems that are easily tuned (or even self-tuned) to provide either good latency or good throughput, as the case demands. <<ETX>>
Stylometric Classification of Ancient Greek Literary Texts by Genre

Classification of texts by genre is an important application of natural language processing to literary corpora but remains understudied for premodern and non-English traditions. We develop a stylometric feature set for ancient Greek that enables identification of texts as prose or verse. The set contains over 20 primarily syntactic features, which are calculated according to custom, language-specific heuristics. Using these features, we classify almost all surviving classical Greek literature as prose or verse with >97% accuracy and F1 score, and further classify a selection of the verse texts into the traditional genres of epic and drama.
ModelKB: Towards Automated Management of the Modeling Lifecycle in Deep Learning

Deep Learning has improved the state-of-the-art results in an ever-growing number of domains. This success heavily relies on the development and training of deep learning models, also known as deep neural networks (DNN). Often, developing a DNN is an ad-hoc, iterative process that results in producing tens to hundreds of models before arriving at a satisfactory result. While there has been a surge in the number of tools and frameworks that aim at facilitating deep learning, the issues of model management have been largely ignored. In particular, deep learning practitioners have to manually track their experiments using text files, spreadsheets or folder hierarchies, which is expensive, time-consuming, and error-prone. In this paper, we present our ongoing work and vision towards automating end-to-end model management in deep learning. Specifically, we introduce a tool prototype, named ModelKB, that can automatically (1) extract and store the model's metadata-including its architecture, weights, and configuration; (2) visualize, query, and compare experiments; and (3) reproduce experiments. Our overarching goal is to automate the model management process with minimal user intervention using the user's favorite framework. We report the current status of ModelKB, a pilot user study, and the challenges of automating model management in deep learning.
Design of multimodal dialogue-based systems

Multimodal dialogue systems integrate advanced (often spoken) language technologies within human-computer interaction methods. Such complex systems cannot be designed without extensive human expertise and systematic design guidelines taking into account the limitations of the underlying technologies. Therefore, this thesis aims at reducing the time and effort needed to build such systems by creating application-independent techniques, tools and algorithms that automate the design process and make it accessible for non-expert application developers. The thesis proposes an interactive system prototyping methodology, which (together with its software implementation) allows for rapid building of multimodal dialogue-based information seeking systems. When designed with our methodology, even partially implemented system prototypes can immediately be tested with users through Wizard of Oz simulations (which are integrated into the methodology) that reveal user behavior and modality use models. Involving users in early development phases increases the chances for the targeted system to be well accepted by end-users. With respect to dialogue system design, we propose a two-layered dialogue model as a variant of the standard frame-based approach. The two layers of the proposed dialogue model correspond to local and global dialogue strategies. One of the important findings of our research is that the two-layered dialogue model is easily extendable to multimodal systems. The methodology is illustrated in full detail through the design and implementation of the Archivus system – a multimodal (mouse, pen, touchscreen, keyboard and voice) interface that allows users to access and search a database of recorded and annotated meetings (the Smart Meeting Room application). The final part of the thesis is dedicated to an overall qualitative evaluation of the Archivus system (user's performance, satisfaction, analysis of encountered problems) and to a quantitative evaluation of all the implemented dialogue strategies. Our methodology is intended (1) for designers of multimodal systems who want to quickly develop a multimodal system in their application domain, (2) for researchers who want to better understand human-machine multimodal interaction through experimenting with working prototypes, (3) for researchers who want to test new modalities within the context of a complete application, and (4) for researchers interested in new approaches to specific issues related to multimodal systems (e.g. the multimodal fusion problem).