Reviewerly/exHarmony huggingface dataset:
Learning of Multivariate Beta Mixture Models via Entropy-based component splitting

Finite mixture models are progressively employed in various fields of science due to their high potential as inference engines to model multimodal and complex data. To develop them, we face some crucial issues such as choosing proper distributions with enough flexibility to well-fit the data. To learn our model, two other significant challenges, namely, parameter estimation and defining model complexity have to be addressed. Some methods such as maximum likelihood and Bayesian inference have been widely considered to tackle the first problem and both have some drawbacks such as local maxima or high computational complexity. Simultaneously, the proper number of components was determined with some approaches such as minimum message length. In this work, multivariate Beta mixture models have been deployed thanks to their flexibility and we propose a novel variational inference via an entropy-based splitting method. The performance of this approach is evaluated on real-world applications, namely, breast tissue texture classification, cytological breast data analysis, cell image categorization and age estimation.
Fast Top-N Personalized Recommendation on Item Graph

In the era of big data, traditional supply chain systems can not match the requirement of e-commerce. The analysis of customers’ demands and behaviors are necessary to exploit the potential insights and to build intelligent supply chain systems, which can be achieved by recommender systems. Graph-based recommendation models work well for top-N recommender systems due to their capability to capture the potential relationships between entities. In this paper, we propose a novel graph-based recommendation model to achieve personalized item ranking. To be specific, we design an adapted semi-supervised learning method to capture item smoothness, item fitting, and item confidence. By exploiting the structure of item graph moderately, the proposed method achieves impressive effectiveness and efficiency. In addition, extensive experimental results on real-world datasets show that our proposed method consistently outperforms the state-of-the-art counterparts on the top-N recommendation task.
Usage of Smart Contracts with FCG for Dynamic Robot Coalition Formation in Precision Farming

In solving the problems of precision farming, an important place has the organization of joint work of robots for processing the field. The paper presents an approach to the dynamic formation of a coalition for solving the problem of precision farming, based on the use of fuzzy cooperative games in determining the structure of the coalition. To collect the initial data and save the result of the calculation of the game, a cyberphysical space is used, built based on the “blackboard” and blockchain technologies. Their use allows to combine the advantages of the concept of the Internet of Things for collecting information from sensors of agricultural robots and the immutability of data blocks to save the results of the calculation of the game in a competitive environment. To ensure the dynamic change of the coalition, smart contracts are used over the blockchain technology. Contracts contain the rules for calculating a fuzzy cooperative game and the rules for changing the composition of the coalition. As a result, the proposed approach provides the dynamic formation of a coalition with the trust of all participants and the ability to collect and disseminate information from robot sensors in a common trusted information space. To implement the blockchain and smart contract, the approach proposes to use the Hyperledger Fabric platform.
Hybrid Multi-Evolutionary Algorithm to Solve Optimization Problems

The article presents a Hybrid Multi-Evolutionary Algorithm designed to solve optimization problems. The Genetic Algorithm and Evolutionary Strategy work together to improve the efficiency of optimization and increase resistance to getting stuck to sub-optimal solutions. Genetic Algorithm and Evolutionary Strategy can periodically exchange the best individuals from each other. The algorithm combines the ability of the Genetic Algorithm to explore the search space and the ability of the Evolutionary Strategy to exploit the search space. It maintains the right balance between the exploration and exploitation of the search space. The results of the experiments suggest that the proposed algorithm is more effective than the Genetic Algorithms and Evolutionary Strategy used separately, and can be an effective tool in solving complex optimization problems.
On Temporal Context Information for Hybrid BLSTM-Based Phoneme Recognition

The modern approach to include long-term temporal context information into speech recognition systems is the use of recurrent neural networks, e.g., bi-directional long short-term memory (BLSTM) networks. In this paper, we decouple the BLSTM from a preceding CNN-based feature extractor network allowing us to investigate the use of temporal context in both models in a modular fashion. Accordingly, we train the BLSTMs on posteriors, stemming from preceding CNNs which use various amounts of limited context in their input layer, and investigate to what extent the BLSTM is able to effectively make use of its long-term modeling capabilities. We show that it is beneficial to train the BLSTM on posteriors stemming from a temporal context-free acoustic model. Remarkably, the best performing combination of CNN acoustic model and BLSTM afterwards is a large-context CNN (expected), followed by a BLSTM which has been trained on context-free CNN output posteriors (surprising).
Harmony Search with Dynamic Adaptation of Parameters for the Optimization of a Benchmark Controller

A fuzzy harmony search algorithm (FHS) is presented in this paper. This method uses a fuzzy system for dynamic adaptation of the harmony memory accepting (HMR) and pitch adjustment (PArate) parameters along the iterations, and in this way achieving control of the intensification and diversification of the search space. This method was previously applied to various benchmark controller cases however in this case we decided to apply the proposed FHS to benchmark controller problem with different types of noise: band-limited white noise, pulse noise, and uniform random number noise to check the efficiency for the pro-posed method. A comparison is presented to verify the results obtained with the original harmony search algorithm and fuzzy harmony search algorithm.
Document Alignment for Generation of English-Punjabi Comparable Corpora from Wikipedia

Comparable corpora come as an alternative to parallel corpora for the languages where the parallel corpora is scarce. The efficiency of the models trained on comparable corpora is comparatively less to that of the parallel corpora however it helps to compensate much to the machine translation. In this article, the authors have explored Wikipedia as a potential source and delineated the process of alignment of documents which will be further used for the extraction of parallel data. The parallel data thus extracted will help to enhance the performance of Statistical Machine translation.
Scene Retrieval Using Text-to-image GAN-based Visual Similarities and Image-to-text Model-based Textual Similarities

Scene retrieval from a video database is a fundamental study in computer vision. Traditionally, content based retrieval methods can retrieve objective scenes with high accuracy by utilizing visual features. However, users cannot utilize content based retrieval methods when they cannot prepare query contents. To solve this problem, in this paper, we propose a novel content based scene retrieval method focusing on text-to-image Generative Adversarial Network and image-to-text model. By utilizing the proposed method, we can retrieve objective scenes in visual feature space with high accuracy even though it only utilizes a sentence as an input. Experimental results show the effectiveness of the proposed method.
Collaborative Attentive Autoencoder for Scientific Article Recommendation

Matrix Factorization (MF) is a successful collaborative filtering approach used in recommendation systems. However, its performance decreases significantly when users of the system have limited, inadequate feedback data. This problem is also known as the data sparsity problem. To handle this problem, hybrid approaches were proposed recently to integrate items' contextual information with MF-based approaches, which improved the performance of recommendations. Nevertheless, learning better representation of the items' contents is still a challenge that needs to be further enhanced. In this paper, we propose a Collaborative Attentive Autoencoder (CATA) that learns latent factors of items through an attention mechanism that can capture the most pertinent part of information for making better recommendations. Comprehensive experiments on two real-world datasets have shown our method performs better than the state-of-the-art models according to various evaluation metrics.
Automatic Facial Expression Recognition Using Histogram Oriented Gradients (HoG) of Shape Information Matrix

Nandi, Avishek Dutta, Paramartha Md NasirAutomatic Recognition of Facial Expression (AFRE) has many potential applications in Human–Computer Interaction (HCI), security and surveillance, smart emotion-driven tutoring, etc. Human beings often display their emotions in various media of communication like language, voice, gesture, and facial expression. The contribution of facial expression is maximum among other means of communication. We have proposed a novel shape-oriented representation of facial structure using appearance information of the face. Active Appearance Model is used to select 25 salient landmark points on the face and a Shape Information Matrix (SIM) is formed by forming triangles on the face using landmark points. Histogram oriented Gradients feature of the Shape Information Matrix (SIM) is used to extract local orientation features of facial image. The proposed machine is trained using MultiLayer Perceptron (MLP) Neural Network and well tested over CK+, JAFFE, MMI, and MUG benchmark databases. Results obtained are impressive and encouraging.
Study of Sentiment Analysis in Mental Health Using Speech Recognition and Image to Text Conversion

The study of sentiment analysis provides a given subject from something written or spoken. Data from social media is generated enormously every year that can be used to analyse the user’s sentiment or opinion. The online data by every user on social media can be used in the field of sociology and psychology. The maximum time is consumed by social media on an average by every human being now. This data analysis can help in the prevention of various mental health disorders and help in early detection of depression.
Analysis of electronic health records based on long short‐term memory

Summary There is a large amount of historical data of the patient's hospitalization named the electronic health records (EHRs), but the data are not fully utilized for great challenges as poor quality, high dimension, and so on. Previous studies have primarily used machine learning methods that rely heavily on manual extraction of features. Recently, many deep learning approaches are applied to predictive model of EHRs. Recurrent neural networks (RNN) are often used to model EHR data, but RNN performance degrades in the face of large sequence lengths. To solve these challenges, we develop a long short‐term memory with attention mechanism for mortality prediction. The dataset used in this article is the Medical Information Mart for Intensive Care III, which contains comprehensive clinical data for the patients. The experimental results demonstrate that the predicted results can be effectively interpreted using the attention mechanism. Compared with other baseline models, our model improves the accuracy of prediction, and helps doctors reduce the average diagnostic time.
A cut-free labelled sequent calculus for dynamic epistemic logic

Abstract Dynamic epistemic logic is a logic that is aimed at formally expressing how a person’s knowledge changes. We provide a cut-free labelled sequent calculus ($\textbf{GDEL}$) on the background of existing studies of Hilbert-style axiomatization $\textbf{HDEL}$ of dynamic epistemic logic and labelled calculi for public announcement logic. We first show that the $cut$ rule is admissible in $\textbf{GDEL}$ and show that $\textbf{GDEL}$ is sound and complete for Kripke semantics. Moreover, we show that the basis of $\textbf{GDEL}$ is extended from modal logic K to other familiar modal logics including S5 with keeping the admissibility of cut, soundness and completeness.
Kripke semantics for the logic of problems and propositions

Abstract In this paper we study the propositional fragment <?CDATA $\mathrm{HC}$?> of the joint logic of problems and propositions introduced by Melikhov. We provide Kripke semantics for this logic and show that <?CDATA $\mathrm{HC}$?> is complete with respect to those models and has the finite model property. We consider examples of the use of <?CDATA $\mathrm{HC}$?> -models usage. In particular, we prove that <?CDATA $\mathrm{HC}$?> is a conservative extension of the logic <?CDATA $\mathrm{H4}$?> . We also show that the logic <?CDATA $\mathrm{HC}$?> is complete with respect to Kripke frames with sets of audit worlds introduced by Artemov and Protopopescu (who called them audit set models). Bibliography: 31 titles.
Joint detection and tracking for stealth targets in noisy imagery based on variational Bayesian-sequential Monte Carlo-cardinality-balanced multitarget multi-Bernoulli filter

We explore the use of an improved particle filter to detect and track stealth targets (STs) in noisy imagery with unknown detection profile. Recently, a cardinality-balanced multitarget multi-Bernoulli (CBMeMBer) of variational Bayesian (VB) approximation has been proposed for tracking multitargets with unknown measurement variances. However, VB-CBMeMBer filter has been implemented with a known detection profile, which is unsuitable for ST tracking. The Bayesian track-before-detect (TBD) is an efficient way to detect low observable targets. A new VB-CBMeMBer-TBD filter is proposed to cope with jointly unknown detection profile and measurement variances for ST tracking. Furthermore, a sequential Monte Carlo implementation is applied to estimate augmented kinematic ST state. The results show that the proposed filter is more accurate than other filters in cardinality estimation and less optimal subpattern assignment errors.
Prototype-Based Interpretation of Pathological Image Analysis by Convolutional Neural Networks

The recent success of convolutional neural networks (CNNs) attracts much attention to applying a computer-aided diagnosis system for digital pathology. However, the basis of CNN’s decision is incomprehensible for humans due to its complexity, and this will reduce the reliability of its decision. We improve the interpretability of the decision made using the CNN by presenting them as co-occurrences of interpretable components which typically appeared in parts of images. To this end, we propose a prototype-based interpretation method and define prototypes as the components. The method comprises the following three approaches: (1) presenting typical parts of images as multiple components, (2) allowing humans to interpret the components visually, and (3) making decisions based on the co-occurrence relation of the multiple components. Concretely, we first encode image patches using the encoder of a variational auto-encoder (VAE) and construct clusters for the encoded image patches to obtain prototypes. We then decode prototypes into images using the VAE’s decoder to make the prototypes visually interpretable. Finally, we calculate the weighted combinations of the prototype occurrences for image-level classification. The weights enable us to ascertain the prototypes that contributed to decision-making. We verified both the interpretability and classification performance of our method through experiments using two types of datasets. The proposed method showed a significant advantage for interpretation by displaying the association between class discriminative components in an image and the prototypes.
A Design Process Integrating Human-Data Interaction Guidelines and Semio-Participatory Design

Software tools that facilitate human interaction with data play a key role in the understanding of information from large masses of data. Interaction design in the domain of petroleum field exploration is a challenging task, in part because of its complex intrinsic context, and the lack of methodological framework to support design activities. The use of guidelines builds on the expertise of design specialists, but does not leverage the knowledge of the various stakeholders in the problem. In this article, we advocate a more socially comprehensive approach to the design of Human-Data Interaction (HDI) applications, articulating design guidelines with artifacts and methods from Organizational Semiotics and Participatory Design. Our investigation defines a process for the design of visual data analysis tools and conducts a case study in the context of petroleum production strategy with the participation of key stakeholders. We propose practices for each step of our design process. Adapted participatory practices combined with design guidelines were used to clarify the problem, design the solution, and evaluate the prototype. Participants showed satisfaction with the employed design practices and the outcomes.
Secure Event Logging Using a Blockchain of Heterogeneous Computing Resources

Secure logging is essential for the integrity and accountability of cyber-physical systems (CPS). To prevent modification of log files the integrity of data must be ensured. In this work, we propose a solution for secure event in cyber-physical systems logging based on the blockchain technology, by encapsulating event data in blocks. The proposed solution considers the real-time application constraints that are inherent in CPS monitoring and control functions by optimizing the heterogeneous resources governing blockchain computations. In doing so, the proposed blockchain mechanism manages to deliver events in hard-to-tamper ledger blocks that can be accessed and utilized by the various functions and components of the system. Performance analysis of the proposed solution is conducted through extensive simulation, demonstrating the effectiveness of the proposed approach in delivering blocks of events on time using the minimum computational resources.
A Scalable Algorithm for Multiparty Interactive Communication with Private Channels

A group of n players wants to run a distributed protocol ρ over a network where communication occurs via private point-to-point channels. Unfortunately, an adversary, who knows P, is able to maliciously flip bits on the channels. Can we efficiently simulate ρ in the presence of such an adversary?
On the Modeling and Agent-Based Simulation of a Cooperative Group Anagram Game

Anagram games (i.e., word construction games in which players use letters to form words) have been researched for some 60 years. Games with individual players are the subject of over 20 published investigations. Moreover, there are many popular commercial anagram games such as Scrabble. Recently, cooperative team play of anagram games has been studied experimentally. With all of the experimental work and the popularity of such games, it is somewhat surprising that very little modeling of anagram games has been done to predict player behavior/actions in them. We devise a cooperative group anagram game and develop an agent-based modeling and simulation framework to capture player interactions of sharing letters and forming words. Our primary goals are to understand, quantitatively predict, and explain individual and aggregate group behavior, through simulations, to inform the design of a group anagram game experimental platform.
Automated Ontology Extraction from Unstructured Texts using Deep Learning

Ontologies are computational artifacts to represent knowledge through classes and relations between them. Those knowledge bases require a lot human effort to be constructed due to the need of domain experts and knowledge engineers. Ontology Learning aims to automatically build ontologies from data that can be from multimedia, web pages, databases, unstructured text, etc. In this work, we propose a methodology to automatically build an ontology to represent concepts map of subjects to be used in academic context. The main contribution of this methodology is that does not require handcrafted features by using Deep Learning techniques to identify taxonomic and semantic relations between concepts of some specific domain. Also, due the implementation of transfer learning is not needed of specific domain dataset, the relation classification model is trained with Wikipedia and WordNet by distant supervision technique and the knowledge is transferred to a specific domain by word embedding techniques. The results of this approach are promising considering the lack of human intervention and feature engineering.
Statistical Indices for Despeckling Evaluation in Multichannel SAR Images

With reference to the application of multichannel (polarimetric or interferometric) synthetic aperture radar (SAR) data, despeckling is a mandatory task in order to exploit fully the image information. Thanks to the long-standing studies, there exist several despeckling techniques that have shown to be powerful tools for filtering multichannel SAR images. Often, such techniques rely on the estimation of the covariance matrix. However, in the literature, the performance evaluation of the filtering operation, which represents a fundamental aspect, has been mainly addressed for a single intensity/amplitude SAR image. In this letter, we present two nonreference indices for the evaluation of the polarimetric/interferometric covariance matrix. The proposed quality indices assess the statistical similarity of the ratio between the original and filtered covariance matrices to the properties derived from the pure speckle model. The analyses both on simulated and real data show that the results of filter ranking by the proposed method are in agreement with the visual evaluation and are consistent with the common polarimetric quality measures.
Detection of Fraudulent Behavior Using the Combined Algebraic and Machine Learning Approach

The paper presents two approaches to detecting fraudulent behavior: the machine learning technique, which was developed to recognize fraud in the Internet purchasing system; the algebraic approach, which was developed for the detection of attacks in blockchain-based systems. The machine learning approach uses technique of translating heterogeneous data to homogenous data and builds the classification model based on fuzzy logic rules for fraud recognition. The algebraic approach uses behavior algebra methods and symbolic modeling techniques.
GSM: Inductive Learning on Dynamic Graph Embeddings

In this paper, we study the problem of learning graph embeddings for dynamic networks and the ability to generalize to unseen nodes called inductive learning. Firstly, we overview the state-of-the-art methods and techniques for constructing graph embeddings and learning algorithms for both transductive and inductive approaches. Secondly, we propose an improved GSM based on GraphSAGE algorithm and set up the experiments on datasets CORA, Reddit, and HSEcite, which is collected from Scopus citation database across the authors with affiliation to NRU HSE in 2011–2017. The results show that our three-layer model with attention-based aggregation function, added normalization layers, regularization (dropout) outperforms suggested by the respective authors’ GraphSAGE models with mean, LSTM, and pool aggregation functions, thus giving more insight into possible ways to improve inducting learning model based on GraphSAGE model.