Reviewerly/exHarmony huggingface dataset:
Developing Multilingual Automatic Semantic Annotation Systems

We report the development of a multilingual system for the semantic analysis of text. The research on the English Semantic Tagger started in 1990, and after that the system has been ported, first, to Finnish and Russian, and, thereafter, to Arabic, Chinese, Czech, Dutch, French, Italian, Malay, Portuguese, Spanish, Urdu, and Welsh. The development processes of the semantic taggers for English, Finnish, and Russian were relatively similar, involving manual construction of the semantic lexicons, whereas, to speed up the research, new bootstrapping methods including computational approaches have been utilised later in the creation of the semantic lexicons for the other languages. We describe these manual and automatic processes as well as envisaging directions for future development. The resulting multilingual framework of semantic taggers based on equivalent semantic lexicons and one common semantic taxonomy offers a wealth of potential applications which this chapter also illustrates. In addition to developing monolingual applications for these semantic taggers, it is also possible to create cross-lingual and multilingual applications. Furthermore, while the existing semantic analysis systems are designed for the analysis of general language, such systems can also be tailored for a specific purpose to deal more accurately with only one particular domain or task.
Ensuring Security &amp; Trust in a Post-Quantum Environment

The wisdom of humanity is growing faster than ever. So too is the evolution of technologies in general. The benefits and consequences of innovations are increasingly impacting daily life. Security, decision-making, and trust are no exceptions to this development. Twenty-five years ago, the work of Shor demonstrated that the security of well-established technologies will be at risk as soon as quantum computing becomes a reality. This challenge is being partly addressed by the quest for ‘‘post-quantum’’ cryptographic techniques while, in part, it can be mitigated by preparing for agility when it comes to implementing cryptography. The focus of our paper is to develop alternative visions and needs for future research and development in order to ensure security and trust in a post-quantum environment.
AHMM Based Speech Synthesis Method Using Articulatory Feature

In corpus-based Chinese concatenative speech synthesis, a new method based on articulatory feature was proposed for the problem of unnatural transitions due to co-articulation at the units concatenation points, and a corpus containing articulatory feature was constructed. Then the Hidden Markov Model was used to predict the articulatory feature, and the units selection methods were designed based on articulatory feature. Experiments show that speech synthesis based on articulatory feature can better deal with co-articulation at concatenation points than the traditional context-based method, making the evaluation of synthesized speech improved.
Scalable Weak Constraint Gaussian Processes

A Weak Constraint Gaussian Process (WCGP) model is presented to integrate noisy inputs into the classical Gaussian Process predictive distribution. This follows a Data Assimilation approach i.e. by considering information provided by observed values of a noisy input in a time window. Due to the increased number of states processed from real applications and the time complexity of GP algorithms, the problem mandates a solution in a high performance computing environment. In this paper, parallelism is explored by defining the parallel WCGP model based on domain decomposition. Both a mathematical formulation of the model and a parallel algorithm are provided. We prove that the parallel implementation preserves the accuracy of the sequential one. The algorithm’s scalability is further proved to be $$\mathcal {O}(p^2)$$ where p is the number of processors.
Zeroing Neural Network Based on the Equation AXA = A

Zeroing Neural Network (ZNN) design arising from different error monitoring functions (or Zeroing functions) defined on the basis of Penrose matrix equations are considered. New Zeroing function based on the Penrose equation AXA = A and initiated ZNN design for computing the time-varying pseudoinverse are defined and investigated. Also, an explicit form of defined model is proposed. Illustrative simulation results are given to verify theoretical results.
Improvement of the Communicator's Performance on the Emirati Satellite Channels a Comparative Study between the Public Female Audience Attitudes and the Female Journalists 2018

The remarkable progress in communication technology, satellites and the available technical and productive techniques provides recipients with the possibility of following these satellite channels and receiving them without time and place constants. This has contributed the increasing rate of acquisition and exposure to their messages. In recent years, the UAE and Arab viewers have become targets of these channels, by providing them with diverse programs.
Speaking-Rate Adaptation of Automatic Speech Recognition System through Fuzzy Classification based Time-Scale Modification

In this paper, we study the role of speaking-rate adaptation (SRA) of automatic speech recognition (ASR) systems. The performance of an ASR system is reported to degrade when the speaking-rate is either too fast or too slow. In order to simulate such a situation, an ASR system was trained on adults' speech and used for transcribing speech data from adult as well as child speakers. Earlier studies have shown that, speaking-rate is significantly lower in the case of children when compared to adults. Consequently, the recognition performance for children's speech was noted to be very poor in contrast to adults' speech. To improve the recognition performance with respect to children's speech, speaking-rate was explicitly changed using time-scale modification (TSM). A recently proposed TSM approach based on fuzzy classification of spectral bins has been explored in this regard. The fuzzy-classification-based TSM technique is reported to be superior to state-of-the-art approaches. Effectiveness of the said TSM technique has not been studied yet in the context of ASR. The experimental studies presented in this paper show that SRA based on fuzzy classification results in a relative improvement of 30% over the baseline.
Proposal of Recollection Support System After Motorcycle Touring with Eye Tracking

A lifelog is a digital record of human life and behavior. In recent years, with the development of wearable devices, it has become possible to record personal experiences for long periods. Future devices might record long lifelogs without imposing a heavy burden. Nevertheless, an important difficulty is that reviewing data recorded over a long period takes much time. Therefore, for this study, we aim to create a system that summarizes a lifelog video automatically for easy review. As described herein, we specifically emphasized eye tracking and examined which of three methods is most effective for video summarization: pupil diameter, saccade, or saliency map.
Learning Experiences in Real Life

In today’s digital world, the usefulness, relevance, and real-world experiencesReal-world experiences” \b \i must inform each dimension of a learning experience. When a student is involved or engaged in experiential learning, they learn to solve the kinds of complex, tricky problems that not always have the clear answers, just like life. This chapter discusses the ways to optimize the learning experience through the use of technologies, making lesson planning to be inspired from imagination, creativity, and the pursuit of knowledge. By connecting the classrooms to the real world using technology, instructors can validate the relevance of their subjects and give students a solid vision of what’s possible. The chapter also presents the background information, learning structures taken from various sources, and the application of real-life learning experienceReal-life learning experience” \b \i , while throwing light on the differences between historic and contemporary learning.
Trust Computation Framework based on User Behavior and Recommendation in Cloud Computing

Cloud computing provides shared environment for different resources and services that are available for users at anytime and from anywhere. Cloud computing has gained considerable attention of users and businesses. However, security concern is one of the major hurdles for acceptance of cloud computing. In order to guarantee security of data, it is necessary to grant access of data, only to authorized users. The traditional system applies different access policies and permission while granting access to any user. The analysis of user behavior is also important aspect, which can be integrated into access control model. In this paper, the trust computation model is presented that takes user behavior into consideration while providing access to the cloud data. The recommendation for the user is also one of the important components to assess user behavior. The proposed model evaluates trustworthiness of user on basis of reputation and recommendation. With the advent in machine learning techniques, applying learning based techniques in security domain has gained lots of popularity. In the proposed method, the machine learning technique (k-means clustering Algorithm) is incorporated in the trust computation process and the users are classified according their trust values.
Research and comparative analysis of algorithms of asymmetric encryption

Using a developed software product, a comparative analysis of asymmetric text data encryption algorithms, their advantages and disadvantages, cryptographic strength, experimental evaluation of their characteristics with respect to the efficiency of their computer memory usage, the duration of key generation, data encryption and decryption ability of algorithms, key dimensions, volumes of encrypted and decrypted files has been made. Based on the results obtained, recommendations were given on the use of the encryption methods considered. The RSA and ElGamal algorithms are chosen as asymmetric encryption algorithms.From the point of view of practical implementation, both in software and hardware, there is no tangible difference between these two methods. However, in their characteristics they are noticeably different. Briefly, the main estimated results obtained are as follows: the cryptographic strength of the ElGamal algorithm is significantly higher than that of the RSA algorithm; the RSA algorithm has a higher speed when encrypting information, and the ElGamal algorithm has the best during decryption; as the key sizes increase, the decryption time by the RSA algorithm grows exponentially, while the duration of the decryption by the ElGamal algorithm has a linear growth order; the RSA algorithm showed 2 times better bandwidth than the ElGamal algorithm in the process of encoding information, but the ElGamal algorithm proved 10 times better throughput compared to the RSA algorithm in the process of decrypting information; the length of the encrypted data by the ElGamal algorithm is 2 times longer than the original data, while the size of the data encrypted by the RSA algorithm is larger than the size of the original data by an average of 1.4; for all key lengths, the ElGamal algorithm creates a pair of public and private keys on average 10 times faster than the RSA algorithm, which is especially noticeable with a significant increase in key sizes; for the ElGamal algorithm, the key generation time increases linearly with increasing key sizes, while for the RSA algorithm it grows exponentially.
Current Problems of Control and Evaluation of Creative Works of Younger Schoolchildren

The article discusses the features of the control over the implementation of creative works in primary school, discusses the problems of their evaluation. The potential of the Russian language lessons is revealed for the development of the skills of younger schoolchildren to build a coherent utterance, to develop their writing skills, as well as to develop skills of self-control and self-esteem in the process of learning to write presentations and mini-essays. It offers practical material of a creative nature, contributing to the improvement of the language culture of the younger schoolchildren.
Telegenic Data Analysis for Compact Description and Genre Classification based on Re-ranking

Extractive summarization aims to shorten textual information to create short summary by selecting sentences enriched with information. Ranking of sentences based on the previously selected sentences can assist in generating summaries for data analysis. In proposed research paper, a policy gradient-based reinforcement learning approach has been discussed to re-rank sentences by selecting high score sentence. Experiments have been conducted on TREC video dataset of rushes video and ROUGE evaluation metric clearly depicts the better performance of the reinforcement model with Rouge score of 74.39%. After generating compact telegenic descriptions, videos are classified into seven categories including: news, action, outdoor/indoor scene setting, close-up, grouping, traffic and meeting using Random Forest classifier with accuracy of 95.73%.
Gaussian Process Dynamical Autoencoder Model

Dimension reduction realize extraction of substantial low dimensional latent structure in high-dimensional data. Due to recent developments in information and measurement technology, it becomes more important to develop dimension reduction algorithms for high dimensional time series data. Gaussian process dynamic model (GPDM) is a method that can obtain low dimensional latent variable representation by using Gaussian process state space model. However, it is difficult to obtain an appropriate latent variable representation of new data point in the GPDM. In this study, we propose a Gaussian Process dynamic autoencoder model (GPDAEM), which consists of Gaussian process state space model and Gaussian process encoder model, in order to estimate appropriate latent variables corresponding to additional new time series data. Experimental results on low dimensional latent variable representation of time series data show that the proposed GPDAEM has better performance than the existing Gaussian process based latent variable models.
Classification tide levels in Semarang City use support vector machine

Abstract As the capital of Central Java, Semarang is a coastal town located on the north coast of Java. This geographical feature exposes the city to environmental problems such as tidal flood (locally known as rob). Therefore, it is high at stake for the city to classify a certain model to determine the high of tide. One of the expert classification methods of machine-based learning is support vector machine (SVM), it is classified as the nonparametric machine which does not require any assumptions. The classification using SVM requires a kernel as a weight to determine support vector data to classify. Therefore, this study uses Kernel of polynomial and radial base function. As for variables of tidal classification, this study used wind speed and rainfall. On the basis of the analysis, the maximum tide level classification accuracy was carried out on the distribution of 80: 20 training and testing data resulting in a classification accuracy of 69.42%. Classification accuracy was determined by the distribution of training and testing data.
Effects of Information Security Legitimacy on Data Breach Consequences

With legitimacy as a theoretical basis, we argue that firms with higher information security legitimacy draw less firm-specific risk at the stock market. Firms gain information security legitimacy by ensuring that their security practices conform to stakeholder expectations and by actively advertising these efforts. Impression management, by means of voluntary disclosure in case of a data breach, helps firms mitigate financial repercussions on the stock price. We test hypotheses with analyses of stock prices following data breach incidents reported in the media for 150 firms over an eighteen-year period.
Enhancing detail of 3D terrain models using GAN

The paper addresses the problem of low quality 3D terrain models enhancement. We propose the approach based on convolutional neural networks (CNN), namely, on Pix2Pix method that uses generative adversarial networks for imageto-image translation. We use heightmap 3D terrain models representation to use classical CNNs. The network was trained on a synthetic dataset that included 150000 images and heightmaps of different landscapes. Our model showed the relative mean absolute difference equal to 0.459% on synthetic testing dataset. In addition, we demonstrate landscapes generation on the real data from Google Maps using our model.
Quantitative Analysis of Software Component’s Reusability

This paper aims at nurturing the component-based development paradigm by quantitatively analyzing the component’s reusability. The study intends to resolve the issues revolving around compatibility of the units to be incorporated. With the help of case study of open source software, multiple versions are studied. Metrics are one of the methods to analyze the growth of software quantitatively. A metric suite has been proposed for this purpose. These metrics can be helpful in making our decision regarding whether any new component can be deployed merely or former component can be easily removed. These metrics contribute to check the reusability properties of the component.
Survival function model estimation for parkinson disease using independent metropolis-hastings algorithm with uniform proposal distribution in bayesian inference

In medicine study, one of the things that is interesting enough to be studied is "time-to-event". In general, time-to-event is used in doing survival analysis, such as analysis of Parkinson disease. Parkinson disease is one of the diseases which affects dopamine producer in brain area that is called by substantia nigra. The symptom of Parkinson disease is measured specifically by stages that are called by Hoehn and Yahr stages. This stages are distributed on integers between 0 to 5 with stage 0 is stage that does not have big impact and stage 5 is the most severe level. In this study, the survival function will be constructed from the time that the patient has the Hoehn and Yahr stages at A until increase to stage B with A < B. With A = 1, 2 and B = 3, 4, 5, overall it will be estimated six graphs of survival function. The process of construction survival function is using the Independent Metropolis-Hastings algorithm in Markov Chain Monte Carlo Methods on Bayesian Inference with uniform proposal distribution and the results are compared with Kaplan-Meier estimator for survival function. The result that is obtained through this algorithm is more represents the actual survival function if it is compared with Kaplan-Meier estimator, although there are so many censored data in the dataset.
Visual Development of Chinese Children, Studied with Eye-Tracking Technology

Visual ability is important for the development of human intelligence and life abilities. In the process of receiving external information, the highest proportion comes through vision, up to 83 percent. Thus the healthy development of visual ability is crucial. Childhood is the main period for visual development. Eye-tracking technology can be used to study visual development in children. We therefore designed a complete experimental procedure and conducted visual developmental detection experiments on five children randomly selected from a Shanghai kindergarten. We investigated the eye data for each child experimentally. The parameters obtained from experimental data include saccadic times (ST), fixation points (FP), maximum fixation duration (MD), the highest incidence of sights passing the same point (NP), and the maximum number of fixations (MF). The parameters of the five children are scored and compared, and the results indicate that our experiments can analyze children’s visual behavior and study children’s visual development.
Fine-Grained Label Learning via Siamese Network for Cross-modal Information Retrieval

Cross-modal information retrieval aims to search for semantically relevant data from various modalities when given a query from one modality. For text-image retrieval, a common solution is to map texts and images into a common semantic space and measure their similarity directly. Both the positive and negative examples are used for common semantic space learning. Existing work treats the positive/negative text-image pairs as equally positive/negative. However, we observe that many positive examples resemble the negative ones in some degrees and vice versa. These “hard examples” are challenging for existing models. In this paper, we aim to assign fine-grained labels for the examples to capture the degrees of “hardness”, thus enhancing cross-modal correlation learning. Specifically, we propose a siamese network on both the positive and negative examples to obtain their semantic similarities. For each positive/negative example, we use the text description of the image in the example to calculate its similarity with the text in the example. Based on these similarities, we assign fine-grained labels to both the positives and negatives and introduce these labels to a pairwise similarity loss function. The loss function benefits from the labels to increase the influence of hard examples on the similarity learning while maximizing the similarity of relevant text-image pairs and minimizing the similarity of irrelevant pairs. We conduct extensive experiments on the English Wikipedia, Chinese Wikipedia, and TVGraz datasets. Compared with state-of-the-art models, our model achieves significant improvement on the retrieval performance by incorporating with fine-grained labels.
Empirical investigation of memory-based strategy on anomalous behavior

We often encounter various anomalous behaviors of systems, such as machine failures, unexpected behaviors of intelligent agents, and irregular natural phenomena. In order to predict these anomalous behaviors, it is a useful strategy to infer the causal structure of target domains (the inference-based strategy). However, we assume another strategy, the memory-based strategy, to memorize the anomalous behaviors for the predictions. In the present study, we analyzed the features and benefits of the memory-based strategy using the spatial movement prediction task. Experiments 1 and 2 revealed that participants who were instructed to apply the memory-based strategy encoded only the anomalous instances, and not the regular instances. Additionally, the inference-based strategy was more effective for identifying the anomalous instances in a low-complexity task, whereas the memory-based strategy was more effective in a high-complexity task. Experiment 3 revealed that it was difficult to spontaneously select an appropriate strategy based on task complexity and to make benefits of the memory-based strategy for a high-complexity task even if the strategy was applied.
A Hybrid Optimum Method for Removal of Haze using Weight Map Estimation and Fusion Strategy

The visibility of a scene is attenuated by external factors such as fog or haze, dimness and smog. It is also degraded by light scattering caused by minute dust fragments which absorb the light refracted in the atmosphere and the light emitted from an object. Hence this phenomenon clearly becomes an issue for image processing, as they are built for a normally clear field-view. Surveillance systems and outdoor object detection systems require the input image to be clear. Thus, the enhancement of visibility is very important for such applications. We propose a method that removes the haze in the input image in step by step process that includes contrast enhancement, white balancing, weight map estimation and multi-scale fusion techniques.