Reviewerly/exHarmony huggingface dataset:
State-Space Realizations and Optimal Smoothing for Gaussian Generalized Reciprocal Processes

This technical note derives stochastic realization and optimal smoothing algorithms for a class of Gaussian generalized reciprocal processes (GGRP). The note exploits the interplay between reciprocal processes and Markov bridges which underpin the GGRP model. A forward-backward algorithm for stochastic realization of GGRP is described. The form on the inverse covariance matrix for the GGRP is used, via Cholesky factorization, to derive a procedure for optimal (MMSE) smoothing of GGRP observed in noise. The note demonstrates that the associated smoothing error is also a GGRP with known covariance which may be used to assess the performance of smoothing as a function of the model parameters. A numerical example is provided to illustrate the performance of the MMSE smoother compared to those derived from compatible Markov and reciprocal model-based algorithms.
Real-time Distance Courses to Improve Satisfaction and Competence - A Case Study on the Performance of Students Observing their Grades

Distance education is an important tool in current education; this paper focuses on the validation of the experience and the assessment by students for a teaching methodology in a real-time distance format. The methodology covers a series of elements such as the introduction of a single teacher type, a transformed teaching space and relevant aspects of distance courses. The results reveal how the flexibility and usefulness of the course format are relevant elements for the students' experience. The general conclusions of this study describe how a real-time distance course can be an area of opportunity for educational innovation in university courses.
Testing as a Feedback in a Smart University and as a Component of the Identification of Smart Systems

The test system is a measurement system through assessments of students’ knowledge. The main issues here are (a) the question of assessing the adequacy of the results of measuring the real level of knowledge and skills of students and (b) the question of a comprehensive assessment of the level of assimilation of the system of knowledge that connects quantitative and qualitative indicators. Both of these questions are related to the main problem—the problem of smart system identification. To answer the first question, we consider two models of algebraic formalization of the knowledge system’s representation—in the form of a free group of factors determining the knowledge system, and in the form of a semantic network, which we represent using finite Boolean algebras. Both models show that all binary tests, i.e., tests involving answers in the form of either “yes” or “no”, can be solved correctly, without knowing the specific essence of the proposed questions. In regards to the second question, we proposed a tensor estimate of the effectiveness of the functioning of a smart system with sufficiently rigid restrictions. Such an assessment can be determined, in particular, over a field of two elements for evaluating binary tests. In this paper, we show that this assessment can be viewed as coding the current state of the e-learning system, which is important for e-learning in relation to the issue of protection and storage of information. Similar algorithms are available for evaluating multi-choice tests. Algebraic smart systems’ formalization technique is used.
One Sample Ring-LWE with Rounding and Its Application to Key Exchange

In this paper, we introduce a new provably secure ephemeral-only RLWE+Rounding-based key exchange protocol and a proper approach to more accurately estimate the security level of the RLWE problem with only one sample. Since our scheme is an ephemeral-only key exchange, it generates only one RLWE sample from protocol execution. We carefully analyze how to estimate the practical security of the RLWE problem with only one sample, which we call the ONE-sample RLWE problem. Our approach is different from existing approaches that are based on estimation with multiple RLWE samples. Though our analysis is based on some recently developed techniques in Darmstadt, our type of practical security estimate was never done before and it produces security estimates substantial different from the estimates before based on multiple RLWE samples. We show that the new design improves the security and reduce the communication cost of the protocol simultaneously by using one RLWE+Rounding sample technique. We also present two parameter choices ensuring $$2^{-60}$$ key exchange failure probability which cover security of AES-128/192/256 with concrete security analysis and implementation. We believe that our construction is secure, simple, efficient and elegant with wide application prospects.
Moods in MOOCs: Analyzing Emotions in the Content of Online Courses with edX-CAS

Nowadays, there is a great variety of approaches aimed at facilitating the effectiveness of the teaching and learning processes. Many of those approaches are based on specific Educational Technologies whose objective is to make the teaching and learning experiences as human as possible. Tools and techniques for the recognition and analysis of emotions in online courses, such as those for the adaptations and the personalization of these courses, are some examples of the aforementioned approaches. At UAM we have designed and developed a tool for extracting and analysing the emotions of our online courses, such as SPOCs and MOOCs. We call this tool edX-CAS, which is the acronym for “Content Analyser System for edX MOOCs”. In this paper, a detailed description of this Tool is presented and an interesting description of current research work in the area of Polarity analysis, Sentiment analysis or Opinion mining in online courses is explained.
Investigating the Social Representations of Code Smell Identification: A Preliminary Study

Context: The identification of code smells is one of the most subjective tasks in software engineering. A key reason is the influence of collective aspects of communities working on this task, such as their beliefs regarding the relevance of certain smells. However, collective aspects are often neglected in the context of smell identification. For this purpose, we can use the social representations theory. Social representations comprise the set of values, behaviors and practices of communities associated with a social object, such as the task of identifying smells. Aim: To characterize the social representations behind smell identification. Method: We conducted a preliminary study on the social representations of smell identification by two communities. One community is composed of postgraduate students involved in various investigations related to code smells. The other community is composed of practitioners from industry, with experience in code reviews. We analyzed the associations made by the study participants about smell identification, i.e., what immediately comes to their minds when they think about this task. Results: One of the key findings is that only the community of practitioners strongly associates this task with semantic smells. This finding suggests research directions on code smells may be revisited, as they focus mostly on measurable or structural smells. Considering the novelty of using the social representations theory in software engineering, we also compiled a set of lessons learned. For instance, we observed some key challenges we faced in using the theory. These challenges include: (i) the predominance of associations with technical rather than non-technical concepts, and (ii) the fuzzy definitions of key concepts in our field. Conclusion: We found initial evidence that social representations analysis is a useful instrument to reveal discrepancies and commonalities on how different communities deal with a subjective task. Thus, we expect the experience reported in this paper may encourage and contribute to future studies of social representations in the field.
Impact security enhancement in chaotic quantum cryptography

In this paper the experimental and the numerical study of an optical key distribution quantum cryptography using BB84 protocol has been achieved. We create secret quantum key and the security of BB84 protocol is tested by inserting Eve in the system, the presence of Eve will cause disturbance to the synchronization of qubits between Alice and Bob and lead to bits errors. Finally we enhance the security of quantum cryptography using one-time pad technique and chaotic signal generated by semiconductor laser with an optical feedback. Mixing quantum key with chaotic signal will make ultimate security of the system.
Ensemble of Classifiers Using CNN and Hand-Crafted Features for Depth-Based Action Recognition

In this paper, we present an algorithm for action recognition that uses only depth maps. At the beginning we extract features describing the person shape in single depth maps. For each class we train a separate one-against-all convolutional neural network to extract class-specific features. The actions are represented by multivariate time-series of such CNN-based frame features for which we calculate statistical features. For the non-zero pixels representing the person shape in each depth map we calculate handcrafted features. For time-series of such handcrafted features we calculate the statistical features. Afterwards, handcrafted features that are common for all actions and CNN-based features that are action-specific are concatenated together resulting in action feature vectors. For each action feature vector we train a multi-class classifier with one-hot encoding of output labels. The prediction of the action is done by a voting-based ensemble operating on such one-hot encoding outputs. We demonstrate experimentally that on UTD-MHAD dataset the proposed algorithm outperforms state-of-the-art depth-based algorithms and achieves promising results on MSR-Action3D dataset.
Real Time Shadow Mapping for Augmented Reality Photorealistic Rendering

In this paper, we present a solution for the photorealistic ambient light render of holograms into dynamic real scenes, in augmented reality applications. Based on Microsoft HoloLens, we achieved this result with an Image Base Lighting (IBL) approach. The real-time image capturing that has been designed is able to automatically locate and position directional lights providing the right illumination to the holograms. We also implemented a negative “shadow drawing” shader that contributes to the final photorealistic and immersive effect of holograms in real life. The main focus of this research was to achieve a superior photorealism through the combination of real-time lights placement and negative “shadow drawing” shader. The solution was evaluated in various Augmented Reality case studies, from classical ones (using Vuforia Toolkit) to innovative applications (using HoloLens).
A quantum walk induced by Hoffman graphs and its periodicity

In order to define the discrete-time quantum walks, we divide the vertex set of a graph by two partitions and give a time evolution operator by a product of two local unitary operators on each partition. In this paper, we make such partitions from a Hoffman graph, which gives generalization of line graphs, and study a staggered walk. We first show that the square of the time evolution operator of the Grover walk is unitarily equivalent to that of the staggered walk on the generalized line graph induced from a Hoffman graph. Furthermore, we focus on the periodicity of quantum walks and show that the staggered walk on the generalized line graph is periodic whenever the Grover walk on the original graph is periodic by using the unitary equivalence. This work is the first trial to develop the relation between quantum walks and Hoffman graphs.
31‐4: <i>Invited Paper:</i> 3D Eyebox in Augmented and Virtual Reality Optics

The eyebox volume is a key design parameter in the optical design of augmented and virtual reality optics. The extent of the eyebox volume determines the experience of a user in seeing the entire virtual magnified image. Furthermore, a 3D description of eyebox facilitates the design of augmented and virtual reality products for a population of users. We define 3D eyebox and discuss visualization approaches to communicate it within interdisciplinary product design groups focused on research and development of augmented and virtual reality optics.
K-RCC: A novel approach to reduce the computational complexity of KNN algorithm for detecting human behavior on social networks

Social networks or social media is an online platform for billons of people around the world. This platform makes it easier for the people to have conversations, share information, share videos, instant messaging, create virtual world and more. The most dominant form of interaction on social media is by the text messaging. To detect the emotions from these text messages is not a difficult job for the humans as they are linked with emotions themselves. But to detect the emotions from these text messages by the computer is a difficult job to perform. Various models like fuzzy model, vector space model, keystroke dynamics, character n-gram models etc have been proposed in the literature for the detection of emotions but every model has its limitations and drawbacks. In this study a novel K-RCC (Reduced Computational Complexity) emotion detection model is proposed which is based on the K Nearest Neighbor (KNN) algorithm. The K-RCC algorithm reduces the computational complexity and incorrect classification rate which is the main drawback of the KNN algorithm. The computational complexity of the KNN algorithm is reduced up to some extent by the K-d Tree algorithm but on the cost of increased incorrect classification rate. The systematic performance analysis of K-RCC is carried out with four Machine learning classification algorithms for the detection of human emotions from tweets collected from social media site twitter. The emotions are classified under six emotional classes such as disgust, fear, joy, sadness, anger, and shame. The K-RCC performs better both in terms of reducing the computational complexity and incorrect classification rate and detection of human emotions.
Smarter Education: Leveraging Stakeholder Inputs to Develop Work Ready Curricula

Society expects higher education institutions to produce a competent labor force composed of graduates who are well equipped with the knowledge, skills, and attributes required for contributing to the economy in today's fast-paced digital age. The ability of graduates to transition successfully into employment is explained by the concept of graduate work readiness, which is of escalating interest to employers worldwide. Adaptability, flexibility, complex problem solving, and innovativeness are critical work-ready skills for Industry 4.0 workplaces. This paper frames work readiness as an essential component of smart education. Smart education in today's digital age is complex and thus requires the coordination and cooperation of multiple stakeholders. Focusing on adaptation, the first level of the smart systems model, this study draws upon stakeholder theory to propose a new conceptual framework for the collaborative codesign of adaptive smart curricula to foster student work readiness.
SANE 2.0: System for fine grained named entity typing on textual data

Assignment of fine-grained types to named entities is gaining popularity as one of the major Information Extraction tasks due to its applications in several areas of Natural Language Processing. Existing systems use huge knowledge bases to improve the accuracy of the fine-grained types. We designed and developed SANE 2.0, which is an extended version of our earlier work SANE (Lal et al., 2017). It uses Wikipedia categories to fine grain the type of the named entities recognized in the textual data. The entities for which types could not be found using Wikipedia categories are typed using an intelligent information extraction method that uses search results of yahoo search engine. SANE uses an efficient algorithm to assign the fine-grained type to the entities extracted from the data. Wikipedia categorizes related topics under common headings. From these categories, we constructed a database that contains Wikipedia articles and their corresponding categories. SANE uses this database to predict the category types of named entities. We use Stanford NER to identify named entities with their coarse-grained types. For locations, we use Geonames data separately. We calculate the similarity between an entity and its categories using word2vec. Each entity is assigned to the category that has the highest similarity score with it. Finally, we map the category to the most appropriate WordNet (Miller et al., 1995) type. The main contribution of this work is building a named entity typing system without the use of knowledge bases. Through our experiments, 1) we establish the usefulness of Wikipedia categories to Named Entity Typing, 2) we present an intelligent method of using yahoo search results for Named Entity Typing and 3) we show that SANE’s performance is on par with the state-of-the-art.
Intelligent On-line Exam Management and Evaluation System

Educational assessment plays a central role in the teaching-learning process as a tool for evaluating students' knowledge of the concepts associated with the learning objectives. The evaluation and scoring of essay answers is a process, besides being costly in terms of time spent by teachers, what may lead to inequities due to the difficulty in applying the same evaluation criteria to all answers. In this work, we present a system for online essay exam evaluation and scoring which is composed of different modules and helps teachers in creating, evaluating and giving textual feedbacks on essay exam solutions provided by students. The system automatically scores essays, semantically, using pair-wise approach. Using the system, the teacher can also give an unlimited number of textual feedbacks by selecting a phrase, a sentence or a paragraph on a given student's essay. We performed a survey to assess the usability of the system with regard to the time saved during grading, an overall level of satisfaction, fairness in grading and simplification of essay evaluation. Around 80% of the users responded that the system helps them to grade essays more fairly and easily.
Investigating the Combination of Adaptive UIs and Adaptable UIs for Improving Usability and User Performance of Complex UIs

An Object-Oriented User Interface Customization (OOUIC) framework has been previously proposed as a systematic approach toward reducing the complexity and improving usability and user performance. The OOUIC suggests combining adaptive and adaptable User Interfaces (UIs). Adaptive UIs reduce complexity while adaptable UIs enable customization for specific needs. The objective of this paper is to assess the combination of adaptive and adaptable UIs for improving usability and user performance of complex UIs. Complex UIs are the original interfaces of a complex system. Adaptive UIs automatically tailor UIs to be relevant to a user role but fail to satisfy an individual’s unique needs. Adaptable UIs enable a user to adjust UIs to meet needs for achieving specific tasks but are hard to use. Combining these two techniques enables users to customize complex UIs more easily. This research can be applied directly to the design and redesign of Product Lifecycle Management (PLM) systems. These systems have been consistently enhanced for their lack of usability. In this study complex UIs, adaptive UIs, adaptable UIs, and combination UIs were assessed by measuring 96 participants’ perceived usability, task time, and eye tracking data. Significant results were found on usability score (F (3, 189) = 12.14, p < .001), time spent (F (3, 189) = 63.40, p < .001), and fixation count (F (3, 189) = 39.72, p < .001), indicating that adaptive, adaptable, and combination UIs had better usability and user performance than complex UIs. The usability of combination UIs and adaptable UIs was better than adaptive UIs. Combining adaptive and adaptable UIs can facilitate users to simplify complex UIs.
Markov multiple feature random fields model for the segmentation of brain MR images

In the research of expert and intelligent systems on three-dimensional (3-D) brain magnetic resonance (MR) images, segmentation is the fundamental step of quantitative analysis of brain tissues. Due to the influence of various factors on brain MR images, segmentation is challenging. The Markov random field (MRF) model is promising for segmentation given some recent encouraging results. However, the traditional MRF model usually relies on a single feature. In this paper, we first propose a novel Markov multiple feature random fields (MMFRF) model, which is able to combine various types of features into a unified decision model using the Bayesian framework. Second, we mainly focus our research on two feature random fields in the MMFRF model for the segmentation of brain MR images. The intensity feature random field and the texture feature random field are combined into a unified framework to model the image. In particular, we use patch-based 3-D texture features through gray-level co-occurrence matrix (GLCM) statistics to construct the texture feature random field. The performance of our proposed method is compared with some state-of-the-art approaches on both real and simulated brain MR datasets. The experimental results demonstrate that the performance of the proposed method is superior to the competing approaches. In theory, the traditional MRF model can be treated as a special case of the proposed general MMFRF model where only one feature random field is considered. Furthermore, the results also show the feasibility of employing the proposed method, which provides accurate and efficient brain tissue segmentation, to develop effective expert and intelligent systems for brain MR images.
Do user-defined gestures for flatscreens generalize to interactive spherical displays for adults and children?

Interactive spherical displays offer unique opportunities for engagement in public spaces. Research on flatscreen tabletop displays has mapped the gesture design space and compared gestures created by adults and children. However, it is not clear if the findings from these prior studies can be directly applied to spherical displays. To investigate this question, we conducted a user-defined gestures study to understand the gesture preferences of adults and children (ages 7 to 11) for spherical displays. We compare the physical characteristics of the gestures performed on the spherical display to gestures on tabletop displays from prior work. We found that the spherical form factor influenced users' gesture design decisions. For example, users were more likely to perform multi-finger or whole-handed gestures on the sphere than in prior work on tabletop displays. Our findings will inform the design of interactive applications for spherical displays.
An intuitionistic fuzzy neural network with gaussian membership function

In this study, an intuitionistic fuzzy neural network (IFNN) with Gaussian membership function and Yager-generating function is proposed. Since intuitionistic fuzzy logic (IFL) considers membership, non-membership and hesitation values simultaneously, the incorporation of the concept of IFL into a fuzzy neural network (FNN) can enhance the performance of an FNN. A back-propagation learning algorithm is developed to optimize the IFNN parameters and weights. The proposed IFNN is applied to ten problems, including nonlinear control and prediction problems. The computational results indicate that the proposed IFNN is more efficient than conventional algorithms, such as artificial neural networks (ANN), fuzzy neural networks (FNN), and a support vector regression (SVR).