Reviewerly/exHarmony huggingface dataset:
On-the-Fly Bayesian Data Assimilation Using Transport Map Sampling and PGD Reduced Models

The motivation of this research work is to address real-time sequential data assimilation and inference of model parameters within a full Bayesian formulation. For that purpose, we couple two advanced numerical approaches. First, the Transport Map sampling is used as an alternative to classical Markov Chain approaches in order to facilitate the sampling of posterior densities resulting from Bayesian inference. It builds a deterministic mapping, obtained from a minimization problem, between the posterior probability measure of interest and a simple reference measure. Second, the Proper Generalized Decomposition (PGD) is implemented in order to reduce the computational effort for the evaluation of the multi-parametric numerical model in the online phase, and therefore for uncertainty quantification on outputs of interest of the model. The PGD also speeds up the minimization algorithm of the Transport Map method, as derivatives with respect to model parameters can then be computed in a straightforward manner. The performance of the approach is illustrated on a fusion welding application.
Deep feature fusion for IRIS based on industrial biometric engineering

Students attendance is one of the primary and most vital things that is used in all the schools. Schools have numerous systems for taking attendance. Some of them use manual attendance, while some of them taking attendance by making use of the bar codes embedded in the student identity cards. Though various systems are been used still it needs ample development to make it more efficient with minimal time consumption. In this paper, we have proposed a model for taking the attendance of the students by making use of biometric attendance systems that are intended to make use of WFT (Wireless Fingerprint System). The system proposed in the paper makes use of the unique Aadhaar number that is given to all the citizens of India. The student biometric is matched with their respective Aadhaar number. By making use of the proposed system it is not only useful for attendance tracking but also useful for the government to provide scholarships or education fees waiver to the deserved students. The system makes use of local normalization, second stage enhancement and matching modules to make it more efficient when compared to other traditional attendance systems.
Intuitionistic Fuzzy Logic Controller Over the Irrigation Field to Optimized Water Utilization

The intuitionistic fuzzy set and intuitionistic fuzzy logic was introduced by Atanassov. It will give the very good output over the uncertainty because it contains membership and non-membership function. The intuitionistic fuzzy system provides a mechanism for communication between computer system and humans. In this work we describe that the development of an intuitionistic fuzzy logic controller control the motor switch on and off for irrigation. In irrigation field and well water level measured by the sensors. The intuitionistic fuzzy system and mamdami intuitionistic fuzzy rules and Takagi Sugni Formula for the defuzzification are used to get the crisp output from an intuitionistic fuzzy input. The well water level and water level in agriculture land are calculated by applied using intuitionistic fuzzy rules applied in an inference engine using defuzzification method.
A New Fast Matching Algorithm for Angle-Adaptive Grayscale Templates

Aiming at the problem that the existing gray-based template matching algorithm is not resistant to rotation and slow operation, combined with fast Fourier algorithm and image pyramid search strategy, a new fast matching algorithm for angle-adaptive grayscale templates was designed. Firstly, the image is preprocessed to reduce the noise interference during matching. Secondly, the multi-angle to-be-detected gallery is built by rotating the sub-image to be matched, which solves the problem of contour artifact caused by template rotation. Finally, the FFT-based NCC algorithm is used to measure the similarity. Combined with the image pyramid search strategy and the indented search step method, the computational complexity is reduced and the robustness is improved. And this algorithm is compared with the traditional anti-rotation algorithm during experiments. Experiments show that the proposed method achieves good results when the angle deviation is large and the image matching probability is low or mismatched.
The Effect of Outline to Improve Students Writing' Ability at the Second Year Students of SMAN 3 Mamuju

The Effects of Outline to Improve Students’ Writing Ability at the Second Year Students of SMAN 3 Mamuju. Outline is a useful tool to help writers get easier in developing their ideas. An outline can do two things: first, it can save a lot of rewriting and second, it allows writers to check quickly their paragraph’s unity. The aim of this research is to find out whether the use of outline can improve the students’ writing ability in a descriptive paragraph. The method used was true-experimental design. The subject of this research was class XI A students of SMAN 3 Mamuju. The data collected was from the test given to the students and was analyzed through quantitative method. To get the score, the researcher used scoring scale which includes the content, organization, vocabulary, language use and mechanics. The mean score, standard deviation, and t-test between the pre-test and post-test was calculated by using Jacob’s scoring rubric. The data analysis showed that the students’ got their mean score greater in the posttest than in the pretest, in which the mean score was 51.8 and 68.5. Moreover, the t-test value (25.064) was also higher than the t-table value (2.045), which is meant the treatment of outline strategy gave a significant improvement to the students’ writing ability of SMAN 3 Mamuju.
Acciones para la implementación en el sistema educativo cubano de tecnologías emergentes identificadas por el informe Horizon

This work is aimed at suggesting action to be taken to use some emerging technologies which may enhance the efficiency of the Cuban education system, and its appeal for teachers and students, so they may be more motivated. The Horizon Project reports were used as guidelines in studying trends in the usage of technology in education. It was examine how a developing and not technologically advanced country may benefit from today’s major innovations. It is claimed that teaching and learning environments may be significantly improved by using emerging technologies.
Non-parametric Method of Topic Identification Using Granularity Concept and Graph-Based Modeling

This paper aims to classify the large unstructured documents into different topics without involving huge computational power and a priori knowledge. The concept of granularity is employed here for extracting contextual information from the documents by generating granules of words (GoWs), hierarchically. In a novel computationally efficient w ay, the proposed Granularity Based Word Grouping (GBWG) algorithm exploits the co-occurrence between the words of different granules to group the contextually related words, representing different topics. However, the topics are coarsely generated, containing common words. A Graph-based method has been proposed to precisely identify the topics using the Louvain graph clustering algorithm. The algorithm clusters the words using mutual information as an association measure between the words of each granule. Each cluster containing unique words, represents different topics. Finally, a test document is classified into a particular topic, corresponding to the cluster having the maximum probability of belongings of the words in the test sequence. The Performance of the proposed method has been compared with other unsupervised, semi-supervised and supervised topic modeling algorithms and we demonstrate that the result is comparable or better than the state of the art algorithms.
Cross-Lingual Topic Prediction For Speech Using Translations

Given a large amount of unannotated speech in a low-resource language, can we classify the speech utterances by topicƒ We consider this question in the setting where a small amount of speech in the low-resource language is paired with text translations in a high-resource language. We develop an effective cross-lingual topic classifier by training on just 20 hours of translated speech, using a recent model for direct speech-to-text translation. While the translations are poor, they are still good enough to correctly classify the topic of 1-minute speech segments over 70% of the time—a 20% improvement over a majority-class baseline. Such a system could be useful for humanitarian applications like crisis response, where incoming speech in a foreign low-resource language must be quickly assessed for further action.
Collaborative Ranking Tags and Items via Cross-domain Recommendation

Tag and item recommendation are both important tasks in recommender systems. Item recommendation, learning user-item rating patterns, helps users to discover a list of items that are more likely to match their interests. On the other hand, tag recommendation, exploiting relationships of user-tag-item, assists users to find a personalized list of tags that can precisely describe their experience on items. Existing methods often consider these two tasks independently. However, these two tasks are inherently related because they both use information from same groups of users and items. In other words, it is the same users who give ratings and annotate tags to the same set of items. Tags may explain why a user purchase an item. For instance, customers on Yelp not only give a five star rating for a restaurant but also leave comments/tags to express their positive sentiments. Users with similar interests might give similar ratings and post similar tags to certain items. The co-occurrence of users, items and tags motivates us to jointly perform item and tag recommendation to better model users' behavior by leveraging their personalized items and tags. In this work, we propose a cross-domain learning method that seamlessly integrates tag and item recommendation into one unified framework. In particular, we utilize the knowledge transferred between two tasks to collaboratively rank items and tags for users through coupled tensor-matrix factorization. We also provide a principled way to incorporate additional auxiliary information about users (e.g., users' relationships in a social network) and items (e.g., descriptions of items in Wikipedia) to improve recommendation accuracy for both tasks. Empirical results demonstrate the effectiveness of our proposed model.
SCDP: Smart Contract-based Decentralized Privacy System for Securing Data Ownership Management

Secure data ownership management is significant for realizing personal and private data sharing, which can be widely used with consumer electronics. The notion of the decentralized privacy (DP) is introduced by Zyskind et al., and accordingly the first DP system is implemented through blockchain and off-blockchain distributed hashtable. To address the efficiency and overhead issues, we present a conceptually simple solution directly from smart contracts with cryptographic primitives. Our system is called the smart contract-based decentralized privacy (SCDP) system. We propose the basic SCDP system as a warm-up to introduce the design principle based on symmetric encryption. Moreover, the strong SCDP system is provided by using ciphertext-policy attribute-based encryption for supporting more flexible scenarios of access control and also eliminating some limitations of the basic system.
A Statistical Causal Inference Method for Exploring Ultrasonics and Topological Deformations in Biological Systems

Understanding deformations in biological systems (e.g., parietals of rhinolophus ferrumequinum) and their relations with ultrasonic functionalities is a fundamental science problem. It has promising potential of providing insightful guidance for design and manufacture of artificial sonars with promising potential. However, previous research encounters enormous real-world constraints, especially limited by environmental settings and availability of experimental animals. Moreover, existing works mainly focus on analyzing statistical correlations between geometric features and acoustic functionalities, and barely conduct causal inference, especially counterfactual reasoning, leading to a failure of in-depth understanding of the acoustic mechanism behind.
Developing and Fostering Smart Ecosystems

Chitkara University Research and Innovation Network in India, and Innopolis University in Tatarstan are two ecosystems, which have received wide attention due to their innovative ways of using new technologies and teaching methods to prepare students for higher education, future employment, and entrepreneurship. This chapter draws attention to the students who are located in a learning environment which is filled with multitude of learning practices. The study also elaborates on the challenges that graduates face in getting jobs and discusses perspectives of these two universities.
Structured Iterative Hard Thresholding for Categorical and Mixed Data Types

In many applications, data exists in a mixed data type format, i.e. a combination of nominal (categorical) and numericalal features. A common practice for working with categorical features is to use an encoding method to transform the discrete values into numeric representation. However, numeric representation often neglects the innate structures in categorical features, potentially degrading the performance of learning algorithms. Utilizing the numeric representation could also limit interpretation of the learned model, such as finding the most discriminative categorical features or filtering irrelevant attributes. In this work, we extend the iterative hard thresholding (IHT) algorithm to quantify the structure of categorical features. The empirical evaluation of the proposed structured hard thresholding algorithm is based on both real and synthetic data sets in comparison with the original hard thresholding algorithm, LASSO and Random Forest. The results demonstrate an improved performance over the original IHT.
Prediction of Echo from Noise Signals by Means of Nonlinear Transform of Signal Spectra

In this note we discuss the algorithm allowing to predict the time of occurrence of echo signals under multi-pulse noise excitation. The technique is verified by the experiments with 59Co samples.
Learning Temporal Relevance in Longitudinal Medical Notes

Clinical notes correspond to sequences of reports about patients written over time by health-care professionals. With the availability of massive Electronic Health Record (EHR) datasets composed of such clinical notes, machine learning models on these rich text data series are being developed for patient outcome prediction from infection diagnosis to mortality. While current models focus on content in these unstructured clinical notes, we postulate that timing of the medical events that are explained in the notes are equally crucial. We thus propose a novel attention mechanism composed of dual-attention blocks based on a rich diversity of time representations. We then pair this mechanism with an LSTM, resulting in our proposed time-aware recurrent network TEND-LSTM. TEND-LSTM learns an integrated set of attention weights, with the first attention based on the content of the clinical notes and the second based on when the notes were taken. Together, they are combined using a deep-attention network layer. The proposed dual attention mechanism not only learns a function of time incorporating different aspects of the temporal nature of note instances but also automatically finds a balance between how much attention to put on content versus time. We evaluate our model on six medical tasks using data sets from the publicly-available MIMIC III database from the Beth Israel Deaconess Medical Center. Our results demonstrate that TEND-LSTM outperforms state-of-the-art methods by a large margin.
Modeling Trajectories for 3D Motion Analysis

3D motion analysis by projecting trajectories on manifolds in a given video can be useful in different applications. In this work, we use two manifolds, Grassmann and Special Orthogonal group SO(3), to analyse accurately complex motions by projecting only skeleton data while dealing with rotation invariance. First, we project the skeleton sequence on the Grassmann manifold to model the human motion as a trajectory. Then, we introduce the second manifold SO(3) in order to consider the rotation that was ignored by the Grassmann manifold on the matched couples on this manifold. Our objective is to find the best weighted linear combination between distances in Grassmann and SO(3) manifolds according to the nature of the input motion. To validate the proposed 3D motion analysis method, we applied it in the framework of action recognition, re-identification and sport performance evaluation. Experiments on three public datasets for 3D human action recognition (G3D-Gaming, UTD-MHAD multimodal action and Florence3D-Action), on two public datasets for re-identification (IAS-Lab RGBD-ID and BIWI-Lab RGBD-ID) and on one recent dataset for throwing motion of handball players (H3DD), proved the effectiveness of the proposed method.
Information Theory-Based Curriculum Learning Factory to Optimize Training

We present a new system to optimize feature extraction from 2D-topological data like images in the context of deep learning using correlation among training samples and curriculum learning optimization (CLO). The system treats every sample as 2D random variable, where a pixel contained in the sample is modelled as an independent and identically distributed random variable (i.i.d) realization. With this modelling we utilize information-theoretic and statistical measures of random variables to rank individual training samples and relationship between samples to construct syllabus. The rank of each sample is then used when the sample is fed to the network during training. Comparative evaluation of multiple state-of-the-art networks, including, ResNet, GoogleNet, and VGG, on benchmark datasets demonstrate a syllabus that ranks samples using measures such as Joint Entropy between adjacent samples, can improve learning and significantly reduce the amount of training steps required to achieve desirable training accuracy. We present results that indicate our approach can produce robust feature maps that in turn contribute to reduction of loss by as much as factors of 9 compared to conventional, no-curriculum, training.
Incremental Lattice Determinization for WFST Decoders

We introduce a lattice determinization algorithm that can operate incrementally. That is, a word-level lattice can be generated for a partial utterance and then, once we have processed more audio, we can obtain a word-level lattice for the extended utterance without redoing all the work of lattice determinization. This is relevant for ASR decoders such as those used in Kaldi, which first generate a state-level lattice and then convert it to a word-level lattice using a determinization algorithm in a special semiring. Our incremental determinization algorithm is useful when word-level lattices are needed prior to the end of the utterance, and also reduces the latency due to determinization at the end of the utterance.
Big Data Processing

Big data has attracted significant and increasing attention recently and has become a hot topic in the areas of IT industry, finance, business, academia, and scientific research. In the digital world, the amount of generated data has increased. According to the research of International Data Corporation (IDC), 33 zettabytes of data were created in 2018, and it is estimated that the amount of data will scale up more than five times from 2018 to 2025. In addition, the advertising sector, healthcare industry, biomedical companies, private firms, and governmental agencies have to make many investments in the collection, aggregation, and sharing of enormous amounts of data. To process this large-scale data, specific data processing techniques are used rather than conventional methodologies. This chapter deals with the concepts, architectures, technologies, and techniques that process big data.
Similarity Analysis of Residual Frames for Inter Frame Forgery Detection in Video

The credibility of digital videos obtained from various sources like surveillance cameras, smartphones, webcams used as evidence in courtrooms, medical world, and journalism needs to be verified for its authenticity and integrity to detect video forgery. The proposed forensic approach detects inter frame forgery due to frame duplication based on statistical correlation for the digital video. The statistical correlation in video content of the frames has been explored through textural features to identify frame duplication and is verified. Experimental results demonstrate accuracy in identifying frame replication.
Mitigating Messaging and Processing Load in IoT Environments Managed by Blockchain

We study the use of Blockchain in an Internet of Things (IoT) environment where microservices are provided by sensors. Blockchain assures secure and trustworthy transactions, yet it causes high load caused by block creation on the numerous IoT nodes. To mitigate the load, sensor nodes are clustered and block creation is restricted to IoT cluster managers in the distributed Blockchain ledger. The concept is exemplified by a use case describing a neighborhood of smart homes that are equipped with environmental sensors such as dust, temperature, and precipitation sensors. We provide an implementation of the Blockchain IoT system comprising two consensus algorithms, Proof of Work (PoW) and Proof of Stake (PoS). By means of simulations conducted in Mininet, we derive first results which reveal that -- depending on the sensor polling period -- the service response time can be reduced by clustering. Further, clustering reduces PoW conflicts to about 30% due to creating fewer blocks.
Implementing Cryptography Pairings over Ordinary Pairing-Friendly Curves of Type $$ y^2 = x^5 +a \, x$$

In this paper, we describe an efficient implementation in Sage of the Tate pairing over ordinary hyperelliptic curves of type $$ y^2 = x^5 +a \, x$$. First, we describe a method of construction of these curves according to Kawazoe and Takahashi [8]. Then, we describe an efficient formula for computing pairings on such curves over prime fields, and develop algorithms to compute Tate pairing. We provide a faster optimisation of the final exponentiation in particular for the embedding degree $$k = 28$$.
Comment Evaluation using Mutual Method, LSTM, Bootstrap and Co-occurrence word in Lecture Questionnaire

Many universities use use questionnaires for course evaluations. Such questionnaires vary by type and analysis method. In questionnaires, free description components are recognized as an important method of obtaining a wide range of opinions. We propose two methods to evaluate free description comments. The first method is mutual evaluation between comments and words in relation to the comment. The second method uses Long Short-Term Memory (LSTM). In addition, we use the free description comment evaluations and the evaluation of co-occurring word pairs based on comment evaluation.