Reviewerly/exHarmony huggingface dataset:
Decision Support System for Classification of Early Childhood Diseases Using Principal Component Analysis and K-Nearest Neighbors Classifier

Background: Data on early childhood disease collected in clinics has accumulated into big data. Those data can be used for classification of early childhood diseases to help medical staff in diagnosing diseases that attack early childhoods.Objective: This study aims to apply Principal Component Analysis (PCA) and K-Nearest Neighbor (K-NN) Classifier for the classification of early childhood diseases.Methods: Data analysis was performed using PCA to obtain variables that had a major influence on the classification of early childhood diseases. PCA was done by observing the correlation between variables and eliminating variables that have little influence on classification. Furthermore, data on early childhood disease was classified using the K-Nearest Neighbor Classifier method.Results: The results of system evaluation using 150 test data indicated that the classification system by applying PCA and KNN Classifier had an accuracy value of 86%.Conclusion: PCA can be used to reduce the number of variables involved so that it can improve system performance in terms of efficiency. In addition, the application of PCA and KNN can also improve accuracy in the classification of early childhood diseases.
Research on the English Teaching Resource Library Management System Based on the Computer Technology

With the continuous development of the computer network technology and the network education, colleges and universities have gradually carried out the construction of the college English multimedia network teaching resource library. The construction of the English teaching resource library management system based on the computer technology is the core of the network teaching. The management system of the English teaching resource library based on the computer technology should integrate the elements of the English teaching into a whole, so that it has the functions of the teaching content publishing and management, the online teaching interaction, the online teaching evaluation, the collaborative learning and the developmental teaching evaluation. According to the characteristics of the teaching objectives, the construction of the resource library should establish a multi-level, multi-functional and multi-module resource system.
Game-Based Security Proofs for Secret Sharing Schemes

Secret sharing schemes allow the secret to be shared among a group of parties, so that a quorum of these parties can work together to recover the secret, but less number of parties cannot learn any information of the secret. In the literature, secret sharing schemes are normally analysed using heuristic arguments rather than strict security proofs. However, such a method may overlook some security flaws, especially when it is used to analyse the secrecy property. In this paper, we illustrate this issue using some concrete examples. We show that in two existing secret sharing schemes, the secrecy property was originally conjectured to be satisfied, but the adversary still can employ some security flaws to violate this property. We then introduce a game-based model that can be used to formally analyse the secrecy property in secret sharing schemes. We prove that our model captures the definition of the secrecy property. And as an example, we show how our method can be used to analyse Shamir secret sharing scheme. Note that our method might find applications in other secret sharing schemes as well.
Gradient tree boosting with random output projections for multi-label classification and multi-output regression

In many applications of supervised learning, multiple classification or regression outputs have to be predicted jointly. We consider several extensions of gradient boosting to address such problems. We first propose a straightforward adaptation of gradient boosting exploiting multiple output regression trees as base learners. We then argue that this method is only expected to be optimal when the outputs are fully correlated, as it forces the partitioning induced by the tree base learners to be shared by all outputs. We then propose a novel extension of gradient tree boosting to specifically address this issue. At each iteration of this new method, a regression tree structure is grown to fit a single random projection of the current residuals and the predictions of this tree are fitted linearly to the current residuals of all the outputs, independently. Because of this linear fit, the method can adapt automatically to any output correlation structure. Extensive experiments are conducted with this method, as well as other algorithmic variants, on several artificial and real problems. Randomly projecting the output space is shown to provide a better adaptation to different output correlation patterns and is therefore competitive with the best of the other methods in most settings. Thanks to model sharing, the convergence speed is also improved, reducing the computing times (or the complexity of the model) to reach a specific accuracy.
Intervención cognitivo-conductual en un caso de ansiedad ante la lectura en público: mejora de la fluidez y comprensión

Cognitive-behavioral intervention in a case of anxiety about public reading: Improvement of fluency and comprehension Intervención cognitivo-conductual en un caso de ansiedad ante la
An Efficient Broadcast Encryption Supporting Designation and Revocation Mechanisms

In this paper our objective is to explore approaches of secure group-oriented communication with designation and revocation mechanisms simultaneously. We present a new scheme of Revocation-based broadcast encryption (RBBE) which is designed on Dan Boneh et al.'s scheme with the designation mechanism proposed in 2005. We combine two above-mentioned schemes into a new cryptosystem, called Dual-mode broadcast encryption (DMBE). Based on these work, we reach the following conclusions. We use the DMBE scheme as an example to show that it is feasible to construct a broadcast encryption scheme that supports designation and revocation mechanisms simultaneously. The cryptosystem with dual modes is more e.cient than that with single mode over computational costs, and the performance is improved to at most O(⌈N/2⌉), where N is the total number of users in the system. We prove completely that both the RBBE scheme and the DMBE scheme are semantically secure against chosen plaintext attack with full collusion under the decisional bilinear Di.e-Hellman exponent assumption.
Directional Multiscale Feature Extraction for Biomedical Image Indexing and Retrieval Using Contourlet Transform

Multiscale and multidirectional are desirable properties for image decomposition. This paper presents directional multiscale feature extraction for biomedical image indexing and retrieval using contourlet transform. The contourlet transform decomposed image at multiscale in directional sub-bands. The feature vector of each contourlet sub-band is calculated by computing the directional energies of the extracted coefficients of respective sub-band. The final feature vector is constructed by concatenating feature vectors of all contourlet sub-bands. The similarity between query feature vector and feature vector of database is calculated using Manhattan distance. The feature extraction time and feature vector length are reduced significantly using proposed method. The effectiveness of the proposed method is evaluated by conducting the experiments on two well-known biomedical databases: Open access series of imaging studies (OASIS) MRI and NEMA-CT. The average retrieval precision (ARP) and average retrieval rate (ARR) are used to measure the performance of the proposed method. The experimental results show that the proposed method outperforms well-known existing methods.
Istex: A Database of Twenty Million Scientific Papers with a Mining Tool Which Uses Named Entities

Istex is a database of twenty million full text scientific papers bought by the French Government for the use of academic libraries. Papers are usually searched for by the title, authors, keywords or possibly the abstract. To authorize new types of queries of Istex, we implemented a system of named entity recognition on all papers and we offer users the possibility to run searches on these entities. After the presentation of the French Istex project, we detail in this paper the named entity recognition with CasEN, a cascade of graphs, implemented on the Unitex Software. CasEN exists in French, but not in English. The first challenge was to build a new cascade in a short time. The results of its evaluation showed a good Precision measure, even if the Recall was not very good. The Precision was very important for this project to ensure it did not return unwanted papers by a query. The second challenge was the implementation of Unitex to parse around twenty millions of documents. We used a dockerized application. Finally, we explain also how to query the resulting Named entities in the Istex website.
Continual Learning in Deep Neural Network by Using a Kalman Optimiser

Learning and adapting to new distributions or learning new tasks sequentially without forgetting the previously learned knowledge is a challenging phenomenon in continual learning models. Most of the conventional deep learning models are not capable of learning new tasks sequentially in one model without forgetting the previously learned ones. We address this issue by using a Kalman Optimiser. The Kalman Optimiser divides the neural network into two parts: the long-term and short-term memory units. The long-term memory unit is used to remember the learned tasks and the short-term memory unit is to adapt to the new task. We have evaluated our method on MNIST, CIFAR10, CIFAR100 datasets and compare our results with state-of-the-art baseline models. The results show that our approach enables the model to continually learn and adapt to the new changes without forgetting the previously learned tasks.
Play Poles

This pictorial reports on the Play Poles prototype that was designed as part of a design ethnography investigating the Internet of Things (IoT) as a resource supporting outdoor play amongst groups of children. We use illustrations and annotations derived from video data and analysis to depict gestures, actions and social interaction that are significant in understanding the qualities of the Poles as a play resource. We argue that simple functions and direct, real-time control can be used by groups of children to support fun and creativity in outdoor play, whilst also highlighting opportunities and challenges in designing IoT play resources.
Generalized Linear Rule Models

This paper considers generalized linear models using rule-based features, also referred to as rule ensembles, for regression and probabilistic classification. Rules facilitate model interpretation while also capturing nonlinear dependences and interactions. Our problem formulation accordingly trades off rule set complexity and prediction accuracy. Column generation is used to optimize over an exponentially large space of rules without pre-generating a large subset of candidates or greedily boosting rules one by one. The column generation subproblem is solved using either integer programming or a heuristic optimizing the same objective. In experiments involving logistic and linear regression, the proposed methods obtain better accuracy-complexity trade-offs than existing rule ensemble algorithms. At one end of the trade-off, the methods are competitive with less interpretable benchmark models.
A Blueprint for Preserving Virtual World Cultural Heritage using Preservica &amp;amp; Custom Metadata Schema

Faced with the possibility of running out of storage space and losing important born-digital cultural heritage artifacts, we are embarking on a project to design a process for preserving objects generated by our virtual world-based group so that these issues will not become critical in the future. Because of the highturnover nature of student-based groups, preservation of objects associated with annual and learning events is essential for continuity in the community. We present our blueprint for preserving these artifacts using a combination of an established archiving system together with a customized metadata schema that reflects the specific needs of VCARA and an easy to use interface for browsing collections.
Audio Compression Using DWT and RLE Techniques

The growth of the cellular technology and wireless networks all over the world has increased the demand for digital information by manifold. This massive demand poses difficulties in handling huge amounts of data that need to be stored and transferred. To overcome this problem we can compress the information by removing the redundancies present in it. Redundancies are the major source of generating errors and noisy signals. Coding in MATLAB helps in analyzing compression of audio signals with varying bit rate and remove errors and noisy signals from the audio signals. The audio signalâs bit rate can also be reduced to remove errors and noisy signals which are suitable for remote broadcast lines, studio links, satellite transmission of high quality audio and voice over internet. This paper focuses on the audio compression process and its analysis using DWT and RLE Techniques through MATLAB by which processed audio signal can be heard with clarity and in the noiseless mode at the receiver end.
A Fitness-Based Adaptive Synchronous-Asynchronous Switching in Simulated Kalman Filter Optimizer

Simulated Kalman Filter (SKF) is a population-based optimizer introduced in 2015 that is based on Kalman filtering, which consists of prediction, measurement, and estimation processes. The original SKF algorithm employs synchronous update mechanism in which the agents in SKF update their solutions after all fitness calculations, prediction process, and measurement process are completed. An alternative to synchronous update is asynchronous update. In asynchronous update, only one agent does fitness calculation, prediction, measurement, and estimation processes at one time. In this study, synchronous and asynchronous mechanisms are combined in SKF. At first, the SKF starts with synchronous update. If no improved solution is found, the SKF changes its update mechanism. Using the CEC2014 benchmark test suite, experimental results indicate that the proposed adaptive switching synchronous-asynchronous SKF outperforms the original SKF significantly.
An Unified Approach on Constructing of MDS Self-dual Codes via Reed-Solomon Codes

Based on the fundamental results on MDS self-dual codes over finite fields constructed via generalized Reed-Solomon codes \cite{JX} and extended generalized Reed-Solomon codes \cite{Yan}, many series of MDS self-dual codes with different length have been obtained recently by a variety of constructions and individual computations. In this paper, we present an unified approach to get several previous results with concise statements and simplified proofs, and some new constructions on MDS self-dual codes. In the conclusion section we raise two open problems.
A Class of Linear Codes with Three and Five Weights

Linear codes have been an interesting topic in both theory and practice for many years. Based on the theory of defining set, a class of three-weight and five-weight linear codes over prime field Fp are constructed, which is an extension of Wang et al.'s construction. Our construction include some optimal codes and almost optimal codes with respect to the Singleton bounds. We employ exponential sums to investigate the weight distributions of these linear codes. The results show that these codes can be used to construct secret sharing schemes.
Ontology of Ubiquitous Learning: WhatsApp Messenger Competes Successfully with Learning Management Systems (LMS)

The purpose for this research was to add to the body of research and further study how mobile learning can help to remedy the limitations e-learning poses for students who live in the sub-Saharan Africa Region with lack of access to electrical power and internet connectivity issues. Qualitative approach was employed with a total sample size of 807 students, composed of 58% male and 42% female. In this study, I made several assertions that, for WhatsApp to work properly in any classroom in sub-Saharan Africa, there must be intentional designs and step-by-step approach to teach both the faculty and the students how to use the application to achieve the utmost outcomes. I, therefore, concluded that using WhatsApp Messenger in a blended mobile learning context can help resolve many of the contextual difficulties that plague students in an e-learning situation.
ESA: Entity Summarization with Attention

Entity summarization aims at creating brief but informative descriptions of entities from knowledge graphs. While previous work mostly focused on traditional techniques such as clustering algorithms and graph models, we ask how to apply deep learning methods into this task. In this paper we propose ESA, a neural network with supervised attention mechanisms for entity summarization. Specifically, we calculate attention weights for facts in each entity, and rank facts to generate reliable summaries. We explore techniques to solve difficult learning problems presented by the ESA, and demonstrate the effectiveness of our model in comparison with the state-of-the-art methods. Experimental results show that our model improves the quality of the entity summaries in both F-measure and MAP.
A Methodology for Automated Facial Expression Recognition Using Facial Landmarks

Facial expression recognition is a crucial part of Psychology as a person's facial expression accounts for 55 percent of the effect of a spoken message.This makes facial expression the single biggest indicator of individual communication.Traditionally, Psychologists trained human observers to identify changes in facial muscles and use a Facial Action Coding System to map muscle movements to an emotion.Though this system helped ensure objectivity and had descriptive power, its major drawback was in effectively training human observers.With the advent of faster computers and the use of pixels/megapixels for picture elements, machine learning researchers became interested in automating facial expression recognition.Most researchers continued adopting the same Facial Action Coding System, used in Psychology, to train their statistical models.Though advances have been made in automating facial detection and finding facial landmarks, facial expression recognition results have stagnated.This stagnation is blamed on lack of training data and the difficulty of training a model to recognize subtle changes in facial muscles.In this paper the authors describe how software engineering best practices assisted in developing and implementing a methodology to leverage larger facial detection and facial landmarking datasets, as well as their improved accuracy over the Facial Action Coding System.This methodology is potentially more descriptive of faces in unconstrained environments.The authors present the software artifacts, the methodology, and the findings of a comparative study.
The Effects of Viewing Natural Scenery Indoors on Middle School Students’ Changes in Mood States and Concentration

The purpose of this study was to investigate the mood states and concentration of students when they see the natural scenery indoors throughout the Free Semester Program (FSP) of middle school.The subjects were total 180 students comprised of 60 students in the group viewing natural scenery, 60 students in the group viewing urban scenery and 60 students in the control group from the first year of middle school located in Daejeon, and the experiment was carried out from September 3 to October 30, in 2018.The results showed that tension, depression, anger, fatigue and confusion were decreased and vigor was increased significantly in group viewing natural scenery compared to group viewing urban scenery and the control group.Ratings of 'pleasant,' 'natural,' and 'calm' were increased and concentration was also improved significantly in the group viewing natural scenery compared to the group viewing urban scenery and the control group.This indicates that positive effects can be anticipated on the improvement of mood states and concentration by developing indoor program viewing natural scenery for people who have difficulties in outdoor activities.
Measuring Dynamic Correlations of Words in Written Texts with an Autocorrelation Function

In this study, we regard written texts as time series data and try to investigate dynamic correlations of word occurrences by utilizing an autocorrelation function (ACF).After defining appropriate formula for the ACF that is suitable for expressing the dynamic correlations of words, we use the formula to calculate ACFs for frequent words in 12 books.The ACFs obtained can be classified into two groups: One group of ACFs shows dynamic correlations, with these ACFs well described by a modified Kohlrausch-Williams-Watts (KWW) function; the other group of ACFs shows no correlations, with these ACFs fitted by a simple stepdown function.A word having the former ACF is called a Type-I word and a word with the latter ACF is called a Type-II word.It is also shown that the ACFs of Type-II words can be derived theoretically by assuming that the stochastic process governing word occurrence is a homogeneous Poisson point process.Based on the fitting of the ACFs by KWW and stepdown functions, we propose a measure of word importance which expresses the extent to which a word is important in a particular text.The validity of the measure is confirmed by using the Kleinburg's burst detection algorithm.
Scalable Collapsed Inference for High-Dimensional Topic Models

Rashidul Islam, James Foulds. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
Universities in the Knowledge Society: The Problem of Creativity Institutionalization

The problem of creativity institutionalization at the university entails an identification and building a model of interrelated socio-epistemic structures (both internal and external), functionally ensuring creative activities of a heterogeneous subject of cognition in line with the university’s academic missions (educational, research, socio-economic). The paper gives a socio-philosophical analysis of transformation of the creative-type cognitive relationship (attitude) in the process of University 3.0 historical development. The author classifies the approaches to the definition of creative spaces and outlines the main provisions of the author’s concept of creativity institutionalization in a modern university. It is shown that the creative function of a modern university develops under the influence of public expectations, economic conditions and new technologies that call for effective educational environments, innovative learning methods, new forms of literacy. The author distinguishes four historical stages in the University 2.0, which differ in types of creative activity of a subject of cognition with respect to learning and research: education as the comprehension of truth, conjunction of research and teaching, conjunction of research and learning, learning through scientific researches. It is shown that University 3.0 takes its origin at the end of the 20th century as a result of commercialization of scientific researches, where scientific- and socialentrepreneurial creativities are added to various types of educational and scientific-research creativity. The paper presents the approaches to conceptualization of creative spaces at the University: environment model, model of cognitive processes, complex “environment as a mode of cognition” model. The concept of generativity is a core element of the author’s model of creative space, which is applied as a social-epistemological characteristic to the processes of learning and the environment of cognition. The creative space is deemed as a cognitive-generative system that interconnects creative-type cognitive processes with the cognitively active environment.