Reviewerly/exHarmony huggingface dataset:
Lexical and Grammatical Cohesions in the Students’ Essay Writing as the English Productive skills

Abstract For the purpose of achieving a good essay writing, the students need to have capability to use the lexical and grammatical cohesions. This research aimed to describe the lexical and grammatical cohesions used by the students in writing essays at Makassar Muhammadiyah University. The research samples consisted of 91 students’ esssays from the English Education Department of FKIP Unismuh Makassar. The research used a writing test as an instrument to collect data. To achieve the aim of the research, a mixed method was used to describe the results of data analysis quatitatively and qualitatively. The results of the research revealed the use of lexical cohesion which included the use of repetition, synonym, hyponym, antonym, meronym, general noun, and collocation. From the seven kinds of lexical cohesion used by the students in writing essays, the general noun was mostly used by the students. Futhermore, grammatical cohesion revealed the use of reference, substitution, ellipsis, and conjunction. Among them, the reference as a cohesion mark was mostly used by the students. The implication of the research results might be used significantly as the base for developing learning materials in teaching writing as one of the English productive skills.
Motion Planning and Experimental Validation for an Autonomous Bicycle

Trajectory planning for an autonomous bicycle is investigated with regard to different vehicle models. To this end, a nonlinear optimization problem is set up using three models which differ with respect to the geometric details and, therefor, the included physical effects. First, using the method of Direct Collocation the optimization problem is transcribed with the vehicle models as equality constraints. The resulting trajectories are applied to the most detailed vehicle model using a 2-degree-of-freedom loop structure for validation and comparison. Further, a prototype autonomous two-wheeled vehicle is introduced which was developed for experimental verification of motion planning and control algorithms. Finally, experiments are presented and discussed that are run on the real vehicle for a particular maneuver. Thereby, the differences between the trajectories created by different vehicle models are emphasized.
Impact of boosting saturation on automatic human detection in imagery acquired by unmanned aerial vehicles

We aim to investigate a potential impact of boosting saturation of aerial imagery on the performance of unsupervised human detection algorithms. The study is empirical since it is based on processing photographs taken during a full year experiment in the Izerskie Mountains (southwestern Poland) by a consumer-grade Canon S110 camera mounted onboard eBee, a fixed-wing micro-unmanned aerial vehicle (UAV). In the preliminary analysis, we used a few basic color adjustments (sharpening, hue–saturation–luminance, contrast, saturation, and vibrance) to process UAV-taken photographs prior to the automated human detection with the nested <italic>k</italic>-means algorithm. We found that saturation boost is an image preprocessing method that may potentially improve the performance of human detection. In the actual analysis, we investigate only the saturation effect by employing four saturation modification schemes (two versions of enhancements of unusual colors, additive boost of saturation, and multiplicative boost of saturation) and three human detection algorithms [three-dimensional (3-D) nested <italic>k</italic>-means on RGB, two-dimensional nested <italic>k</italic>-means on hue–saturation–value, morphological operations of erosion, and dilation with thresholding]. All the studied saturation boost techniques increase detection rates of the 3-D nested <italic>k</italic>-means on RGB, with particularly meaningful improvement for images acquired in the spring. Morphological operations of erosion and dilation are not found to be skillful in detecting persons. However, their performance is improved after the initial preprocessing by the original or modified enhancement of unusual colors.
Build your own SQL-on-Hadoop Query Engine

This is a report on a course taught at OTH Regensburg in the summer term of 2018. The students in this course built their own SQL-on-Hadoop engine as a term project in just 8 weeks. miniHive is written in Python and compiles SQL queries into MapReduce workflows. These are then executed on Hadoop. miniHive performs generic query optimizations (selection and projection pushdown, or cost-based join reordering), as well as Map- Reduce-specific optimizations. The course was taught in English, using a flipped classroom model. The course material was mainly compiled from third-party teaching videos. This report describes the course setup, the miniHive milestones, and gives a short review of the most successful student projects.
Literary and Colloquial Dialect Identification for Tamil using Acoustic Features

The evolution and diversity of a language is evident from it's various dialects. If the various dialects are not addressed in technological advancements like automatic speech recognition and speech synthesis, there is a chance that these dialects may disappear. Speech technology plays a role in preserving various dialects of a language from going extinct. In order to build a full fledged automatic speech recognition system that addresses various dialects, an Automatic Dialect Identification (ADI) system acting as the front end is required. This is similar to how language identification systems act as front ends to automatic speech recognition systems that handle multiple languages. The current work proposes a way to identify two popular and broadly classified Tamil dialects, namely literary and colloquial Tamil. Acoustical characteristics rather than phonetics and phonotactics are used, alleviating the requirement of language-dependant linguistic tools. Hence one major advantage of the proposed method is that it does not require an annotated corpus, hence it can be easily adapted to other languages. Gaussian Mixture Models (GMM) using Mel Frequency Cepstral Coefficient (MFCC) features are used to perform the classification task. The experiments yielded an error rate of 12%. Vowel nasalization, as being the reason for this good performance, is discussed. The number of mixture models for the GMM is varied and the performance is analysed.
Design and Development of Question Answering System in Bangla Language from Multiple Documents

The paper has presented about the experiment of design and development of automatic question answering system for Bangla language. The purpose of proposed answering system is to provide answer based on the keyword, lexical and semantic feature of a question. User gives a question for which answer has to be found from multiple documents. For measurement (time and quantity) related question the system gives specific answer otherwise the system retrieves relevant answers.
Wavelet transform and cyclic cumulant based modulation classification in wireless network

With the development of Internet of things, a large number of embedded devices are interconnected by ad hoc and wireless network. The embedded devices can work correctly, only by ensuring correct communication between them. Identifying modulation scheme is the precondition to ensure the correct communication between embedded devices. However, in the multipath channel, ensuring the correct communication between embedded devices is a great challenge. Multipath channel always exists in the wireless network. However, most of the available modulation classification algorithms are based on ideal channel. It leads to the low-modulation classification probability in multipath channel. To resolve this problem, we propose a novel modulation classification algorithm. The proposed algorithm can classify signal without prior information about multipath channel. We calculate feature by high-order cyclic cumulant and wavelet transform. The feature is robust to multipath channel. The simulation results show that the proposed algorithm can achieve the much better classification accuracy than the available method in multipath channel.
Good Governance Principles as a Requirement to Achieve Sustainable Development: A Comparative Study Between Yemen and India

Good governance can be presented as the perfect base to achieve the sustainable development strategy. In turn, decentralisation principle is considered as the main mechanism that works as the base for the good governance, in addition to the accountability, predictability, and transparency. This article is a part of a field study aimed to explore and investigate the reality of the sustainable development management in Yemen and India. The ‘Good Governance’ is an important element. The aim of this study is to investigate and explore the extent and the level of adopting the Good Governance principles, as a requirement for achieving sustainable development, in the managerial activities in both countries Yemen and India. The result of an analysis by the chi-square (χ2) test indicated that there is a weakness of applying the decentralisation principle in Yemen. The study ascertains the weakness of the accountability principle, the deficit of the predictability principle, and the weakness of the transparency principle among the managerial practices in Yemen and the lack of analysis in the governmental departments, suggesting the needs to enhance the related process. Although the findings showed that the situation in India is much better compared to Yemen, compared to the developed countries we can say that India also needs more enhancement in this regard in order to facilitate the achievement of the sustainable development for societies in both urban and rural areas.
Local matrix approximation via automatic anchor selection and asymmetric neighbor inclusion based on feature divergence measure

Due to the ever-increasing users and items, the global matrix factorization (MF) over a large yet sparse user-item rating matrix no longer suffices for accurate rating approximations. This paper studies the problem of how to obtain many local matrices for improving the global rating approximation via averaging local MF results. In our scheme, we propose to use a feature divergence measure to compute the asymmetric distances in between users and items. To obtain local matrix approximation, we first propose to use a density-based clustering algorithm to automatically select a number of user-item pairs as anchors for self-adapting different datasets. Next for each anchor, we recruit its neighboring users and items with small feature divergences to the anchor, thus forming each local matrix. A kernel-based approach is then applied for local matrix approximation and a weighted averaging is used to obtain the global matrix rating approximations. Our experiments on the three real-world datasets show that the proposed solution can outperform the state-of-the-art schemes in terms of lower approximation errors and higher coverage ratios.
Solving the next release problem by means of the fuzzy logic inference system with respect to the competitive market

A number of software programms are developed in several releases. Before developing any new release, a set of requirements is suggested for inclusion in the release. Having multiple constraints, it is impossible to develop all the requirements proposed in the next release. The presence of competing companies, replication of product ideas, shortening of the development time and lack of project funding will reduce the cost of developing a release. Developer teams should select a subset of the proposed requirements for development that would provide their clients with the highest amount of satisfaction despite the deadline limitations or cost constraints. The existence of conflicting goals and other constraints makes this choice very complicated. In this paper, an algorithm is introduced which is based on a fuzzy inference system to determine the suitability of each requirement for development in the next release. The proposed algorithm, rather than the developer team, takes the responsibility to select the optimal subset of requirements for the development of the next release. Experimental results of the proposed algorithm are then compared with the results of the genetic algorithm. The subset selected by the proposed algorithm provides much more satisfaction than the genetic algorithm.
Multi-perspective Denoising Reader for Multi-paragraph Reading Comprehension

Multi-paragraph reading comprehension (MPRC) aims to answer questions based on a number of paragraphs. The background paragraphs in MPRC are usually collected by information retrieval (IR) systems. Most of the MPRC models indiscriminately read all the background paragraphs, and regard paragraphs which contain the answer string as ground truths. Among these paragraphs, some are not semantically related to the question, therefore, the noisy data will distract the model from finding the right answer. Besides, most of the MPRC methods only use the paragraph-question relevance to predict the answer span. However, the utilization of the paragraph-paragraph relevance that provides enhanced and complementary evidence has only been explored in limited methods. To address these issues, we propose a hierarchical model with a multi-level attention mechanism, which can leverage both the inter-relation (paragraph-question relevance) and the intra-relation (paragraph-paragraph relevance), to filter out noisy data and extract the final answer. We conduct experiments on two challenging public datasets Quasar-T and SearchQA. The results demonstrate that our model outperforms recent MPRC baselines.
Modeling and Simulation of QoS-Aware Power Budgeting in Cloud Data Centers

Power budgeting is a commonly employed solution to reduce the negative consequences of high power consumption of large scale data centers. While various power budgeting techniques and algorithms have been proposed at different levels of data center infrastructures to optimize the power allocation to servers and hosted applications, testing them has been challenging with no available simulation platform that enables such testing for different scenarios and configurations. To facilitate evaluation and comparison of such techniques and algorithms, we introduce a simulation model for Quality-of-Service aware power budgeting and its implementation in CloudSim. We validate the proposed simulation model against a deployment on a real testbed, showcase simulator capabilities, and evaluate its scalability.
Trabecular Bone Texture Characterization Using Regularization Dimension and Box-counting Dimension

This paper presents texture characterization techniques for effective diagnosis of osteoporosis cases on bone radiograph images. The automatic classification of osteoporosis and healthy (control) cases with bone radiograph images presents a major challenge as the images show little or no visual difference for both cases. The proposed work uses multifractal analysis to characterize the texture of bone radiographs. A regularization dimension method is newly introduced in this work for texture analysis of bone radiographs. Series of local fractal dimensions are used here instead of single global fractal dimension to characterize local texture variations effectively. The performance of the regularization dimension method is compared to the performance of the box-counting method. Then, regularization dimension and box-counting dimension features are combined to improve the performance. The proposed method is evaluated using texture characterization of bone radiograph (TCB) challenge dataset and compared against other methods participated in the challenge to demonstrate the effectiveness of the proposed method
A neural graph embedding approach for selecting review sentences

Product reviews written by the crowd on e-commerce shopping websites have become a critical information source for making purchasing decisions. An important challenge, however, is that the vast majority of products (e.g., 90% of products on amazon.com) do not receive enough attention and lack sufficient reviews by the users; hence, they constitute the so-called cold products. One solution to address cold products, which has already been studied in the literature, is to generate reviews for these products by sampling review sentences from closely related warm products. Our method proposed in this paper is specifically focused on such a solution. While a majority of the works in the literature rely on product specification similarity to identify relevant reviews that can be used for review sentence selection, our work differs in that it not only employs product specification similarity but also employs product-review, product-user, and user-review interactions when determining the suitability of a review sentence to be selected. More specifically, the contributions of our work can be enumerated as follows: (1) We propose that the selection of review sentences from other products should not only consider product-product similarity but also consider product-review, user-review, and user-user relationships. As such, we show how neural graph embeddings can be used to encode product, user, and review information into an attributed heterogeneous graph representation based on which similarities can be calculated. (2) We further propose how review relevance and importance can be considered using graph traversal to select appropriate review sentences for a given cold product. (3) Finally, we systematically compare the performance of our work with those of several state-of-the-art baselines on five datasets collected from CNET.com and rottentomatoes.com with different characteristics from both quantitative (e.g., the Recall-Oriented Understudy for Gisting Evaluation (ROUGE) metrics) and qualitative aspects and show how our proposed approach was able to provide statistically significantly improved performance over various strong baselines.
Heavy metal content in annually-banded coral Porites lutea at windward and leeward of Tunda Island, Banten bay, West Java Indonesia

Abstract Longterm fluctuation content of heavy metal concentrations (Sr, Cr, Fe, Zn, and Pb) was traced through the analysis of coral skeletal growth bands of massive coral core Porites lutea. Annual growth band was examined using X radiography. Heavy metal samples were extracted from each yearly layer of the core section. Seven samples of the coral skeleton were analyzed using neutron activation analysis and atomic absorption spectrometry to detect metal concentrations. The oldest layer of coring corals obtained from the windward side started from 1940, while in leeward of the island starting from 1969. All five metals from both sides were still detected until 2014. There was significant increase over time of the four metals (Cr, Fe, Zn. and Pb), while the concentrations of strontium remained relatively stable.
H-Matrix: Hierarchical Matrix for Visual Analysis of Cross-Linguistic Features in Large Learner Corpora

This paper presents a visualization technique for cross-linguistic error analysis in large learner corpora. H-Matrix combines a matrix, which is commonly used by linguists to investigate cross-linguistic patterns, with a tree diagram to aggregate and interactively re-weight the importance of matrix rows to create custom investigative views. Our technique can help experts to perform data operations, such as, feature aggregation, filtering, ordering and language comparison interactively without having to reprocess the data. H-Matrix dynamically links the high-level multi-language overview to the extracted textual examples, and a reading view where linguists can see the detected features in context, confirm and generate hypotheses.
Reliable and Accurate Pattern Search by Combination of Absent Color Indexing with Correlation Filter

Color-based image processing has been tried as a mean near to human vision, so they have been utilized to realize a likeness to human eyes, for instance in flexible extraction of prominent features. In this paper, a novel approach to image registration called Absent color indexing (ABC) is proposed to address the problem of robust pattern search. Color histogram-based methods have shown favorable performance in the conditions of object rotation, deformation, and occlusion. Most of them have had good performances, but their weakness to fail in distinguishing similar but different objects in the scene. The proposed approach works well in these situations by using absent colors that have relatively low-frequencies or nonexisting colors in color histogram bins. In order to obtain a more accurate positioning, we extended our approach to combine with a correlation filter. Experimental results on Mondrian random patterns and the real-world images show that the proposed ABC has rather high performance for distinguishing similar objects.
Integrated Usage between Relational DBs and NoSQL DB

This paper proposes the system realizing integrated use of Relational Databases and a NoSQL Database. A heterogeneous information source integration system has been proposed. It enables equi-join and projection of tables without converting data. With this system, it became possible to handle various databases at the same time by using JDBC, but the databases which can be handled are only relational databases. Therefore, we add functions to the system to handle the NoSQL database which has been increasingly used in recent years. The table join execution time and the memory consumption of the system are experimentally evaluated. It showed that the performance is tolerable in practical use as compared with integrated use of only relational database. We also showed that it is practical to change the join order or to register column families which are unique elements of HBase.
Quantum Calculi—From Theory to Language Design

In the last 20 years, several approaches to quantum programming have been introduced. In this survey, we focus on the QRAM (Quantum Random Access Machine) architectural model. We explore the twofold perspective (theoretical and concrete) of the approach and we list the main problems one has to face in quantum language design. Moreover, we propose an overview of some interesting languages and open-source platforms for quantum programming currently available. We also provide the higher-order encoding in the functional languages qPCF and IQu of the well known Deutsch-Jozsa and Simon’s algorithms.