Reviewerly/exHarmony huggingface dataset:
Attention based gender and nationality information exploration for speaker identification

Gender and nationality information has not been exploited in large-scale speaker recognition despite being provided in the popular VoxCeleb1 dataset. This paper explores methods that combine high-level features extracted from the gender and nationality information with low-level acoustic features for speaker identification. To our knowledge, this is the first time that the gender and nationality information provided in VoxCeleb1 is utilized in speaker identification. Specifically, we propose Gender-Guided Spectrogram-Attention network and Nationality-Guided Spectrogram-Attention network that embed gender and nationality information into the spectrogram features, respectively. The resulting gender and nationality embeddings are then used with the spectrogram features together for classification. Experimental results show that the proposed methods can successfully capture the gender and nationality information of the speakers, and can effectively improve speaker identification accuracy.
Temporal Debiasing using Adversarial Loss based GNN architecture for Crypto Fraud Detection

The tremendous rise of cryptocurrency in the payment domain has unlocked huge opportunities but also raised numerous challenges in parallel involving cybercriminal activities like money laundering, terrorist financing, illegal and risky services, etc, owing to its anonymous and decentralized setup. The demand for building a more transparent cryptocurrency network, resilient to such activities, has risen extensively as more financial institutions look to incorporate it into their network. While a plethora of traditional machine learning and graph based deep learning techniques have been developed to detect illicit activities in a cryptocurrency transaction network, the challenge of generalization and robust model performance on future timesteps still exists. In this paper, we show that the model learned on transactional feature set provided in dataset (Elliptic Dataset) carry a temporal bias, i.e. they are highly dependent on the timesteps they occur. Deploying temporally biased models limits their performance on future timesteps. To address this, we propose a temporal debiasing technique using GNN based architecture that ensures generalization by adversarially learning between fraud <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> classification and temporal classification. The adversarial loss constructed optimizes the embeddings to ensure they 1.) perform well on fraud classification task 2.) does not contain temporal bias. The proposed architecture capture the underlying fraud patterns that remain consistent over time. We evaluate the performance of our proposed architecture on the Elliptic dataset and compare the performance with existing machine learning and graph-based architectures. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> Fraud and illicit are used interchangeably in this paper
Human Age and Gender Prediction using Machine Learning Algorithm

From the past few years, much exertion has been carried out for age assessment & recognition of gender through face images. The images of Human’s faces uncover different data like gender, age, and nationality. Computer vision and pattern recognition in the discipline of computer vision represent an ideal role in human beings through automatic detection of face, recognition of gesture and face, and finding age and gender. Here through the paper describes a survey on predicting age and gender using face images. Various research has been developed on age and gender prediction using various features like face features, body pose, MRI brain images, giants, and pam. The face features can give more features for age and gender identification.
Beyond exploitation: Measuring the impact of local search in swarm-based memetic algorithms through the interactions of individuals in the population

Memetic algorithms are known for their enhanced solution refinement capabilities. These capabilities are a result of incorporating local-search methods into population-based metaheuristics such as swarm and evolutionary algorithms. However, designing a memetic algorithm is not a trivial task. The inclusion of local-search procedures must consider the exploitation–exploration balance and its interplay with other algorithm operators. Due to these variables, there is no universal methodology to design a memetic algorithm. Although previous works have investigated the impact of local search procedures on genetic and ant colony algorithms, we have limited knowledge about the impact of these procedures on other types of swarm-based algorithms. For swarm-based algorithms, the interactions within the population are vital to the emergence of collective intelligence and shape the algorithm’s behaviour. Here, we model these interactions into a network and analyse the impact of local search in swarm-based algorithms. We selected the Particle Swarm Optimization (PSO), the Artificial Bee Colony (ABC), and one memetic version of each algorithm as a case study. We examined the effects of the modifications proposed in the memetic variants. The results obtained indicate that the networks of interactions capture several characteristics of the algorithms and the impact of the local search strategies. The impact of local search operators can be gauged by the temporal analysis of the changes in the structural properties of the algorithm’s network (e.g. study of the weight and distribution of the network’s connections). These changes are linked to the algorithms’ convergence signature and can be used as a proxy to assess the differences between the algorithms studied and their memetic versions.
Multi-Stream HiFi-GAN with Data-Driven Waveform Decomposition

Although a HiFi-GAN vocoder can synthesize high-fidelity speech waveforms in real time on CPUs, there is a tradeoff between synthesis quality and inference speed. To increase inference speed while maintaining synthesis quality, a multi-band structure is introduced to HiFi-GAN. However, it cannot be trained well because of the strong constraint imposed by the fixed multi-band structure. As an alternative approach, Multi-stream MelGAN and HiFi-GAN are proposed, in which the fixed synthesis filter in Multi-band MelGAN is replaced by a trainable convolutional layer with the same structure. In contrast to Multi-band MelGAN, the proposed methods use the trainable synthesis filter to decompose speech waveforms in a data-driven manner. To evaluate the proposed Multi-stream HiFi-GAN as an entire real-time neural text-to-speech system on CPUs, a fast acoustic model, based on Parallel Tacotron 2 with forced alignment and accentual label input, was implemented. The results of experiments-using Japanese male, female, and multi-speaker corpora-indicate that Multi-stream HiFi-GAN can increase synthesis speed while improving or maintaining synthesis quality in analysis-synthesis and text-to-speech conditions for single-speaker models and unseen speaker synthesis for multi-speaker models, compared with the original HiFi-GAN.
Targeting Online Network Branding of Russian Universities: Scope of Influence on Public Consciousness

The article presents the results of a media study of the effectiveness of targeting Russian universities’ activities in developing an online brand. The empirical analysis we carried out in two stages. At the first stage, we analysed content characteristics (context, topic, focus, impact technologies) of information media — digital representatives of Russian universities in the format of cognitive mapping. At the second stage, we accumulated social and media streams, which form the online brand of universities of the automated tool. As a result of the analysis of the metrics of digital services, we revealed the coverage of the user audience of universities, the sociodemographic characteristics of its representatives, the specificity of the resonance concerning university online content. Comparison of the structural and behavioural parameters of the user audience of universities in social media determines the effectiveness of the leading Russian groups in targeting target groups in the social media environment.
Maximum-Entropy Progressive State Aggregation for Reinforcement Learning

We propose a reinforcement learning algorithm based on an adaptive state aggregation scheme defined by a progressively growing set of codevectors placed in the joint state-action space according to a maximum-entropy vector quantization scheme. The proposed algorithm constitutes a two-timescale stochastic approximation algorithm with: (a) a fast component that executes a temporal-difference learning algorithm, and (b) a slow component, based on an online deterministic annealing algorithm, that adaptively partitions the state-action space according to a dissimilarity measure that belongs to the family of Bregman divergences. The proposed online deterministic annealing algorithm is a competitive-learning neural network that shows robustness with respect to the initial conditions, requires minimal hyper-parameter tuning, and provides online control over the performance-complexity trade-off. We study the convergence properties of the proposed methodology and quantify its performance in simulated experiments. Finally, we show that the generated codevectors can be used as training samples for sparse and progressively more accurate Gaussian process regression.
Blockchain-based system for e-voting using Blind Signature Protocol

Reports of possible third-party interference in the electoral process, unauthorized voting, disenfranchisement, and technical failure raise questions about the transparency of elections around the world. At the same time, e- voting has partially replaced the traditional paper ballot-based system, with the advantages of high human labor efficiency, low cost, and low probability of error in the process. However, their use has not been extended due to the security issues of the developed solutions. Blockchain technology has been proposed to support e-voting processes to ensure the integrity, resilience and verifiability of votes, but blockchain alone is not able to guarantee the anonymity and privacy demanded by voters. In this paper, we propose a voting mechanism that uses Blockchain and a cryptographic Blind Signature protocol to guarantee anonymity and privacy to voters. The performance in terms of computational complexity and scalability is evaluated via simulation. The analysis of simulation results shows that the blockchain-based voting mechanism proposed in this paper achieves good performance in terms of computational cost and scalability.
Spatial-temporal 3D dependency matching with self-supervised deep learning for monocular visual sensing

Monocular visual sensing is the task of using a camera to estimate the scene depth, optical flow and camera pose. In this paper, we propose a spatial–temporal 3D dependency matching approach that enforces the robustness of continuous frames matching for monocular visual sensing. 3D structure and warped depth based geometry backpropagation are used to encourage jointly learning the view depth, optical flow and camera pose employing a novel self-supervised neural network from monocular sequences. We designed two different iterative convolutional prediction sub-networks, where the optical flow obtained by depth and camera pose is iteratively used for depth prediction. A virtual frame method is proposed to optimize the optical flow of moving objects. The salient feature of the proposed learning framework is completely unsupervised, requiring only consecutive monocular images for training and testing. Evaluation on publicly benchmark datasets shows that our unsupervised learning model significantly outperforms previous methods and achieves better performance compared with previously unsupervised manners and achieves comparable results with supervised ones.
Development of research capacity of a future social pedagogue in the face of digital technologies

The aim of this article is to theoretically and practically justify the development of a future social pedagogue's research capacity in the context of digital technologies. Practical implementation of a model for building social pedagogues' research capacity within the framework of an online educational course can provide effective training of pedagogues in a digitalized educational environment and ensure a high level of pedagogical excellence. Approbation of the model was carried out on bachelor students majoring in Social Pedagogy and Self-Knowledge and taking an online Scientific Research Organization and Planning course in the Pavlodar Pedagogical University (Kazakhstan). The course was held from September to December 2020 on the Moodle learning platform, one of the most popular for the purposes of e-learning. The communication part of the course was supported by the social networking features of Facebook. In total, the educational experiment encompassed 52 students. Their thorough examination enabled the conclusion that research capacity brings proficient results in creative, innovative, communication, leadership, pedagogical, and digital activities, and thus remains an important component of pedagogical excellence. Thus, it can be stated that the integration of digital tools and platforms, along with the creation of multimedia learning content and the introduction of research methods and structured research practice into the students' education, allows for an effective research learning practice. The results of the students' end-of-course survey identified factors that had a significant influence on the effectiveness of research activities in online learning, namely the quality of the digital platform, structured research practice, active learning opportunities, pedagogical excellence, effective communication, and personalization.
Profile Aware ObScure Logging (PaOSLo): A Web Search Privacy-Preserving Protocol to Mitigate Digital Traces

Web search querying is an inevitable activity of any Internet user. The web search engine (WSE) is the easiest way to search and retrieve data from the Internet. The WSE stores the user’s search queries to retrieve the personalized search result in a form of query log. A user often leaves digital traces and sensitive information in the query log. WSE is known to sell the query log to a third party to generate revenue. However, the release of the query log can compromise the security and privacy of a user. In this work, we propose a Profile Aware ObScure Logging (PaOSLo) Web search privacy-preserving protocol that mitigates the digital traces a user leaves in Web searching. PaOSLo systematically groups users based on profile similarity. The primary objective of this work is to evaluate the impact of the systematic group compared to random grouping. We first computed the similarity between the users’ profiles and then clustered them using the K-mean algorithm to group the users systematically. Unlikability and indistinguishability are the two dimensions in which we have measured the privacy of a user. To compute the impact of systematic grouping on a user’s privacy, we have experimented with and compared the performance of PaOSLo with modern distributed protocols like OSLo and UUP(e). Results show that, at the top degree of the ODP hierarchy, PaOSLo preserved 10% and 3% better profile privacy than the modern distributed protocols mentioned above. In addition, the PaOSLo has less profile exposure for any group size and at each degree of the ODP hierarchy.
Co-saliency-regularized correlation filter for object tracking

Correlation filtering (CF) has emerged as one of the best object-tracking frameworks, achieving a good balance between tracking accuracy and speed. Based on this framework, many successful trackers have been developed. We propose a new co-saliency regularization method for visual object tracking based on the correlation filter, called the CRCF. To the best our knowledge, this is the first application of co-saliency regularization to CF-based tracking, in the context of widely used co-saliency detection and regularization methods. In the CRCF, the co-saliency information is extracted and introduced into the regularization component in the relevant tracking framework. The model first calculates the salient regions based on the feature differences between salient objects and background information. A co-saliency map is generated for the global correspondence of implicit learning between multiple images. To avoid traversing all the pixels, we use SLIC superpixel clustering to extract the objects saliency information, thus significantly reducing the computational overhead in the image processing step while retaining the image features and structural information. Finally, during the tracking process, it is possible to dynamically learn the co-saliency map configuration, highlight relevant object areas, and mitigate the impact of the lack of discriminatory representations on the performance. Quantitative and qualitative experimental results on OTB-2015, UAV123, LaSOT, GOT-10k and VOT-2018 datasets show that our CRCF outperforms the latest trackers.
Syntax and prejudice: ethically-charged biases of a syntax-based hate speech recognizer unveiled

Hate speech recognizers (HSRs) can be the panacea for containing hate in social media or can result in the biggest form of prejudice-based censorship hindering people to express their true selves. In this paper, we hypothesized how massive use of syntax can reduce the prejudice effect in HSRs. To explore this hypothesis, we propose Unintended-bias Visualizer based on Kermit modeling (KERM-HATE): a syntax-based HSR, which is endowed with syntax heat parse trees used as a post-hoc explanation of classifications. KERM-HATE significantly outperforms BERT-based, RoBERTa-based and XLNet-based HSR on standard datasets. Surprisingly this result is not sufficient. In fact, the post-hoc analysis on novel datasets on recent divisive topics shows that even KERM-HATE carries the prejudice distilled from the initial corpus. Therefore, although tests on standard datasets may show higher performance, syntax alone cannot drive the "attention" of HSRs to ethically-unbiased features.
On Secure Distributed Linearly Separable Computation

Distributed linearly separable computation, where a user asks some distributed servers to compute a linearly separable function, was recently formulated by the same authors and aims to alleviate the bottlenecks of stragglers and communication cost in distributed computation. The data center assigns a subset of input datasets to each server in an uncoded manner, and each server computes some coded packets on the assigned datasets, which are then sent to the user. The user should recover the task function from the answers of a subset of servers, such that the effect of stragglers could be tolerated. In this paper, we formulate a novel secure framework for this distributed linearly separable computation, where we aim to let the user only retrieve the desired task function without obtaining any other information about the input datasets, even if it receives the answers of all servers. In order to preserve the security of the input datasets, some common randomness variable independent of the datasets should be introduced into the transmission. We show that any non-secure linear-coding based computing scheme for the original distributed linearly separable computation problem, can be made secure without increasing the communication cost (number of symbols the user should receive). Then we focus on the case where the computation cost of each server (number of datasets assigned to each server) is minimum and aim to minimize the size of the randomness variable (i.e., randomness size) introduced in the system while achieving the optimal communication cost. We first propose an information theoretic converse bound on the randomness size. We then propose secure computing schemes based on two well-known data assignments, namely <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">fractional repetition assignment</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">cyclic assignment</i> . These schemes are optimal subject to using these assignments. Motivated by the observation of the general limitation of these two schemes on the randomness size, we propose a computing scheme with novel assignment, which strictly outperforms the above two schemes. Some additional optimality results are also obtained.
Sketch2Vis: Generating Data Visualizations from Hand-drawn Sketches with Deep Learning

Data visualization has become a vital tool to help people understand the driving forces behind real-world phenomena. Although the learning curve of visualization tools have been reduced, domain experts still often require significant amounts of training to use them effectively. To reduce this learning curve even further, this paper proposes Sketch2Vis, a novel solution using deep learning techniques and tools to generate the source code for multi-platform data visualizations automatically from hand-drawn sketches provided by domain experts, which is similar to how an expert might sketch on a cocktail napkin and ask a software engineer to implement the sketched visualization.This paper explores key challenges (such as model training) in generating visualization code from hand-drawn sketches since acquiring a large dataset of sketches paired with visualization source code is often prohibitively complicated. We present solutions for these problems and conduct experiments on three baseline models that demonstrate the feasibility of generating visualizations from hand-drawn sketches. The best models tested reach a structural accuracy of 95% in generating correct data visualization code from hand-drawn sketches of visualizations.
Binocular camera calibration based on timing correction

Camera calibration is crucial to the application of binocular measurement. However, many of the calibrations are carried out manually, so shaking of the calibration plate cannot be avoided. When the cameras are not synchronized or the exposure time is inconsistent, the image acquisition time of different cameras does not correspond strictly, which will introduce inevitable calibration errors. In this paper, two consecutive frames are collected instead of one frame, and the calibration data are corrected by the image acquisition time value returned by the camera. Experiments show that the proposed method can greatly reduce the re-projection error of calibration, and achieve higher calibration accuracy and less image quantity dependence.