Reviewerly/exHarmony huggingface dataset:
Fuzzy Set Similarity Between Fuzzy Words

Fuzzy set similarity measures determine the similarity between two fuzzy sets. Semantic similarity measures determine the similarity between concepts within an ontology. Research in determining sentence similarity has used semantic similarity between fuzzy words that have been structured in an ontology based on data provided by human experts to create fuzzy sets for these fuzzy words. The research uses four different kinds of fuzzy set similarity measures, three that are standard ones for type-1 fuzzy sets and another one based on the distance between defuzzified and normalized COGs for type-2 fuzzy sets and examines both the Pearson and Spearman correlations among these different fuzzy set similarity measures on fuzzy words from four of the six categories established in past sentence similarity research. The results show that all standard fuzzy set measures are highly correlated though some categories of fuzzy words result in higher correlations than others. The standard fuzzy set similarity measures have lower correlation with the one developed for similarity between type-2 fuzzy sets using their defuzzified and normalized COGs.
Multivariate analysis of ecological data with ade4

"Multivariate analysis of ecological data with ade4." Journal of Statistical Computation and Simulation, 90(6), pp. 1150–1151
Bayesian Estimation of the Linear Expenditure System

We argue that the Bayesian approach is appealing to parameter estimation for multi-sector economic models, in that it (i) handles problems with many parameters and little data, (ii) allows for explicit and flexible formulation of prior knowledge on parameter values, and parameter constraints, (iii) provides full account of uncertainty of the estimates, (iv) is based on a single estimation strategy for a wide variety of models. It is an alternative to the more common maximum entropy (minimum cross entropy) techniques. We specify a Bayesian model for the linear expenditure system, and estimate it based on data for Poland, using the open source Stan package. We then extend the basic formulation to allow for changes in tastes, represented by time-varying marginal budget shares.
CONTEMPORARY ISSUES OF EDUCATIONAL ACTIVITIES: ESSENTIAL FEATURES AND EFFICIENCY OF A DIDACTIC GAME

Bearing in mind that one of the key challenges in the school system is the development of mental abilities, in this paper, we discuss the possibilities of the influence of didactic games on the development of concepts about geometric forms, the abilities of analytical and synthetic thinking and the abilities to draw a conclusion. The aim of this research was to examine the possibility of encouraging the development of the thinking skills of first grade pupils of the primary school, using a system of didactic games. Research design involved the implementation of a parallel-group experiment on the sample of 163 primary school pupils (6.5 to 7.5 years old). The following instruments were applied: The Kamenov’s instrument for testing the level of development of concepts on geometric shapes, Raven's progressive colour matrices and Kohs Block Design Test. The results of the research showed that the system of didactic games significantly influenced the development of concepts about geometric shapes, the development of the ability to conclude, but not the ability of the analytical and synthetic thinking of pupils.
Learning Strategies Used By The Eighth Grade Students Of Special Program At MTs Negeri 2 Sukoharjo In Developing Reading Comprehension

This research is aimed at describing (1) type of learning strategies, (2) learning strategies applied by higher and lower achievers, (3) the percentage of learning strategies applied, and (4) similarities and differences of learning strategies used by the eighth grade students of special program at MTs Negeri 2 Sukoharjo in developing reading comprehension.The type of research is qualitative research. The object is learning strategies. The subject of this study is the eighth grade students of special program at MTs Negeri 2 Sukoharjo. Data collection was conducted by applying document, questionnaire, observation, and interview. The results showed that: (1) types of learning strategies applied, namely meta-cognitive strategies, cognitive strategies, and socio-affective strategies. (2) The higher achiever students used meta-cognitive strategies (95%) from 8 meta-cognitive strategies and cognitive strategies (90,76%) from 13 cognitive strategies. While the lower achiever students used meta-cognitive strategies (25%) from 8 meta-cognitive strategies, cognitive strategies (12,82%) from 13 cognitive strategies, and socio affective strategies (100%) from 2 socio-affective strategies. (3) In applying learning strategies, overall, all of students used meta-cognitive strategies (63,75%) from 8 meta-cognitive strategies, cognitive strategies (60,51%) from 13 cognitive strategies, and socio affective strategies (51,67%) from 2 socio-affective strategies. Most dominant learning strategy used by all students was meta-cognitive strategy. (4) There were some similarities found in meta-cognitive strategies, the cognitive strategies, while in socio-affective strategies, there was no similarities. The differences were the higher achiever used delayed production and self-evaluation. Besides, there were no students of lower achiever used them. In cognitive strategies, the higher achievers applied repetition, resourcing, translation, grouping, note taking, deduction, recombination, imagery, keyword, contextualization, elaboration, transfer, and inferencing while the lower achievers used translation, grouping, and note taking. The lower achievers applied socio-affective strategies, while there were no students of higher achiever did that.
Towards Lakosian Multilingual Software Design Principles

Large software systems often comprise programs written in different programming languages. In the case when cross-language interoperability is accomplished with a Foreign Function Interface (FFI), for example pybind11, Boost.Python, Emscripten, PyV8, or JNI, among many others, common software engineering tools, such as call-graph analysis, are obstructed by the opacity of the FFI. This complicates debugging and fosters potential inefficiency and security problems. One contributing issue is that there is little rigorous software design advice for multilingual software.In this paper, we present our progress towards a more rigorous design approach to multilingual software. The approach is based on the existing approach to the design of large-scale C++ systems developed by Lakos. The FFI is an aspect of physical rather than logical architecture. The Lakosian approach is one of the few design methodologies to address physical design rather than just logical design. Using the MLSA toolkit developed in prior work for analysis of multilingual software, we focus in on one FFI – the pybind11 FFI. An extension to the Lakosian C++ design rules is proposed to address multilingual software that uses pybind11. Using a sample of 50 public GitHub repositories that use pybind11, we measure how many repositories would currently satisfy these rules. We conclude with a proposed generalization of the pybind11-based rules for any multilingual software using an FFI interface.
LibGuides: Virtual & Augmented Reality: Educational Resources

Learn about Virtual and Augmented Reality, and services available at the Nexus Collaborative Learning Lab!
Color Display Using 365 nm UV Light Projection onto an Optimized Fluorescent Pattern Surface

We propose a method to generate a display with enhanced color appearance by projecting monochromatic 365&#xA0;nm ultraviolet (UV) light onto an optimized pattern surface containing red, green, and blue fluorescent pigments. Our method is based on the Paxel framework proposed by Pjanic and Grundh&#xF6;fer [1] describing the color formulation process when projecting spatially varying illumination onto a printed surface containing pigments with spatially varying properties, in our case fluorescent emission. In this article, we extend this approach by optimizing the spatial arrangement and surface ratios of the used red, green, and blue transparent fluorescent pigments within the patterns. These ratios are optimized in order to obtain a color gamut with maximized volume. Furthermore, we propose a novel setup where the fluorescent inks are printed onto a transparent film which is then placed onto a black, light-absorbing material. This projection setup, consisting of an emissive and a black absorbing layer, enables us to produce high-contrast imagery even if a high amount of ambient light is present. Finally, to reduce the visually unpleasant appearance of the printed patterns, we introduce an alternative hexagon-shaped pattern structure.
Machine learning applied to speaker verification of fake voice recordings

For forensic application, speaker verification consists of evaluating whether the voice of a suspect matches the evidence audio recording. In this project, we propose a solution based on machine learning for speaker verification of audios with fake intonation. The input of the system corresponds to indirect characteristics of the audio recordings, and the classifier is a neural network, in which the hyperparameters are adjusted using cross validation. The performance results are: OA (Overall Accuracy) of 88.2%, P (Precision) of 84.5%, R (Recall) of 90%, F1 of 87.2% and AUC (Area under the Curve) of 93.8%.
Perception of Special Education Experts on the Implementation and Improvement of IEP

This study investigated the perception of the concept and practice of individualized education plans and to search for improvements. The research method was the focus group interview(FGI). The participants were grouped into 6 groups of 2 professors, 6 junior supervisors, 9 elementary and secondary resource room teachers, and 11 elementary and secondary special school teachers. As a result of the research, 3 domains, 11 topics and 21 sub-topics were derived. First, there was a gap between the realistic and ideal concept of individualized education plan recognized by educational experts. Second, there was a difficulty in the actual practice of individualized education plan due to the lack of connection between curriculum and class, NEIS IEP’s pros and cons, lack of cooperative approach of individualized education support team, and lack of continuity of education through assessment of the individualized education plan. Third, there was a need for strengthening the capacity of special education teachers and improving the specific operating system as an improvement plan for the individualized education plan. Based on the results, the researchers provided directions for improving individualized education plan and further research.
Confidently Accessing Research in a Turbulent Time

This paper was presented at the 2019 Annual Kansas Consortium-Intelligence Community Center for Academic Excellence (Kansas-ICCAE) Conference held at the University of Kansas on April 23, 2019. It was the keynote for the session entitled Cyber Attacks, Intellectual Property, & University Open Research: The Perfect Storm.
Mechanical Entanglement: A Collaborative Haptic-Music Performance

Conference paper published in Proceedings of the 16th Sound & Music Computing ConferenceSMC 2019, May 28 - 31, Malaga, Spain, available at http://smc2019.uma.es/
ICARUS

ICARUS is a game/performance for drum-kit and live electronics. The work explores improvised musical structures within a game setting, where the player navigates between five musical chapters. Each of these levels defines a particular set of musical interactions, sonorities, and performance possibilities through distinct mappings and level design. The performer is free to improvise, fail, explore, and through trial and error understand what the game rules are and complete each chapter. This results into a dynamic audio-visual performance, where while the rules are fixed, each level can be completed in multitude of ways. The game is designed specifically for the augmented drum-kit and the instrument's affordances; performance minutiae, digital electronics and musical parameters are analyzed and used as control input to the game. As the musical instrument offers a much wider range of expressive possibilities compared to a conventional game controller, the result is a musically expressive game play performance where the game acts as the mediator to the improvised drum-kit performance, while the music becomes the live soundtrack of the game.
Usability Study of a Student-Centered Career Resource Website for Faculty and Staff

Usability study of a website content includes: Statement of the Problem, Literature Review, Methodology, Results, Discussion and Appendices
A Study on Generic Data Analysis Application the Cloud Using SGX

The analysis itself however in the midst of preparation and post-processing steps. Handling information returning directly from the supply, e.g. a sensor, typically needs preconditioning like parsing and removing orthogonal data before data processing algorithms will be applied to research the info. complete data processing frameworks generally don't offer such parts since they need a currency computer file format. what is more, they're typically restricted to the offered algorithms or a speedy integration of latest algorithms for the aim of fast testing isn't attainable. to deal with this disadvantage, we have a tendency to gift the info analysis framework Knowing, that is well extendable with extra algorithms by exploitation associate OSGi compliant design. the software package and therefore the hypervisor out of the TCB; therefore, confidentiality and integrity square measure preserved albeit these massive parts square measure compromised. VC3 depends on SGX processors to isolate memory regions on individual computers, and to deploy new protocols that secure distributed MapReduce computations. VC3 optionally enforces region self-integrity invariants for all MapReduce code running inside isolated regions, to stop attacks thanks to unsafe memory reads and writes. Experimental results on common benchmarks show that VC3 performs well compared with unprotected Hadoop: VC3's average runtime overhead is negligible for its base security guarantees, 4.5% with write integrity and eight with read/write integrity.
Saliency based 360° Video Contents Encoding for Streaming Service (poster)

Video streaming service has been essential to the Internet ecosystem since a majority of Internet contents is consumed via streaming ser-vices, such as Netflix and Youtube [3]. Moreover, contents providers started to upload 4K and even 8K videos on streaming service to satisfy users' demand for higher quality of streaming service in accordance with the recent refinement on display technology.
Data Mining Methods for Terrorist Activity

Identification of outliers is an important component of data mining since such observations can have a profound influence and distort the analysis. In the recent years, detection of outlier has been broadly applied in the field of data mining. It is an important method for many research applications such as credit card fraud, computer intrusion detection, and criminal activities in electronic commerce, medical diagnosis and anti-terrorism, image processing. Detection of outliers-objects which is abnormal away from the remaining of the data set. In this paper, to modify a definition of neighbourhood parameter in information system has been proposed to discover the outliers in rough set theory. The computational results are compared with other existing methods. Keywords: Data mining, rough set, value difference metric and neighborhood outlier Cite this Article Ezhilarasi R, P. Arumugam. Data Mining Methods for Terrorist Activity. Research & Reviews Journal of Statistics . 2019; 8(1): 38–43p.
A Synthesis-by-Analysis Network with Applications in Image Super-Resolution

Recent studies have demonstrated the successful application of convolutional neural networks in single image super-resolution. In this paper, we present a general synthesis-by-analysis network for super-resolving a low-resolution image. Unlike Laplacian Pyramid Super-Resolution Network (LapSRN) that progressively reconstructs the sub-band residuals of high-resolution images, our proposed network breaks through the sequential dependency to expand the input and output into multiple disjoint bandpass signals. At each band, we perform the nonlinear mapping in truncated frequency interval by applying a carefully designed sub-network. Specifically, we propose a validated network sub-structure that considers both efficiency and accuracy. We also perform exhaustive experiments in existing commonly used dataset. The recovered high-resolution image is competitive or even superior in quality compared to those images produced by other methods.
Scyther Validated Session Key Establishment in IoT

Key agreement is a bed rock of all security services and primitives. Asymmetric cryptography is a widely adopted method for negotiating a session key over an insecure communication channel. The usage of asymmetric cryptography for key agreement based on elliptical curve cryptography in IoT is of considerable research interest. This owning to the fact that ECC based cryptography is computational less intense than traditional cryptography. The computational effectiveness makes it suitable for resource constraint devices used in IOT and smart city application. In this paper, a de-facto key exchange protocol, Elliptical Curve Diffie–Hellman(ECDH) is evaluated and formally validated .The limitations of ECDH are also pointed out. A session key agreement scheme based on elliptical curve cryptography has been suggested. The scheme has also been simulated in Scyther to validate it against various attacks.
Computational Thinking

A scientific revolution does not only create new knowledge. It also generates new ways of thinking, new ways of asking questions, and new ways of answering them. Before the scientific revolution at the beginning of the seventeenth century, when questions were raised, for example, about whether or not blood circulated in the body, people looked for answers in the ancient texts. Aristotle and Galen taught that blood did not circulate in the body; therefore, the question had been answered. How did Aristotle and Galen know what they knew? That question was not posed. They were more knowledgeable than us, and that was sufficient.
DunDi: Improving Robustness of Neural Networks Using Distance Metric Learning

The deep neural networks (DNNs), although highly accurate, are vulnerable to adversarial attacks. A slight perturbation applied to a sample may lead to misprediction of the DNN, even it is imperceptible to humans. This defect makes the DNN lack of robustness to malicious perturbations, and thus limits their usage in many safety-critical systems. To this end, we present DunDi, a metric learning based classification model, to provide the ability to defend adversarial attacks. The key idea behind DunDi is a metric learning model which is able to pull samples of the same label together meanwhile pushing samples of different labels away. Consequently, the distance between samples and model’s boundary can be enlarged accordingly, so that significant perturbations are required to fool the model. Then, based on the distance comparison, we propose a two-step classification algorithm that performs efficiently for multi-class classification. DunDi can not only build and train a new customized model but also support the incorporation of the available pre-trained neural network models to take full advantage of their capabilities. The results show that DunDi is able to defend 94.39% and 88.91% of adversarial samples generated by four state-of-the-art adversarial attacks on the MNIST dataset and CIFAR-10 dataset, without hurting classification accuracy.
Prediction Based Deep Autoencoding Model for Anomaly Detection

Latent variables and reconstruction error generated from auto encoder are the common means for anomaly detection dealing with high dimensional signals. They are exclusively typical representations of the original input, and a plenty of methods utilizing them for anomaly detection have achieved good results. In this paper, we propose a new method combining these two features together to generate proper scores for anomaly detection. As both these two features contain useful information contributing to anomaly detection, good results can be expected by fusion of those two. The architecture proposed in this paper comprises of two networks, and we only use normal data for training. To compress and rebuild an input, a deep auto encoder (AE) is utilized where low dimensional latent variables and reconstruction error can be obtained, and compactness loss is introduced on latent variables to maintain a low intra-variance. Meanwhile, multi-layer perceptron (MLP) network which takes the generated latent variables as input is established aiming at predicting its corresponding reconstruction error. By introducing MLP network, anomalies sharing similar reconstruction error yet different distribution of latent variables to normal data or vice versa can be separated. These two networks, AE and MLP are trained jointly in our model and the prediction error form MLP network is used as the final score for anomaly detection. Experiments on several benchmarks including image and multivariable datasets demonstrate the effectiveness and practicability of this new approach when comparing with several up-to-data algorithms.
An Improved method for assessing quality of a WEB site based on completeness

Assessing quality of a web site is most important especially because people world over are dependent on the quality of the content hosted on the WEB site for various purposes. Every aspect that reflects the quality of the WEB site must be considered so that the WEB site is used by many relevant users and the content is straight away used for various purposes. The content is hosted on the WEB is accepted and used by the users without any doubt. Many factors are to be considered for assessing the quality of the WEB sites. There as many as 42 factors such as look and feel, ease of navigation, structure, quality of content, quality of mullti-media etc. that needs to be considered for assessing overall quality of a WEB site. “Completeness” is one such factor. Completeness is all about total availability of the data related to a specific context that the user is pursuing. An incomplete WEB site will be unused and eventually gets out of the ambit of search engines and also the users loose interest in such web sites. Every quality factor must be assessed independently by using a model and all the models of all the factors must be combined to reflect the overall quality of the WEB site. Every quality factors can be expressed in terms of some sub-factors, the quality of which must be individually computed and combined to arrive at quality value of the Factor. This paper is primarily focused on presenting a model using which the quality assessment of a web site can be made considering the factor called “Completeness” which is one of the factors that should be considered for computing the quality of the WEB sites. There can be much of the disconnected in the content hosted on the web site such as missing href, columns in the tables and forms. The more the disconnectedness in the content that is hosted on the WEB site, the less the quality, as the information hosted on the WEB site is incomplete and less connected.