Reviewerly/exHarmony huggingface dataset:
Optimizing Nonlinear Parameters of Sugeno Type Fuzzy Rules using GWO for Data Classification

In this paper, a Sugeno type fuzzy system based on the fuzzy clustering has been developed for a variety of datasets. The number of rules for each dataset is based on the optimum number of clusters in that dataset. Rule sets provide the knowledge base for the classification of data. Each rule set is fine-tuned using the GWO with the intention to improve the classification. The approach is compared with the work of previous researchers on similar data sets using a variety of techniques, including nature-inspired algorithms such as genetic algorithms and Swarm based algorithms. Statistical Analysis of the performance of GWO shows that it is better than five other algorithms 95% of the time.
Spatial Gating Strategies for Graph Recurrent Neural Networks

Graph Recurrent Neural Networks (GRNNs) are a neural network architecture devised to learn from graph processes, which are time sequences of graph signals. Similarly to traditional recurrent neural networks, GRNNs experience the problem of vanishing/exploding gradients when learning long term causal dependencies. When these dependencies do not depend on the graph, this issue is solved by the addition of time gates (long short-term memory architectures). However, in graph processes long term dependencies are directly influenced by the graph structure, and can be stronger or weaker across certain node paths. To address this, we propose two spatial gating strategies for GRNNs leveraging the node and edge structure of the graph. Node and edgegated GRNNs are shown to outperform other GRNNs in a synthetic as well as a real-world problem of earthquake epicenter prediction.
Efficient VM Migration Policy in Cloud Computing Environment

Cloud computing provides resources on shared basis but resources do get exhausted, as more and more resource-dependent tasks are being executed on the cloud. This eventually leads to distortion and one possible solution to overcome this problem is migration. In this paper, we perform VM migration in an energy-efficient manner for which we calculate the load factor on all the individual servers. If the load exceeds the assigned threshold value, then that server is considered as the overloaded host, after this the random selection of the VMs is done from the under-loaded hosts and then the machine with less migration time and more utilization will be migrated to the destination host. Further, we compare our purposed technique with already established techniques. The comparison results in the form of Migration Time, Utilization, and Energy Consumption shows that the proposed technique performs better than the existing one.
Off-the-Record (OTR) Security Protocol Application in Cloud Environment

The Pretty Good Privacy protocol is the most preferred choice for a safe and encrypted communication between two or more parties since it uses encrypted messages for the communication. However, the Off-the-Record (OTR) protocol fits better than PGP protocol as it does not fit well due to some of its characteristics. In the real world, physical communication has some characteristics that do not match with nature of PGP protocol. That offset in aspects was the stimulus to create a new kind of communication protocol: OTR. As the name says, "Off-the-Record" protocol tries to create distance from the message and its sender. When this distance is created, every message sent is unofficial and the sender can negate his correlation to it. The sender can make a statement and, in the future, deny any relations with it. Although this is the main premise of OTR, there is a list of additional characteristics that we are addressed in this paper. We also address the differences between OTR and some of the most used protocols in Internet of things (IoT) sector and why should IoT network use it.
Chinese Prosodic Phrase Prediction Based on Shallow Semantic Features

Syntactic structure features can improve the performance of prosodic phrase prediction. But only using the syntactic structure features, the performance of the algorithm is worse than the traditional text features. In this paper, we use the statistical machine learning method (CART, Adaboost and CRF) for prosodic phrase prediction based on the shallow semantic features. Experiments show that the shallow semantic features can effectively improve the performance of prosodic prediction model. And we also optimized the features, and divided them into the global and local semantic structures. The optimized experiments show that the optimized features can improve the performance of the model.
Fast Algorithms on Primality Testing for Numbers 255 ⋅ 2^n ± 1

In this paper, we study the problem of primality testing for numbers of the form h · 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> ± 1, where h <; 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> is odd, and n is a positive integer. We describe explicit Lucasian primality tests for these numbers in certain cases, which run in deterministic quasi-quadratic time. In particular, we implement the explicit algorithms related to numbers M <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sub> = h · 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> - 1 for the case h =255. And we find large prime numbers which are more than 512 bits efficiently. The algorithms of Bosma (1993), Berrizbeitia and Berry (2004), Deng and Huang (2016) can not generate these prime numbers, even can not test the primality of these numbers.
An EMD based approach for Saliency Detection in Multimedia Data

The advancement of multimedia technology has increased the problem of its storage, transmission and display etc. As this multimedia data is mostly targeted to humans, multimedia researchers are working in field of incorporation of human's perception, action related abilities. Detection of interesting content in images or videos based on human's attention-based perspective is termed as Saliency. Various existing popular methods which have been used for saliency estimation were based on techniques like Difference of Gradients (DoG), K1 Divergence. These methods are time consuming and are applicable on selected parts of input only. This causes need to find a better approach which should be time-efficient and provides better results. In this paper Earth Mover's Distance (EMD) based approach is adopted with bottom-up features-based saliency estimation for image and video data. Result shows that incorporation of EMD which is based on calculating histogram differences to find minimal cost incurred in transforming input data as close as output data, provides better results in terms of time consumption.
Learning to Generate Diverse Questions from Keywords

Diverse text generation has been emerging as an important topic of natural language generation. Traditional studies on question generation mainly investigate how to generate one question based on a given input (one-to-one). In this paper, we focus on a more complex question generation task, i.e., generating a series of questions for each set of keywords (one-to-many). As an effort towards this, we propose a novel neural generative model, which incorporates context information and control signal to produce multiple diverse questions from a given fixed set of keywords. The control signal is designed to increase the diversity of questions by capturing the diverse patterns from the entire dataset. The context information is used to guarantee the generated questions are highly related to the given keywords. To evaluate the effectiveness of the proposed model, we collect a dataset which contains 62835 questions with respect to 12567 sets of keywords. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> To the best of our knowledge, it's the first Chinese financial dataset for diverse question generation. The experimental results show that our model outperforms the competitor methods in terms of BLEU and Distinct. The qualitative evaluation indicates that our model is able to generate diverse and meaningful questions.
Teaching Data Carving Using The Real World Problem of Text Message Extraction From Unstructured Mobile Device Data Dumps

Data carving is a technique used in data recovery to isolate and extract files based on file content without any file system guidance.It is an important part of data recovery and digital forensics.However, it is also useful in teaching computer science students about file structure and the binary encoding of information, especially within a digital forensics program.This work demonstrates how the authors teach data carving using a real-world problem they encounter in digital forensics evidence processing involving the extracting of text messages from unstructured small device binary extractions.The authors have used this problem for instruction in digital forensics courses and other computer science courses.
A Comprehensive Study of Residual CNNS for Acoustic Modeling in ASR

Long short-term memory (LSTM) networks are the dominant architecture for large vocabulary continuous speech recognition (LVCSR) acoustic modeling due to their good performance. However, LSTMs are hard to tune and computationally expensive. To build a system with lower computational costs and which allows online streaming applications, we explore convolutional neural networks (CNN). To the best of our knowledge there is no overview on CNN hyper-parameter tuning for LVCSR in the literature, so we present our results explicitly. Apart from recognition performance, we focus on the training and evaluation speed and provide a time-efficient setup for CNNs. We faced an overfitting problem in training and solved it with data augmentation, namely SpecAugment. The system achieves results competitive with the top LSTM results. We significantly increased the speed of CNN in training and decoding approaching the speed of the offline LSTM.
A Fast and Effective Watermarking Method for Medical Data security

Personal health information is considered a target for illegal users that attempt to read, delete, and/or steal data. Therefore, watermarking techniques offer new ways to protect sensitive data transferred over the Internet. This article presents a novel watermarking approach for securing sensitive data in e-health applications based on DWT and Schur decomposition coefficients. The proposed approach aims to offer a valuable balance between robustness and imperceptibility. Several experiments were conducted using a standard dataset of medical images. The experiments achieved promising results for high-level data protection and good imperceptibility values compared to existing works.
Distributed Source Coding Based on Denoising Theory

Aiming at the error propagation problem and other shortcomings of existing distributed source coding (DSC) schemes, we propose a DSC scheme based on the denoising theory (DSCBDT). First, the amount of data that need to be transmitted is reduced by sampling the source output sequence. The un-sampled sequence is compressed by conventional arithmetic coder. Finally, the receiver recovers the sampled symbols by making full use of the correlation between the side-information (SI) sequence and the un-sampled sequence. The experiment results show that compared with the low-density parity-check (LDPC) code-based DSC scheme, the proposed scheme shows higher compression ratios and lower error rate when the correlation between the sources is weak. Specifically, the error propagation problem is effectively avoided. The DSCBDT scheme is highly efficient, practical and easy to implement.
Segmentation of Medical Images with a Combination of Convolutional Operators and Adaptive Hidden Markov Model

Medical image segmentation is a problem of fundamental importance in medical image processing. The accurate segmentation of a medical image can provide important information for the diagnosis and treatment of many diseases. Since a medical image often contains noises and the objects in it are inherently complex in general, methods that can accurately segment an arbitrary medical image are still unavailable. In this paper, a new approach that combines convolutional operators and an adaptive Hidden Markov Model is developed for the segmentation of medical images. Specifically, the features associated with each pixel in a medical image are obtained with a set of convolutional operators. The semantic and spatial correlations among pixels in the image are then progressively captured by an adaptive Hidden Markov Model. The labels of the pixels can be efficiently obtained with a dynamic programming algorithm in linear time. Our experimental results show that this approach can achieve segmentation results with improved accuracy on a set of brain medical images.
A Novel Collaborative Filtering Approach Based on the Opposite Neighbors’ Preferences

Collaborative filtering (CF) has become an effective way to predict useful items. It is the most widespread recommendation technique. It relies on users who share similar tastes and preferences to suggest the items that they might be interested in. Despite its simplicity and justifiability, the collaborative filtering approach experiences many problems, including sparsity, gray sheep and scalability. These problems lead to deteriorating the accuracy of the obtained results. In this work, we present a novel collaborative filtering approach based on the opposite preferences of users. We focus on enhancing the accuracy of predictions and dealing with gray sheep problem by inferring new similar neighbors based on users who have dissimilar tastes and preferences. For instance, if a user X is dissimilar to a user Y then the user ┐X is similar to the user Y. The Experimental results performed on two datasets including MovieLens and FilmTrust show that our approach outperforms several baseline recommendation techniques.
Coevolving Artistic Images Using OMNIREP

We have recently developed OMNIREP, a coevolutionary algorithm to discover both a representation and an interpreter that solve a particular problem of interest. Herein, we demonstrate that the OMNIREP framework can be successfully applied within the field of evolutionary art. Specifically, we coevolve representations that encode image position, alongside interpreters that transform these positions into one of three pre-defined shapes (chunks, polygons, or circles) of varying size, shape, and color. We showcase a sampling of the unique image variations produced by this approach.
Formation of Multicultural Competence of the Teacher of Native (Tatar) Language in the Context of Advanced Training Program Implementation

The ways and means of implementing the contents of an additional professional educational advanced training program consisting of classroom and extracurricular activities aimed at incorporating the mastered experience into the practice of trainees (listeners) to solve specific problems of professional activity are considered. Based on the analysis of legal documents and scientific and methodical literature, the purpose, objectives and content of the formation of professional competences of teachers of the native (Tatar) language within the additional advanced training program implementation and post-course period have been determined. The purpose of the study is to reveal the possibilities of additional professional educational advanced training program aimed at improving the multicultural competence of the teacher of native (Tatar) language “Formation of multicultural competences of the teacher of teacher of native (Tatar) language”. The author outlines that the aim of the additional professional educational program is to improve the professional competence of teachers of the native (Tatar) language, oriented and ready to work in a multicultural educational environment at the entry stage of Russian school in international competition. Methods. During the implementation of the program the following methods were applied: reflexive analysis, modeling, case-study, project method, etc. It is concluded that professional competences formed during the implementation of the additional professional educational advanced training program are designed to implement the tasks of multicultural education in practical activities of teachers of the native (Tatar) language.
Multi-agent Based Recommender System for Netflix

The main aim of this work is to build a multi-agent based recommender system that comprises heterogeneous software agents, where various member agents interact among themselves to accomplish various tasks and achieve objectives of the system. The main objectives of this work are: first, it presents some basics of multi-agent based recommender system. Second, it reviews the main research developments and works previously performed in the field of recommender systems with machine learning. Third, it introduces a multi-agent based recommender system framework based on a collaborative approach by considering various agents to recommend movies to users, who have similar interests. Furthermore, the proposed framework has been experimentally assessed by implementing a cosine similarity algorithm to measure user–user similarity based on movie ratings. The multi-agent programming environment NetLogo is used to simulate the results.
An Optimized Intuitionistic Fuzzy Associative Memories (OIFAM) to Identify the Complications of Type 2 Diabetes Mellitus (T2DM)

Fuzzy associative memories (FAM) is a recurrent neural network, consisting of two layers. Since points of the fuzzy set are defined in a cube, it maps between cubes. That is, it maps from input fuzzy set into an output fuzzy set. While the input layer is deliberated as the cause infusing agent the output layer influences the requisite effect. It is a powerful technique to analyze the cause and effect of any problem. Determining the most influential factors in the cause and effect group of any problem is a challenging task. To quench such a task, this present study constructs an optimized intuitionistic fuzzy associative memory using an intuitionistic fuzzy set and a variance of fitness formula. To check the validity of the proposed model, Type 2 diabetes mellitus is taken for diagnosing the early complications of T2DM patients from the risk factors.
Environmental engineering for social resilience and tourism in Plajan Village Jepara Central Java Province

Abstract We investigate the suitable model to carry out environmental engineering primarily in aspects of social resilience and tourism in Plajan Village Jepara Central Java. Using a mixed method, this study shows that social and tourism resilience can be developed by involving stakeholders in the village consisting of communities, entrepreneurs and local governments, as well as academic groups; these forces are communities that work together synergistically and coordinative. “The necessary conditions” for local environmental engineering towards social and tourism resilience in a sustainable manner is the need for village infrastructure, especially for tourist sites or spots that are still lacking in information. This infrastructure can be divided into two things: “gray infrastructure” and “blue infrastructure”. Gray infrastructure is public infrastructure that can disrupt community activities and tourism, such as roads, bridges, social infrastructure, while Blue infrastructure is directly related to local environments such as protected forests and water resources.
The Picasso Algorithm for Bayesian Localization Via Paired Comparisons in a Union of Subspaces Model

We develop a framework for localizing an unknown point w using paired comparisons of the form "w is closer to point x <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sub> than to x <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">j</sub> " when the points lie in a union of known subspaces. This model, which extends a broad class of existing methods to exploit union of subspaces structure, provides a powerful framework for using the types of structure found in many practical applications. We divide the problem into two phases: (1) determining which subspace w lies in, and (2) localizing w within the identified subspace using existing techniques. We introduce two algorithms for determining the subspace in which an unknown point lies: the first admits a sample complexity guarantee demonstrating the advantage of the union of subspaces model, and the second improves performance in practice using an adaptive Bayesian strategy. We demonstrate the efficacy of our method with experiments on synthetic data and in an image search application.
Learning From Class Imbalance

Learning a classifier from imbalanced data is one of the most challenging research problems. Data imbalance occurs when the number of instances belonging to one class is much less than the number of instances belonging to the other class. A standard classifier is biased towards the majority class and therefore misclassifies the minority class instances. Minority class instances may be regarded as rare events or unusual patterns that could potentially have a negative impact on the society. Therefore, detection of such events is considered significant. This chapter proposes a FireWorks-based Hybrid ReSampling (FWHRS) algorithm to resample imbalance data. It is used with Weighted Pattern Matching based classifier (PMC+) for classification. FWHRS-PMC+ was evaluated on 44 imbalanced binary datasets. Experiments reveal FWHRS-PMC+ is effective in classification of imbalanced data. Empirical results were validated using non-parametric statistical tests.
Adaptive Parameters Softmax Loss for Deep Face Recognition

Face recognition has achieved great success due to the development of Deep convolutional neural networks (DCNN). Loss functions with angular margin have been proposed to supervise DCNN for better feature representation. However, these methods would suffer from sensitivity of hyper-parameters setting. In this paper, we propose an Adaptive Parameters Softmax Loss with different scale parameters for target logits and non-target logits and dynamically adaptive margin parameter. Extensive experiments on Mega-Face and IJB-C demonstrate the effectiveness of our method.
Improved quantum de Finetti theorem and its application in quantum key distribution

Quantum de Finetti theorem indicates that a surprising connection exists between the permutation-invariant property of a large system and the independent, identical distribution that describes its subsystems. The theorem is highly nontrivial in that it allows one to infer independence---within a finite error---from symmetry alone, both of which are important and fundamental concepts in physics and mathematics. Here, we present an improved quantum de Finetti theorem that provides a lower error bound, thus establishing an even closer relationship between symmetry and independence than before. As application, we study the security of quantum key distribution under the most general attacks, and show that the key rate is higher than previous ones obtained by existing trace-norm-based de Finetti theorems. Therefore, the application of the quantum de Finetti theorem in quantum information processing, as well as developing the theorem itself, is advanced here.