Reviewerly/exHarmony huggingface dataset:
A hierarchical clustering algorithm and an improvement of the single linkage criterion to deal with noise

Hierarchical clustering is widely used in data mining. The single linkage criterion is powerful, as it allows for handling various shapes and densities, but it is sensitive to noise1. Two improvements are proposed in this work to deal with noise. First, the single linkage criterion takes into account the local density to make sure the distance involves core points of each group. Second, the hierarchical algorithm forbids the merging of representative clusters, higher than a minimum size, once identified. The experiments include a sensitivity analysis to the parameters and a comparison of the available criteria using datasets known in the literature. The latter proved that local criteria yield better results than global ones. Then, the three single linkage criteria were compared in more challenging situations that highlighted the complementariness between the two levels of improvement: the criterion and the clustering algorithm.
A Deep Spatial and Temporal Aggregation Framework for Video-Based Facial Expression Recognition

Video-based facial expression recognition is a long-standing problem owing to a gap between visual features and emotions, difficulties in tracking the subtle movement of muscles and limited datasets. The key to solving this problem is to exploit effective features characterizing facial expression to perform facial expression recognition. We propose an effective framework to solve these problems. In our work, both spatial information and temporal information are utilized through the aggregation layer of a framework that fuses two state-of-the-art stream networks. We investigate different strategies for pooling across spatial information and temporal information. We find that it is effective to pool jointly across spatial information and temporal information for video-based facial expression recognition. Our framework is end-to-end trainable for whole-video recognition. In addressing the problem of facial recognition, the main contribution of this project is the design of a novel, trainable deep neural network framework that fuses spatial information and temporal information of video according to CNNs and LSTMs for pattern recognition. The experimental results on two public datasets, i.e., the RML and eNTERFACE05 databases, show that our framework outperforms previous state-of-the-art frameworks.
Best Information is Most Successful

Using information-theoretic tools, this paper establishes a mathematical link between the probability of success of a side-channel attack and the minimum number of queries to reach a given success rate, valid for any possible distinguishing rule and with the best possible knowledge on the attacker’s side. This link is a lower bound on the number of queries highly depends on Shannon’s mutual information between the traces and the secret key. This leads us to derive upper bounds on the mutual information that are as tight as possible and can be easily calculated. It turns out that, in the case of an additive white Gaussian noise, the bound on the probability of success of any attack is directly related to the signal to noise ratio. This leads to very easy computations and predictions of the success rate in any leakage model.
Feature selection for classification of microarray gene expression cancers using Bacterial Colony Optimization with multi-dimensional population

The main challenge of feature selection is overcoming the curse of dimensionality. In this paper, a new Bacterial Colony Optimization method with Multi-Dimensional Population, abbreviated as BCO-MDP, is presented for feature selection for the purpose of classification. To address the combinational problem associated with feature selection, the population with multiple dimensionalities is represented by subsets of different feature sizes. The population is grouped in terms of ‘Tribes’. The sizes of the feature subsets within a tribe are equal, while the dimensionalities differ when they belong to different tribes to achieve parallel solutions. The features are identified by their contributions to the most promising solutions in the total population and the classification performances of their tribes. A search is then conducted for the optimal feature subsets with varying dimensionalities. The convergence speed can be enhanced by a variety of exchange strategies within and between tribes. The proposed BCO-MDP method is demonstrated to be superior to the binary algorithms in terms of feature size and efficiency, while having a lower computational complexity in comparison to other population-based algorithms with constant dimensionality.
Deep learning for ultrasound image caption generation based on object detection

Deep learning for image caption generation makes great progress in the field of natural images. However, there are still lack of effective methods for detailed analysis and automatic description of diseases content information in ultrasound image understanding. In order to find the location of focus areas, and understand the content of focus areas conveniently, we propose a novel method of ultrasound image captioning generation based on region detection. The method simultaneously detects and encodes the focus areas in ultrasound images, then utilizes the LSTM to decode the encoding vectors and generate annotation text information to describe the diseases content information in ultrasound images. The experimental results show that the method can accurately detect the location of the focus area, and also improves 1% the scores of BLEU-1, BLEU-2 with less parameters and running time, which compared with the full-size-image captioning model for ultrasound images.
Demonstration of Controlled Quantum Teleportation for Discrete Variables on Linear Optical Devices

We report an experimental implementation of tripartite controlled quantum teleportation on quantum optical devices. The protocol is performed through bi- and tripartite entangled channels of discrete variables and qubits encoded in the polarization of individual photons. The experimental results demonstrate successful controlled quantum teleportation with a fidelity around 83%, well above the classical limit. By realizing the controlled quantum teleportation through a biseparable state, we show that tripartite entanglement is not a necessary resource for controlled quantum teleportation, and the controller's capability to allow or prohibit the teleportation cannot be considered to be a manifestation of tripartite entanglement. These results open new possibilities for further application of controlled quantum teleportation by lowering the teleportation channel's requirements.
The Use of Exploratory and Confirmatory Factor Analyses: A Document Analysis

This paper aims to review the scale development research published in Turkey between 2006 and 2016 with regard to their processes of exploratory (EFA) and confirmatory factor analysis (CFA). Within this scope, the distribution of the studies according to years and factor analyses, the extent to which their hypotheses came through for both analysis methods, and the distribution of EFA and CFA results according to the published papers were investigated. In this way, we aimed to infer significant results on the practical application of EFA and CFA which are frequently referred in theory. Hence, the present research incorporates a descriptive document analysis on 131 scale development studies published in the fields of education and social sciences in the journals indexed in The Scientific and Technological Research Council of Turkey (TÜBİTAK) TR Dizin data base between 2006 and 2016. Frequency tables, column charts, histogram, line charts, and measures of central tendency were used to analyse the data. More than 70% of 131 scale development studies were carried out after 2013. The maximum number of studies was published in 2015, and the minimum number in 2008. Only EFA was used in 59 articles, only CFA was used in 4 articles, and both analysis methods were employed in 58 articles. The remaining 10 papers used none of the analyses and included only item analyses. The average sample sizes were 395 participants for EFA and 529 participants for CFA. Sample sizes were accordingly adequate in terms of the factor analyses. EFA and CFA were conducted over the same sample in 36 papers, whereas the analyses were based on different samples in 22 ones. In terms of estimation, 65% of 117 EFA using papers employed Principle Component Analysis (PCA), 2% used Maximum Likelihood Estimation (MLE), 1% used Parallel Analysis (PA), and 3% used Principle Axis Factoring (PAF), while 29% did not specify their method of estimation. The hypotheses of EFA analyses were examined in 93% of the papers. Scree plots were taken into consideration when determing the number of factors in 50% of the papers. Varimax rotation method was used in 67% of the EFA using papers. The total variance explained was 53.05%. The analytical hypotheses were not examined in 87% of the 62 studies conducted with CFA; 51 of them included track diagrams and 52 included factor loadings. 90% of the papers did not give any information on parameter estimation method. All indices, except the Parsimony Goodness of Fit Index (PGFI), were acceptable as model fit indices in the papers. The results from the papers examined were evaluated in the light of the relevant literature in the last section of the present paper, and some suggestions were made in reference to both theory and practice.
Modeling Intra-Relation in Math Word Problems with Different Functional Multi-Head Attentions

Several deep learning models have been proposed for solving math word problems (MWPs) automatically.Although these models have the ability to capture features without manual efforts, their approaches to capturing features are not specifically designed for MWPs.To utilize the merits of deep learning models with simultaneous consideration of MWPs' specific features, we propose a group attention mechanism to extract global features, quantity-related features, quantitypair features and question-related features in MWPs respectively.The experimental results show that the proposed approach performs significantly better than previous state-of-the-art methods, and boost performance from 66.9% to 69.5% on Math23K with training-test split, from 65.8% to 66.9% on Math23K with 5-fold cross-validation and from 69.2% to 76.1% on MAWPS.
Leveraging Local and Global Patterns for Self-Attention Networks

Self-attention networks have received increasing research attention.By default, the hidden states of each word are hierarchically calculated by attending to all words in the sentence, which assembles global information.However, several studies pointed out that taking all signals into account may lead to overlooking neighboring information (e.g.phrase pattern).To address this argument, we propose a hybrid attention mechanism to dynamically leverage both of the local and global information.Specifically, our approach uses a gating scalar for integrating both sources of the information, which is also convenient for quantifying their contributions.Experiments on various neural machine translation tasks demonstrate the effectiveness of the proposed method.The extensive analyses verify that the two types of contexts are complementary to each other, and our method gives highly effective improvements in their integration.
Determining Optimal Solar Power Plant Locations Based on Remote Sensing and GIS Methods: A Case Study from Croatia

In the last few years, the world has been turning to the exploitation of renewable energy sources due to increased awareness of environmental protection and increased consumption of fossil fuels. In this research, by applying geographic information systems and integrating them with multi-criteria decision making methods, an area suitable for the construction and exploitation of renewable energy sources is determined. The research uses not only climate, spatial, environmental, and geomorphological parameters but also socioeconomic parameters, population, unemployment, and number of tourist nights as well as electricity consumption. By applying spatial analysis, rasters of all parameters were created using GRASS GIS software. Using the analytic hierarchy process, the calculated rasters are assigned with weight coefficients, and the sum of all those rasters gives the final raster of optimal locations for the construction of solar power plants in Croatia. To test the accuracy of the obtained results, sensitivity analysis was performed using different weight coefficients of the parameters. From the sensitivity analysis results, as well as a histogram and statistical indicators of the three rasters, it is apparent that raster F1 gives the best results. The most decisive parameters in determining the optimal solar plant locations that result from this research are GHI, land cover, and distance to the electricity network.
Graph Theory-Based Approach to Accomplish Complete Coverage Path Planning Tasks for Reconfigurable Robots

Extensive studies regarding complete coverage problems have been conducted, but a few tackle scenarios where the mobile robot is equipped with reconfigurable modules.The reconfigurability of these robots creates opportunities to develop new navigation strategies with higher dexterity; however, it also simultaneously adds in constraints to the direction of movements.This paper aims to develop a valid navigation strategy that allows tetromino-based self-reconfigurable robots to perform complete coverage tasks.To this end, a novel graph theory-based model to simulate the workspace coverage and make use of dynamic programming technique for optimal path searching and adaptive robot morphology shifting algorithms is proposed.Moreover, the influence of algorithms starting variables on workspace coverage outcome is analyzed thoughtfully in this paper.The simulation results showed that the proposed method is capable of generating navigation paths throughout the workspace, which ensures complete workspace coverage while minimizing the total number of actions performed by the robot.
Empirical Estimation of Information Measures: A Literature Guide

We give a brief survey of the literature on the empirical estimation of entropy, differential entropy, relative entropy, mutual information and related information measures. While those quantities are of central importance in information theory, universal algorithms for their estimation are increasingly important in data science, machine learning, biology, neuroscience, economics, language, and other experimental sciences.
CensNet: Convolution with Edge-Node Switching in Graph Neural Networks

In this paper, we present CensNet, Convolution with Edge-Node Switching graph neural network, for semi-supervised classification and regression in graph-structured data with both node and edge features. CensNet is a general graph embedding framework, which embeds both nodes and edges to a latent feature space. By using line graph of the original undirected graph, the role of nodes and edges are switched, and two novel graph convolution operations are proposed for feature propagation. Experimental results on real-world academic citation networks and quantum chemistry graphs show that our approach has achieved or matched the state-of-the-art performance.
Deep Reinforcement Learning With Optimized Reward Functions for Robotic Trajectory Planning

To improve the efficiency of deep reinforcement learning (DRL)-based methods for robotic trajectory planning in the unstructured working environment with obstacles. Different from the traditional sparse reward function, this paper presents two brand-new dense reward functions. First, the azimuth reward function is proposed to accelerate the learning process locally with a more reasonable trajectory by modeling the position and orientation constraints, which can reduce the blindness of exploration dramatically. To further improve the efficiency, a reward function at subtask-level is proposed to provide global guidance for the agent in the DRL. The subtask-level reward function is designed under the assumption that the task can be divided into several subtasks, which reduces the invalid exploration greatly. The extensive experiments show that the proposed reward functions are able to improve the convergence rate by up to three times with the state-of-the-art DRL methods. The percentage increase in convergence means is 2.25%-13.22% and the percentage decreases with respect to standard deviation by 10.8%-74.5%.
Cryptographic Solutions for Cloud Storage: Challenges and Research Opportunities

While cloud computing is relatively mature and its potential benefits well understood by individual, industry and government consumers, a number of security and privacy concerns remain. Unsurprisingly, designing cryptographic solutions to ensure the security of cloud services and the privacy of data outsourced to the cloud remains an ongoing research area. This paper provides a critique of the wide range of cryptographic schemes designed for securing sensitive data in the cloud computing environment, as well as outlining the research opportunities in the use of cryptographic techniques in cloud computing.
Quantum-classical hybrid algorithm using an error-mitigating <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>N</mml:mi></mml:math> -representability condition to compute the Mott metal-insulator transition

Quantum algorithms for molecular electronic structure have been developed with lower computational scaling than their classical counterparts, but emerging quantum hardware is far from being capable of the coherence, connectivity, and gate errors required for their experimental realization. Here we propose a class of quantum-classical hybrid algorithms that computes the energy from a two-electron reduced density matrix (2-RDM). The 2-RDM is constrained by $N$-representability conditions, constraints for representing an $N$-electron wave function, which mitigate noise from the quantum circuit. We compute the strongly correlated dissociation of doublet ${\mathrm{H}}_{3}$ into three hydrogen atoms. The hybrid quantum-classical computer matches the energies from full configuration interaction to 0.1 kcal/mol, one-tenth of ``chemical accuracy,'' even in the strongly correlated limit of dissociation. Furthermore, the spatial locality of the computed one-electron RDM reveals that the quantum computer accurately predicts the Mott metal-insulator transition.
Augmenting Neural Response Generation with Context-Aware Topical Attention

Sequence-to-Sequence (Seq2Seq) models have witnessed a notable success in generating natural conversational exchanges.Notwithstanding the syntactically well-formed responses generated by these neural network models, they are prone to be acontextual, short and generic.In this work, we introduce a Topical Hierarchical Recurrent Encoder Decoder (THRED), a novel, fully data-driven, multi-turn response generation system intended to produce contextual and topic-aware responses.Our model is built upon the basic Seq2Seq model by augmenting it with a hierarchical joint attention mechanism that incorporates topical concepts and previous interactions into the response generation.To train our model, we provide a clean and high-quality conversational dataset mined from Reddit comments.We evaluate THRED on two novel automated metrics, dubbed Semantic Similarity and Response Echo Index, as well as with human evaluation.Our experiments demonstrate that the proposed model is able to generate more diverse and contextually relevant responses compared to the strong baselines.
DHNE: Network Representation Learning Method for Dynamic Heterogeneous Networks

Analyzing the rich information behind heterogeneous networks through network representation learning methods is signifcant for many application tasks such as link prediction, node classifcation and similarity research. As the networks evolve over times, the interactions among the nodes in networks make heterogeneous networks exhibit dynamic characteristics. However, almost all the existing heterogeneous network representation learning methods focus on static networks which ignore dynamic characteristics. In this paper, we propose a novel approach DHNE to learn the representations of nodes in dynamic heterogeneous networks. The key idea of our approach is to construct comprehensive historical-current networks based on subgraphs of snapshots in time step to capture both the historical and current information in the dynamic heterogeneous network. And then under the guidance of meta paths, DHNE performs random walks on the constructed historical-current graphs to capture semantic information. After getting the node sequences through random walks, we propose the dynamic heterogeneous skip-gram model to learn the embeddings. Experiments on large-scale real-world networks demonstrate that the embeddings learned by the proposed DHNE model achieve better performances than state-of-the-art methods in various downstream tasks including node classifcation and visualization.
Whole Heart and Great Vessel Segmentation in Congenital Heart Disease Using Deep Neural Networks and Graph Matching

Congenital heart disease (CHD) is the leading cause of mortality from birth defects, which occurs 1 in every 110 births in the United States. While various whole heart and great vessel segmentation frameworks have been developed in the literature, they are ineffective when applied to medical images in CHD, which have significant variations in heart structure and great vessel connections. To address the challenge, we leverage the power of deep learning in processing regular structures and that of graph algorithms in dealing with large variations, and propose a framework that combines both for whole heart and great vessel segmentation in CHD. Particularly, we first use deep learning to segment the four chambers and myocardium followed by blood pool, where variations are usually small. We then extract the connection information and apply graph matching to determine the categories of all the vessels. Experimental results using 68 3D CT images covering 14 types of CHD show that our method can increase Dice score by 12% on average compared with the state-of-the-art whole heart and great vessel segmentation method in normal anatomy. Our dataset is released to the public.
A Brief History of Chatbots

Unquestionably, we come across conversational algorithms or chatbots more and more frequently, in increasingly everyday situations; while buying a flight ticket, or clothes from an e-shop for example. This study presents a brief history of chatbots as a topic that is necessary for understanding this phenomenon, its externalities, paradoxes, and future prospects. Chatbots, as an expanded and practically deployed AI, have their recognisable history and logical development structure, which are summed up in the following article. The article presents the logical structure of development on selected programs from Eliza to Tay and Xiaoice chatbots.
Equivalence of approximation by convolutional neural networks and fully-connected networks

Convolutional neural networks are the most widely used type of neural networks in applications. In mathematical analysis, however, mostly fully-connected networks are studied. In this paper, we establish a connection between both network architectures. Using this connection, we show that all upper and lower bounds concerning approximation rates of fully-connected neural networks for functions $f \in \mathcal {C}$âfor an arbitrary function class $\mathcal {C}$âtranslate to essentially the same bounds concerning approximation rates of convolutional neural networks for functions $f \in \mathcal {C}^{\mathrm {equi}}$, with the class $\mathcal {C}^{\mathrm {equi}}$ consisting of all translation equivariant functions whose first coordinate belongs to $\mathcal {C}$. All presented results consider exclusively the case of convolutional neural networks without any pooling operation and with circular convolutions, i.e., not based on zero-padding.