Reviewerly/exHarmony huggingface dataset:
Gender Classification Using Sentiment Analysis and Deep Learning in a Health Web Forum

Sentiment analysis is the most common text classification tool that analyzes incoming messages and tells whether the underlying sentiment is positive, negative, or neutral. We can use this technique to understand people by gender, especially people who are suffering from a sensitive disease. People use health-related web forums to easily access health information written by and for non-experts and also to get comfort from people who are in a similar situation. The government operates medical web forums to provide medical information, manage patients’ needs and feelings, and boost information-sharing among patients. If we can classify people’s emotional or information needs by gender, age, or location, it is possible to establish a detailed health policy specialized into patient segments. However, people with sensitive illness such as AIDS tend to hide their information. Especially, in the case of sexually transmitted AIDS, we can detect problems and needs according to gender. In this work, we present a gender detection model using sentiment analysis and machine learning including deep learning. Through the experiment, we found that sentiment features generate low accuracy. However, senti-words give better results with SVM. Overall, traditional machine learning algorithms have a high misclassification rate for the female category. The deep learning algorithm overcomes this drawback with over 90% accuracy.
Toward a Matrix-Free Covariance Matrix Adaptation Evolution Strategy

In this paper, we discuss a method for generating new individuals such that their mean vector and the covariance matrix are defined by formulas analogous to the covariance matrix adaptation evolution strategy (CMA-ES). In contrast to CMA-ES, which generates new individuals using multivariate Gaussian distribution with an explicitly defined covariance matrix, the introduced method uses combinations of difference vectors between archived individuals and univariate Gaussian random vectors along directions of past shifts of the population midpoints. We use this method to formulate the differential evolution strategy (DES)-an algorithm that is a crossover between differential evolution (DE) and CMA-ES. The numerical results presented in this paper indicate that DES is competitive against CMA-ES in performing both local and global optimization.
A local mean and variance active contour model for biomedical image segmentation

To effectively segment the biomedical images with intensity inhomogeneity, a local mean and variance (LMV) active contour model is presented in this paper. In the model, the distribution of intensity belonging to each region is assumed as a Gaussian distribution with spatially varying mean and variance. At the same time, an energy function is defined and incorporated into a variational level set formulation. The curve evolution equation is then obtained from energy minimization. Because both image local mean and variance are considered, the proposed model can more effectively deal with the images with intensity inhomogeneity than the existing active contour model. Experimental results on synthetic and real images show the advantages of our method in terms of both effectiveness and robustness. Compared with the local binary fitting (LBF) model and the local hybrid image fitting (LHIF) model, our model is much most computationally efficient and much least sensitive to the initial contour.
An Axiomatic Approach to Diagnosing Neural IR Models

Traditional retrieval models such as BM25 or language models have been engineered based on search heuristics that later have been formalized into axioms. The axiomatic approach to information retrieval (IR) has shown that the effectiveness of a retrieval method is connected to its fulfillment of axioms. This approach enabled researchers to identify shortcomings in existing approaches and "fix" them. With the new wave of neural net based approaches to IR, a theoretical analysis of those retrieval models is no longer feasible, as they potentially contain millions of parameters. In this paper, we propose a pipeline to create diagnostic datasets for IR, each engineered to fulfill one axiom. We execute our pipeline on the recently released large-scale question answering dataset WikiPassageQA (which contains over 4000 topics) and create diagnostic datasets for four axioms. We empirically validate to what extent well-known deep IR models are able to realize the axiomatic pattern underlying the datasets. Our evaluation shows that there is indeed a positive relation between the performance of neural approaches on diagnostic datasets and their retrieval effectiveness. Based on these findings, we argue that diagnostic datasets grounded in axioms are a good approach to diagnosing neural IR models.
Adversarial Training for Speech Super-Resolution

Speech super-resolution or speech bandwidth expansion aims to upsample a given speech signal by generating the missing high-frequency content. In this paper, we propose a deep neural network approach exploiting the adversarial training ideas that have been shown effective in image super-resolution. Specifically, our proposed network follows the generative adversarial networks setup, where the generator network uses a convolutional autoencoder architecture with one-dimensional convolution kernels to generate high-frequency log-power spectra from the low-frequency log-power spectra of the input speech. We propose to use both the reconstruction loss and the adversarial loss for training, and we employ a recent regularization method that penalizes the gradient norms of the discriminator to stabilize the training. We compare our proposed approach with two state-of-the-art neural network baselines and evaluate these methods with both objective speech quality measures and subjective perceptual and intelligibility tests. Results show that our proposed method outperforms both baselines in terms of both objective and subjective evaluations. To gain insights of the network architecture, we analyze key parameters of the proposed network including the number of layers, the number of convolution kernels, and the relative weight of the reconstruction and adversarial losses. Besides, we analyze the computational complexity of our method and the baselines and discuss ways for phase estimation. We further develop a noise-resilient version of the proposed approach by training the network with noisy speech inputs. Objective evaluation validates the noise-resilient property on unseen noise types.
Forensic classification of black inkjet prints using Fourier transform near-infrared spectroscopy and Linear Discriminant Analysis

This work presents a study regarding the forensic discrimination of black inkjet-printed documents in question. Nondestructive Fourier transform near-infrared (FT-NIR) spectroscopy in combination with supervised classification method Discriminant Analysis (DA); Linear Discriminant Analysis (LDA) and Quadratic Discriminant Analysis (QDA) were utilized to investigate 22 different prints of the three most sold office printer brands. The spectra were acquired using the FT-NIR spectrometer NIRFlex N-500 (Büchi Labortechnik AG, Flawil, Switzerland) with the Fiber Optic Solids measuring cell in the spectral region of 10,000-4000 cm-1. Each sample was printed on the same type of office paper. The spectra were 45 times acquired on 3 separate printed squares of each sample. It results in 990 acquired spectra for the presented experiment. The FT-NIR spectra of the printed squares were split into calibration and test sets with which the Classification accuracy (CA) value of unknown samples was evaluated. In order to increase the significance, three different compilations of calibration and test sets were realized. The performance of three different Discriminant model methods; LDA (Euclidean and Mahalanobis algorithm) and QDA were compared to each other. Furthermore, the CA of each DA method was examined using 1-5 principal components (PCs) in the construction of the respective DA model. Two groups of models, according to the ink subset (Carbon black and Black colorant), were performed in raw and Standard Normal Variate (SNV) correction spectra alternations. The results showed that the Euclidean method yielded the highest accuracy in predicting independent test samples and thus clearly outperformed the QDA and Mahalanobis algorithm DA method. It was also determined that the ink type Carbon black had higher CA values than the ink type Black colorant. This work demonstrated the special ability of FT-NIR spectroscopy in combination with DA to examine inkjet-printed documents in a fast and non-destructive fashion.
Special aspects of digital technology-based brand promotion

Experience economy and digital economy inside of which new level of 'brand-targeted public' communication is being shaped assume significant importance in new market conditions. Empirical marketing and digital marketing contribute to establishment of long-term relations with consumers and shaping of stable links in the market. Empirical marketing having its own specificity is laying emphasis on consumers' emotional experience and feelings while digital marketing using digital tools is providing address linkage and mobile communication with brand's targeted public. Under such conditions, educational institutions may achieve significant success in university brand promotion by combining digital technologies and cooperation with art and culture industries and thus provide the university with competitive advantages in the market of education services and raise customer loyalty. The article is addressing the process of cooperation between Russian state hydrometeorological University (RGGMU) and Russian Museum within the project entitled 'Virtual museum' and (analyzing synergetic effect produced by their joint activities performed with the use of digital technologies.
Interprofessional empathy and communication competency development in healthcare professions’ curriculum through immersive virtual reality experiences

Virtual Reality 360 (VR) Video for student learning was implemented into a face-to-face interprofessional course. A blend of undergraduate and graduate students from eight health sciences professions were represented. The objectives for this experience were: 1) students will be able to develop empathy and communication competency as a result of increased presence and immersion during VR 360 simulation and 2) students will be able to report increased communication competency in a health team as a result of participation in interprofessional training focused on communication. Students were provided with virtual reality headsets and an existing virtual reality simulation that placed them in the role of the patient. Following the VR viewing, participants provided feedback and learning was assessed via focus groups. Focus group data showed that the participants had positive learning, felt presence and demonstrated empathy through this immersive experience. Generation Z students are tech savvy and want to learn in a technology-enhanced environment. Students reported an increase in their perceptions of learning via VR.
Privacy-preserving Cross-domain Location Recommendation

Cross-domain recommendation is a typical solution for data sparsity and cold start issue in the field of location recommendation. Specifically, data of an auxiliary domain is leveraged to improve the recommendation of the target domain. There is a typical scenario that two interaction domains (location based check-in service, for example) combine data to perform the cross-domain location recommendation task. Existing approaches are based on the assumption that the interaction data from the auxiliary domain can be directly shared across domains. However, such an assumption is not reasonable, since in the real world those domains may be operated by different companies. Therefore, directly sharing raw data may violate business privacy policy and increase the risk of privacy leakage since the user-location interaction records are very sensitive. In this paper, we propose a framework named privacy-preserving cross-domain location recommendation which works in two stages. First, for the interaction data from the auxiliary domain, we adopt a differential privacy based protection mechanism to hide the real locations of each user to meet the criterion of differential privacy. Then we share the protected user-location interaction to the target domain. Second, we develop a new method of Confidence-aware Collective Matrix Factorization (CCMF) to effectively exploit the transferred interaction data. To verify its efficacy, we collect two real-world datasets suitable for the task. Extensive experiments demonstrate that our proposed framework achieves the best performance compared with the state-of-the-art baseline methods. We further demonstrate that our method can alleviate the data sparsity issue significantly while protecting users' location privacy.
Comparative evaluation of consensus mechanisms in cryptocurrencies

The Bitcoin cryptocurrency allows entities in a peer‐to‐peer network to reach consensus about the state of the blockchain through a set of cryptographic algorithms and economic incentives. Bitcoin uses a proof of work system, but there are other consensus mechanisms used by other cryptocurrencies. The performance of a cryptocurrency depends on the consensus mechanism used. This paper presents the results of a comparative evaluation that has been conducted on various significant consensus mechanisms. The unique characteristics, as well as drawbacks of each mechanism, are highlighted, together with their applications. The mechanisms are also classified into major types and those types are evaluated elaborately. The paper concludes with a discussion of the open challenges related to the available solutions.
Designated Verifier/Prover and Preprocessing NIZKs from Diffie-Hellman Assumptions

In a non-interactive zero-knowledge (NIZK) proof, a prover can non-interactively convince a verifier of a statement without revealing any additional information. Thus far, numerous constructions of NIZKs have been provided in the common reference string (CRS) model (CRS-NIZK) from various assumptions, however, it still remains a long standing open problem to construct them from tools such as pairing-free groups or lattices. Recently, Kim and Wu (CRYPTO'18) made great progress regarding this problem and constructed the first lattice-based NIZK in a relaxed model called NIZKs in the preprocessing model (PP-NIZKs). In this model, there is a trusted statement-independent preprocessing phase where secret information are generated for the prover and verifier. Depending on whether those secret information can be made public, PP-NIZK captures CRS-NIZK, designated-verifier NIZK (DV-NIZK), and designated-prover NIZK (DP-NIZK) as special cases. It was left as an open problem by Kim and Wu whether we can construct such NIZKs from weak paring-free group assumptions such as DDH. As a further matter, all constructions of NIZKs from Diffie-Hellman (DH) type assumptions (regardless of whether it is over a paring-free or paring group) require the proof size to have a multiplicative-overhead $$|C| \cdot \mathsf {poly}(\kappa )$$ , where |C| is the size of the circuit that computes the $$\mathbf {NP}$$ relation. In this work, we make progress of constructing (DV, DP, PP)-NIZKs with varying flavors from DH-type assumptions. Our results are summarized as follows: Along the way, we construct two new homomorphic authentication (HomAuth) schemes which may be of independent interest.
Privacy-Preserving and Truthful Double Auction for Heterogeneous Spectrum

Over the past decades, there have been extensive research endeavors in spectrum auction design. However, most solutions only focus on the allocation efficiency while ignoring the privacy leakage inherent in the process of spectrum auction. So far, the very few existing works on secure spectrum auctions either provide inadequate privacy protection or incur performance loss in terms of spectrum reusability. In this paper, for the first time, we propose PS-TAHES, a privacy-preserving and truthful double auction mechanism for heterogeneous spectrum. PS-TAHES is constructed based on our carefully designed security primitives, which can support various arithmetics over encrypted data, including multiplication, bid comparison, and sorting matrix, and they are well applicable in other contexts. We theoretically analyze the security and efficiency of PS-TAHES, which is proved to ensure a full and strong privacy protection for bidders while preserving the allocation efficiency of the original auction mechanism. Experimental results, consistent with the theoretical analysis, further validate the practical use of PS-TAHES in real-world applications.
Online Asymmetric Metric Learning With Multi-Layer Similarity Aggregation for Cross-Modal Retrieval

Cross-modal retrieval has attracted intensive attention in recent years, where a substantial yet challenging problem is how to measure the similarity between heterogeneous data modalities. Despite using modality-specific representation learning techniques, most existing shallow or deep models treat different modalities equally and neglect the intrinsic modality heterogeneity and information imbalance among images and texts. In this paper, we propose an online similarity function learning framework to learn the metric that can well reflect the cross-modal semantic relation. Considering that multiple CNN feature layers naturally represent visual information from low-level visual patterns to high-level semantic abstraction, we propose a new asymmetric image-text similarity formulation which aggregates the layer-wise visual-textual similarities parameterized by different bilinear parameter matrices. To effectively learn the aggregated similarity function, we develop three different similarity combination strategies, i.e., average kernel, multiple kernel learning, and layer gating. The former two kernel-based strategies assign uniform weights on different layers to all data pairs; the latter works on the original feature representation and assigns instance-aware weights on different layers to different data pairs, and they are all learned by preserving the bi-directional relative similarity expressed by a large number of cross-modal training triplets. The experiments conducted on three public datasets well demonstrate the effectiveness of our methods.
Are query-based ontology debuggers really helping knowledge engineers?

Real-world semantic or knowledge-based systems can become large and complex, e.g., in the biomedical domain. Tool support for the localization and repair of faults within knowledge bases of such systems can therefore be essential for their practical success. Correspondingly, a number of knowledge base debugging approaches, in particular for ontology-based systems, were proposed in recent years. Query-based debugging is a comparably recent interactive approach that localizes the true cause of an observed problem by asking knowledge engineers a series of questions. Concrete implementations of this approach exist, such as the OntoDebug plug-in for the ontology editor Protégé. To validate that a newly proposed method is favorable over an existing one, researchers often rely on simulation-based comparisons. Such an evaluation approach however has certain limitations and often cannot fully inform us about a method’s true usefulness. We therefore conducted a range of user studies to assess the practical value of query-based ontology debugging. One main insight from the studies is that the considered interactive approach is indeed more efficient than an alternative algorithmic debugging based on test cases. We also observed that users frequently made errors in the process, which highlights the importance of a careful design of the queries that users need to answer.
Binary Whale Optimization Algorithm and Binary Moth Flame Optimization with Clustering Algorithms for Clinical Breast Cancer Diagnoses

Models based on machine learning algorithms have been developed to detect the breast cancer disease early. Feature selection is commonly applied to improve the performance of these models through selecting only relevant features. However, selecting relevant features in unsupervised learning is much difficult. This is due to the absence of class labels that guide the search for relevant information. This kind of the problem has rarely been studied in the literature. This paper presents a hybrid intelligence model that uses the cluster analysis algorithms with bio-inspired algorithms as feature selection for analyzing clinical breast cancer data. A binary version of both moth flame optimization and whale optimization algorithm is proposed. Two evaluation criteria are adopted to evaluate the proposed algorithms: clustering-based measurements and statistics-based measurements. The experimental results positively demonstrate that the capability of the proposed bio-inspired feature selection algorithms to produce both meaningful data partitions and significant feature subsets.
Chatbot Personalities Matters

In this study, we investigated the impact of a match in personality between a chatbot and the user. Previous research have proposed that personality can offer a stable pattern to how chatbots are perceived, and add consistency to the user experience. The assumptions regarding the effects of personality was investigated by measuring the effects of two chatbot agents, with two levels of personality, on the user experience. This study found that personality has a significant positive effect on the user experience of chatbot interfaces, but this effect is dependent on context, the job it performs, and its user group.
Comparative Research of Random Search Algorithms and Evolutionary Algorithms for the Optimal Control Problem of the Mobile Robot

An experimental comparison of evolutionary algorithms and random search algorithms for the optimal control problem is carried out. The problem is solved separately by several representatives of each type of algorithms. The simulation is performed on a mobile robot model. The results of each algorithm performance are compared according to the best found value of the fitness function, the mean value and the standard deviation.
Accuracy Analysis of Feature-based Automatic Modulation Classification with Blind Modulation Detection

The process of automatic classification of a detected signal's employed modulation type has gained importance in recent years. The goal of such an approach is to maximize the achievable throughput for intelligent receiver designs in civilian applications as well as jamming malicious signals in military applications. Automatic Modulation Classification (AMC) increases in difficulty since there is no a-priori knowledge of transmitted signal properties, such as signal power, carrier frequency, or bandwidth, nor any associated link properties such as channel state information (CSI), noise characteristics, signal-to-noise ratio (SNR) or any offset in frequency and phase. The most complex, albeit also most realistic, scenarios for AMC are faced when considering Non-Gaussian noise with multipath fading in frequency selective and time-varying channels. Different methods have been proposed in the literature to estimate unknown signals and channel parameters for AMC. However, a key consideration in selecting among them is attaining low computational complexity in order for AMC to become a technique feasible for real-time applications. Predominantly, blind AMC and associated parameter estimation utilizes feature-based approaches, owing to their low-complexity calculations of statistical values. In this work, we have analyzed the accuracy of High-order Statistics-based (HoS) methods utilizing feature extraction approaches, Support Vector Machine classifiers, and estimation techniques to determine an optimized framework for different real-time applications.