Reviewerly/exHarmony huggingface dataset:
Presentation of an Efficient Automatic Short Answer Grading Model Based on Combination of Pseudo Relevance Feedback and Semantic Relatedness Measures

Automatic short answer grading (ASAG) is the automated process of assessing answers based on natural language using computation methods and machine learning algorithms. Development of large-scale smart education systems on one hand and the importance of assessment as a key factor in the learning process and its confronted challenges, on the other hand, have significantly increased the need for an automated system with high flexibility for assessing exams based on texts. Generally, ASAG methods can be categorized into supervised and unsupervised approaches. Supervised approaches such as machine learning and especially deep learning methods require the manually constructed pattern. On the other hands, while in the assessment process, student's answer is compared to an ideal response and scoring is done based on their similarity, semantic relatedness and similarity measures can be considered as unsupervised approaches for this aim. Whereas unsupervised approaches do not require labeled data they are more applicable to real-world problems and are confronted with fewer limitations. Therefore, in this paper, various measures of semantic relatedness and similarity are extensively compared in the application of short answer grading. In the following, an approach is proposed for improving the performance of short answer grading systems based on semantic relatedness and similarity measures which leverages students' answers with the highest score as feedback. Empirical experiments have proved that using students' answers as feedback can considerably improve the precision of semantic relatedness and similarity measures in the automatic assessment of exams with short answers.
Replicating Relevance-Ranked Synonym Discovery in a New Language and Domain

Domain-specific synonyms occur in many specialized search tasks, such as when searching medical documents, legal documents, and software engineering artifacts. We replicate prior work on ranking domain-specific synonyms in the consumer health domain by applying the approach to a new language and domain: identifying Swedish language synonyms in the building construction domain. We chose this setting because identifying synonyms in this domain is helpful for downstream systems, where different users may query for documents (e.g., engineering requirements) using different terminology. We consider two new features inspired by the change in language and methodological advances since the prior work's publication. An evaluation using data from the building construction domain supports the finding from the prior work that synonym discovery is best approached as a learning to rank task in which a human editor views ranked synonym candidates in order to construct a domain-specific thesaurus. We additionally find that FastText embeddings alone provide a strong baseline, though they do not perform as well as the strongest learning to rank method. Finally, we analyze the performance of individual features and the differences in the domains.
Multi-Wavelengths Michelson Interferometer Based Spatial Phase Shift Shearography with Color Camera

&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;The paper describes the use of a color camera for spatial phase shift shearography according to the carrier frequency method, whereby the focus is on measurement stability and practical usability. The basics are derived from the simple and extremely robust Michelson interferometer setup with spatial aperture and quality criteria are formulated according to the achievable result quality. The state of research of Multi-Wavelengths application is shown in general as well as in a publication, which serves as a further basis for comparison. The use of three laser sources and a Bayer-Matrix RGB color camera is considered to be the most effective method for the following development. For this purpose, the 3 out-of-plane illumination arrangement is used, which provides the three separate results in the spatial directions in only one measurement (for shear direction in x). The conditions and the special features of the spatial phase shift with a color camera are discussed and theoretical comparisons to the classical application with a black and white camera are made. On an experimental test, the high-resolution quality and usability of the presented method (3 Out-of-Plane Multi-Wavelengths RGB Camera spatial phase shift shearography) is demonstrated and validated. Prospective considerations are the extension to dual-shear applications (shear direction in x and in y simultaneously).&lt;/div&gt;&lt;/div&gt;
INTEGRATION OF TEAMWORK SKILLS IN TEACHING IN ORDER TO MEET THE LEARNING OUTCOMES IN THE CDIO SYLLABUS: APPLICATION TO TECHNOLOGY AND ENGINEERING

With the goal of fundamental and comprehensive innovation of higher education towards shifting the educational process from mainly equipped with the knowledge to the complete development of learners’ competencies and qualities, universities have made many innovation initiatives to meet the increasing demands for higher education. To implement the above objectives, one of the innovations is to apply the CDIO approach as a standard framework for developing training programs, advanced training techniques to meet social needs and response, and international quality standards. Improving teaching capacity for teachers through the selection of teaching methods integrated between teaching professional knowledge with personal skills and interactive skills compatible with output standards and methods evaluate the effectiveness. Teamwork skills are a CDR on skills in CDIO syllabus, so it is necessary to implement teaching, organizing and evaluating group activities. The paper focused on the organization and method of implementing group activities in each class when implementing the training program by the CDIO approach.
Estimation of solar radiation per month on horizontal surface using adaptive neuro-fuzzy inference system (case study in Surabaya)

One source of natural energy that is always available and has a great energy that is solar energy. Solar energy can emit radiation known as solar radiation. Solar radiation can be utilized into alternative energy that is in the form of electrical energy with the help of Photovoltaic which is currently almost all components require a source of electrical energy. Solar radiation can be measured by a measuring device called pyranometer. Pyranometer including measuring instruments that have a relatively expensive price compared to other measuring instruments. So as to raise research to get an estimation of solar radiation per hour on photovoltaic. This estimate uses Adaptive Neuro-Fuzzy Inference System (ANFIS) to model solar radiation estimators. ANFIS is a fuzzy inference system based on an adaptive neural network that adopts a learning system from an artificial neural network. This research uses an ANFIS which in its learning can use reversal algorithm or hybrid algorithm. The results of this study indicate that the ANFIS method has a smaller Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) values than the ELM method of artificial neural networks. Of the two methods used are ELM. ANFIS for ELM method can deliver the RMSE value of 15.42 and using ANFIS method got the RMSE value of 0.87. In addition to the value of MAE on the ELM method obtained by 11.72 and for ANFIS method obtained by 0.22.
Review of Standards for Mineral Resources and Reserves Estimation in Korea

In Korea, even though the standards for mineral resources and reserves estimation were established through the Korean Industrial Standards, terminology confusion is increasing due to revision of the laws and establishment of new standards by the government. In this study, the existing Korean standards for mineral resources and reserves estimation were reviewed and compared with international standards. Based on these reviews, the situation in Korea and international standards, a terminology system was proposed. Notation and evaluation criterion for mineral resources and reserves should be established carefully according to a legal standards, because they are not just technical terms but also criteria for legal decisions. Thus, it is necessary for the government, industry, and academia to discuss the standard system in more depth.
Image enhancement in a miniature self-imaging degenerate optical cavity

Image-enhancing optical cavities offer an intriguing platform for nonlinear optical processing of two-dimensional signals, with potential applications in optical information processing and optically implemented artificial neural networks. Here, we analyze the performance of a self-imaging degenerate cavity via numerical simulations and show the existence of a cavity-size-dependent minimum spread in the transverse-mode resonances. This nondegeneracy, in turn, leads to an inherent trade-off between the resonant enhancement and the fidelity of the intracavity image as the cavity finesse changes.
A Clustering Algorithm Based on Minimum Spanning Tree and Density

A Minimum spanning tree (MST) clustering algorithm based on density is proposed under the background of no effective support on the dataset with noises. Split and merge stages are employed for the proposed clustering algorithm. Furthermore, density estimation method is designed for split stage, and maximal connected sub graph is employed for merge stage. The experiments are performed on synthetic and real datasets, and clustering results show the proposed clustering algorithm can detect clusters with noises. Moreover, clusters with complex shapes can be recognized as well and it demonstrates the proposed clustering algorithm is robust to noises.
Methods for Calculating and Coding the Parameters of Autoregressive Speech Model when Developing the Vocoder Based on Fixed Point Signal Process

Consideration is given to calculating and coding the parameters of vocoders operating at speed of 1200 and 2400 bps. The efficient methods for calculating and coding the parameters of the autoregressive speech signal model such as line spectral frequencies and the pitch period are proposed. The works on computation optimization allowed a vocoder to be implemented in real time on the signal processor ADSP-2191.
Clustering the basic human category indicator levels in Banten region

Abstract Indonesia has an inequality in human development, education provides the highest inequality. Loss that occurs in the education category reaches 20.8 percent. In the categories of decent living standards reaches 17.3 percent, and in the health category 16.5 percent. Most volunteers and the society take responsive action after getting information from the media or from the local communities. Therefore, this study aims to find out which areas have problems on indicators of basic human categories in a certain year. Regions will be grouped into three cluster groups, regions with low values, regions with medium values, and regions with high values. K-Means is an algorithm used to classify regions. The results of this study are, knowing the areas that have the lowest, medium, and highest values based on indicators of basic human categories in 2013 to 2017. Pandeglang and Serang Regencies are the lowest indicators such as life expectancy with a center point of 62.928 in 2013 and up to 64.03 in 2017, mean years of school is 6.299 to 6.666, per capita expenditure is 8411.72 to 9065.33. The cities of South Jakarta, East Jakarta, and Central Jakarta, areas that are at the highest indicators such as life expectancy with a center point of 72.938 to 73.107, expected years of school is 12.673 to 13.438, mean years of school is 10.469 to 10.851, and per capita expenditure with a center point of 17942.57 up to 18856.4.
Low-Dimensional Classification of Text Documents

In this paper we focus on overcoming a common belief that accurate subject classification of text documents must involve high dimensional feature vectors. We study the fastText algorithm in terms of its ability to find and extract well distinguishable characteristics for a text corpora. In research we compare the achieved accuracy in the task of subject classification with various size of feature space selected. Finally, we attempt to discover the foundation behind fastText’s well performance.
Separation of geochemical anomalies by concentration-area and concentration-number methods in the Saqez 1:100,000 sheet, Kurdistan

Regional exploration and identification of anomalies can be done by geochemical data resulted from stream sediments. In this paper, fractal methods of concentration-number and concentration-area were used for regional studies, and abnormalities of elements including gold, arsenic, bismuth and mercury were studied. Statistical processing of these elements took place with the help of 1063 samples of stream sediments in the Saqez 1:100,000 sheet (Kurdistan Province). The elements were analyzed in the laboratory of the Geological Survey and Mineral Exploration of Iran by ICP-OES method and Au element was analyzed by Emission Spectrograph method. According to the position of geology, existing structures and the dominant rock type, the comparison between the map prepared methods of concentration-number and concentration-area was conducted in Saqez sheet. In general, the results of the two methods showed that in the south, southwest, center and west of the region, the concentration of these elements is increased, and presence of ore promising areas is high in these areas. Concentration-number method showed a good overlap with concentration-area method; making extensive geochemical halos and encompassing known ores of Qolqoleh, Kervian and Qabghlojeh, it follows the expected process of mineralization in Sanandaj-Sirjan zone.
Multitasking collision-free optimal motion planning algorithms in Euclidean spaces

We present optimal motion planning algorithms which can be used in designing practical systems controlling objects moving in Euclidean space without collisions. Our algorithms are optimal in a very concrete sense, namely, they have the minimal possible number of local planners. Our algorithms are motivated by those presented by Mas-Ku and Torres-giese (as streamlined by Farber), and are developed within the more general context of the multitasking (a.k.a. higher) motion planning problem. In addition, an eventual implementation of our algorithms is expected to work more efficiently than previous ones when applied to systems with a large number of moving objects.
Cyber Treat Intelligence Modeling

This paper proposes semantic approach to manage cyber threat intelligence (CTI). The economic rational is presented as well as functional needs. Several cases of domain standards, tools and practices are modeled as a representation of the CTI sub-domain. This work focuses on the technical and operational CTI that is common to most organizations.
Finding the Transitive Closure of Functional Dependencies using Strategic Port Graph Rewriting

We present a new approach to the logical design of relational databases, based on strategic port graph rewriting. We show how to model relational schemata as attributed port graphs and provide port graph rewriting rules to perform computations on functional dependencies. Using these rules we present a strategic graph program to find the transitive closure of a set of functional dependencies. This program is sound, complete and terminating, assuming that there are no cyclical dependencies in the schema.
A transformation‐based approach to Gaussian mixture density estimation for bounded data

Finite mixture of Gaussian distributions provide a flexible semiparametric methodology for density estimation when the continuous variables under investigation have no boundaries. However, in practical applications, variables may be partially bounded (e.g., taking nonnegative values) or completely bounded (e.g., taking values in the unit interval). In this case, the standard Gaussian finite mixture model assigns nonzero densities to any possible values, even to those outside the ranges where the variables are defined, hence resulting in potentially severe bias. In this paper, we propose a transformation-based approach for Gaussian mixture modeling in case of bounded variables. The basic idea is to carry out density estimation not on the original data but on appropriately transformed data. Then, the density for the original data can be obtained by a change of variables. Both the transformation parameters and the parameters of the Gaussian mixture are jointly estimated by the expectation-maximization (EM) algorithm. The methodology for partially and completely bounded data is illustrated using both simulated data and real data applications.
Detailed Surface Geometry and Albedo Recovery from RGB-D Video under Natural Illumination

This article presents a novel approach for depth map enhancement from an RGB-D video sequence. The basic idea is to exploit the photometric information in the color sequence to resolve the inherent ambiguity of shape from shading problem. Instead of making any assumption about surface albedo or controlled object motion and lighting, we use the lighting variations introduced by casual object movement. We are effectively calculating photometric stereo from a moving object under natural illuminations. One of the key technical challenges is to establish correspondences over the entire image set. We, therefore, develop a lighting insensitive robust pixel matching technique that out-performs optical flow method in presence of lighting variations. An adaptive reference frame selection procedure is introduced to get more robust to imperfect lambertian reflections. In addition, we present an expectation-maximization framework to recover the surface normal and albedo simultaneously, without any regularization term. We have validated our method on both synthetic and real datasets to show its superior performance on both surface details recovery and intrinsic decomposition.
Active Informative Pairwise Constraint Formulation Algorithm for Constraint-Based Clustering

Constraint-based clustering utilizes pairwise constraints to improve clustering performance. In this paper, we propose a novel formulation algorithm to generate more informative pairwise constraints from limited queries for the constraint-based clustering. Our method consists of two phases: pre-clustering and marking. The pre-clustering phase introduces the fuzzy c-means clustering (FCM) to generate the cluster knowledge that is composed of the membership degree and the cluster centers. In the marking phase, we first propose the weak sample with the larger uncertainty expressed by the entropy of the membership degree. Then, we study the strong sample that contains less uncertainty and should be closest to its cluster center. Finally, given weak samples in descending order of entropy, we formulate informative pairs with strong samples and seek answers using the second minimal symmetric relative entropy priority principle, which leads to more efficient queries. Making use of the pairwise constraint k-means clustering (PCKM) as the underlying constraint-based clustering algorithm, further data experiments are conducted in several datasets to verify the improvement of our method.
Adaptive Ranking-Based Constraint Handling for Explicitly Constrained Black-Box Optimization

We propose a novel constraint-handling technique for the covariance matrix adaptation evolution strategy (CMA-ES). The proposed technique is aimed at solving explicitly constrained black-box continuous optimization problems, in which the explicit constraint is a constraint whereby the computational time for the constraint violation and its (numerical) gradient are negligible compared to that for the objective function. This method is designed to realize two invariance properties: invariance to the affine transformation of the search space, and invariance to the increasing transformation of the objective and constraint functions. The CMA-ES is designed to possess these properties for handling difficulties that appear in black-box optimization problems, such as non-separability, ill-conditioning, ruggedness, and the different orders of magnitude in the objective. The proposed constraint-handling technique (CHT), known as ARCH, modifies the underlying CMA-ES only in terms of the ranking of the candidate solutions. It employs a repair operator and an adaptive ranking aggregation strategy to compute the ranking. We developed test problems to evaluate the effects of the invariance properties, and performed experiments to empirically verify the invariance of the algorithm. We compared the proposed method with other CHTs on the CEC 2006 constrained optimization benchmark suite to demonstrate its efficacy. Empirical studies reveal that ARCH is able to exploit the explicitness of the constraint functions effectively, sometimes even more efficiently than an existing box-constraint handling technique on box-constrained problems, while exhibiting the invariance properties. Moreover, ARCH overwhelmingly outperforms CHTs by not exploiting the explicit constraints in terms of the number of objective function calls.
MicroITS: A Scaled-Down ITS Platform

Intelligent Transportation Systems (ITS) are an excellent illustration of the types of challenges that future technologists must address. In previous work we presented a course designed to engage students with theoretical aspects of embedded and cyber-physical systems. In this paper we present MicroITS, a platform addressing applied aspects. We articulate the design goals that we believe are needed to achieve engagement in an educational setting, and describe the platform and its baseline functionality. We briefly describe example projects that can be realized using MicroITS. Our hope is that this report will encourage the development of a community of educators and students interested in the use and the continued development of the platform.
Forgery Attacks on FlexAE and FlexAEAD

is one of the round-1 candidates in the ongoing NIST Lightweight Cryptography standardization project and an evolution of the previously published scheme. For each data block, the mode performs multiple calls to a permutation in an Even-Mansour construction. The designers argue that the mode permits using a permutation with slightly suboptimal properties in order to increase performance, such as allowing differential distinguishers which cannot be extended to attacks on the full construction. We first show that this claim is incorrect since differences can not only be introduced via the processed data, but also via the mode’s control flow. Second, by exploiting a strong differential clustering effect in the permutation, we propose several forgery attacks on with complexity less than the security bound given by the designers, such as a block reordering attack on full with estimated success probability about $$2^{-54}$$. Additionally, we discuss some trivial forgeries and point out domain separation issues.