Reviewerly/exHarmony huggingface dataset:
Contextual Joint Factor Acoustic Embeddings

Embedding acoustic information into fixed length representations is of interest for a whole range of applications in speech and audio technology. Two novel unsupervised approaches to generate acoustic embeddings by modelling of acoustic context are proposed. The first approach is a contextual joint factor synthesis encoder, where the encoder in an encoder/decoder framework is trained to extract joint factors from surrounding audio frames to best generate the target output. The second approach is a contextual joint factor analysis encoder, where the encoder is trained to analyse joint factors from the source signal that correlates best with the neighbouring audio. To evaluate the effectiveness of our approaches compared to prior work, two tasks are conducted-phone classification and speaker recognition - and test on different TIMIT data sets. Experimental results show that one of the proposed approaches outperforms phone classification baselines, yielding a classification accuracy of 74.1%. When using additional out-of-domain data for training, an additional 3% improvements can be obtained, for both for phone classification and speaker recognition tasks.
Two-Way Neural Machine Translation: A Proof of Concept for Bidirectional Translation Modeling Using a Two-Dimensional Grid

Neural translation models have proven to be effective in capturing sufficient information from a source sentence and generating a high-quality target sentence. However, it is not easy to get the best effect for bidirectional translation, i.e., both source-to-target and target-to-source translation using a single model. If we exclude some pioneering attempts, such as multilingual systems, all other bidirectional translation approaches are required to train two individual models. This paper proposes to build a single end-to-end bidirectional translation model using a two-dimensional grid, where the left-to-right decoding generates source-to-target, and the bottom-to-up decoding creates target-to-source output. Instead of training two models independently, our approach encourages a single network to jointly learn to translate in both directions. Experiments on the WMT2018 German↔English and Turkish↔English translation tasks show that the proposed model is capable of generating a good translation quality and has sufficient potential to direct the research.
Privacy-Preserving Polynomial Evaluation over Spatio-Temporal Data on an Untrusted Cloud Server

Nowadays, with the popularity of location-aware devices, multifarious applications based on the spatio-temporal data come forth in our lives. In these applications, a platform (enterprise) collects the users' spatio-temporal data based on which it recommends the top-k users (passengers) to the registered service providers (drivers). Outsourcing the tremendous scale of spatio-temporal data to cloud provides an economical way for the enterprises to implement their applications. In this paradigm, the third-party cloud server is not completely trustworthy. The collected spatio-temporal data can hold users' privacy, so it's a critical challenge to design a secure and efficient query mechanism for this scenario, such as the ride-hailing or the ride-sharing services. However, the existing solutions for the privacy-preserving kNN queries mainly focus on data privacy protection or computation complexity. There still lacks a practical privacy-preserving polynomial evaluation solution over the spatio-temporal data. In this paper, we propose a virtual road network structure to storage and index the spatio-temporal data in the road network and design a novel homomorphic encryption scheme based on Order-Revealing Encryption to enable an untrusted cloud server to execute the polynomial evaluation over the encrypted spatio-temporal data in the road network. We formally prove the security of the proposed scheme under the random oracle model. Extensive experiments on real world data demonstrate the effectiveness and efficiency of the proposed scheme over alternatives.
A novel domain activation mapping-guided network (DA-GNT) for visual tracking

Conventional convolution neural network (CNN)-based visual trackers are easily influenced by too much background information in candidate samples. Further, extreme imbalance of foreground and background samples has a negative impact on training the classifier, whereas features learned from limited data are insufficient to train the classifier. To address these problems, we propose a novel deep neural network for visual tracking, termed the domain activation mapping guided network (DA-GNT). First, we introduce the class activation mapping with weakly supervised localization in multi-domain to identify the most discriminative regions in the bounding box and suppress the background in the positive sample. Next, to further increase the discriminability of deep feature representation, we utilize an ensemble network to achieve a kind of multi-view feature representation and a channel attention mechanism for adaptive feature selection. Finally, we propose a simple but effective data augmentation method to further increase the positive samples for our network training. Extensive experiments on two widely used benchmark datasets demonstrate the effectiveness of the proposed tracking method against many state-of-the-art trackers. The novel DA-GNT is thus posited as a potential benchmark resource for the computer vision and machine learning research community.
Blockchain-based security algorithm on IoT framework for shielded communication in smart cities

This manuscript describes since the resources are heterogeneous, a smart city is vulnerable to several security attacks, and to devise an effective response, it is necessary to recognize certain risks and their potential implications. The framework is responsible for the data transfer security issues in the smart city to incorporate and maintain physical, social, and industrial systems to provide high-quality secure data transmission to its residents. The proposed method of integrating the Blockchain and Internet of Things (IoT) with a consensus algorithm framework (BCIoT-CAF) to overcome the issues and secure data sharing in smart cities. The data security is enforced by splitting the blockchain network into separate networks, with any channel consisting of a limited number of approved entities, the data stored in the cloud gate server, and analysis of data achieved by IoT applications. The consensus algorithm plays an important role in retaining the blockchain's speed, protection, and performance. Using an appropriate security algorithm, blockchain applications may greatly improve their performance. The modeling simulation of this research improves the speed, protection, and efficiency of 96% and tolerance error as 55%. The integrated evaluation results suggest that the proposed system integrates blockchain with IoT to provide a shielded communication platform in smart cities.
Prediction of courses score using Artificial Neural Network with Backpropagation algorithm

Abstract This study aims to predict the final score of courses of students who are undergoing education in higher education. In the future, this work will be used to inform students that there must be early anticipation in implementing the study of courses in order to get a satisfying final grade. The Artificial Neural Network with Backpropagation algorithm is used to solve the prediction problem for the final grade of this course. The dataset collected was 337 students who will be used for training and testing needs. Prediction variables used consisted of the value of attendance, assignments, midterms, and final exams. The network architecture using a multi-layer perceptron with three layers, namely the input layer, hidden layer, and output layers, with a 4-3-1 neuron pattern. The results showed that using a learning rate of 0.2 and repetition Epoch 1000 times, resulting in an RMSE value of 0.040929 and MSE of 0.001675 with an accuracy rate of 93.43%.
Augmented Reality technology - a sustainable element for Industry 4.0

Abstract Augmented Reality (AR) is a current, sustainable, efficient, and promising tool that will make an essential contribution to the development of the Industry 4.0 revolution. AR technology fully and effectively responds to the current extended digital age, in the last year and in the field of education, of learning at all levels. The applicability of AR technology is varied and complex: retail interior design, health, industry, logistics, etc. This paper show the applicability of AR technology in the learning process, from the perspective of the Industry 4.0 concept, highlighting the sustainable and efficient aspects on the economic performance and not only of the companies. The main conclusion of this research is that the use of modern digital technology (AR and VR) in learning in various fields, including education, must be based on the principles of ROI (Return on Investments) and SROI (Social Return on Investments).
Mining Implicit Equations From Data Using Gene Expression Programming

Symbolic regression is an active research topic that has various applications in data mining and knowledge discovery. Existing methods for symbolic regression mainly focus on mining explicit equations. In contrast, implicit equations are more flexible and powerful than explicit equations with regard to describing the relationships between variables in the given dataset. However, evaluating the quality of an implicit equation is more difficult than evaluating an explicit equation. The traditional method for implicit equation evaluation is based on derivative calculation, which requires much time consumption and dense training data. To address the above issues, this article proposes a new evolutionary framework to efficiently mine implicit equations from data. In the proposed framework, a new mechanism (named CL-FEM) is proposed to evaluate implicit equations, and it can efficiently evaluate the accuracy and validity of implicit equations. In addition, a multiple sub-chromosome encoding method based on a least-squares estimator is proposed in the framework to further improve its search efficiency. Based on the proposed evolutionary framework, an efficient algorithm named gene expression programming with a least-squares estimator (LSE-GEP) is developed to mine implicit equations from data. Experimental results demonstrate that the proposed LSE-GEP method performs much better than the recently published methods, in terms of its success rate, accuracy and readability.
Semantic interoperability: the future of healthcare

Healthcare interoperability has been a major challenge since the early 1980s and still remains an unsolved issue. Attempts in the past have addressed this problem using bespoke solutions, but a generic solution still eludes the healthcare community. This chapter discusses the various levels at which interoperability needs to be preserved for an uninterrupted exchange of healthcare information. The chapter reviews the various healthcare standards developed in an attempt to solve the interoperability problem at a syntactic level and then moves on to examine medical ontologies developed to solve the problem at a semantic level. The chapter explains the features of semantic web technology that can be leveraged at each level. A literature survey is carried out to gage the current contribution of semantic web technologies in this area along with an analysis of how semantic web technologies can be improved to better suit the health informatics domain and solve the healthcare interoperability challenge.
Efficient corpus design for wake-word detection

Wake-word detection is an indispensable technology for preventing virtual voice agents from being unintentionally triggered. Although various neural networks were proposed for wake-word detection, less attention has been paid to efficient corpus design, which we address in this study. For this purpose, we collected speech data via a crowdsourcing platform and evaluated the performance of several neural networks when different subsets of the corpus were used for training. The results reveal the following requirements for efficient corpus design to produce a lower misdetection rate: (1) short segments of continuous speech can be used as negative samples, but they are not as effective as random words; (2) utterances of "adversarial" words, i.e., phonetically similar words to a wake-word, contribute to improving performance significantly when they are used as negative samples; (3) it is preferable for individual speakers to provide both positive and negative samples; (4) increasing the number of speakers is better than increasing the number of repetitions of a wake-word by each speaker.
Experimental analysis of non-periodic sound regimes in flute-like musical instruments

Self-sustained musical instruments are complex nonlinear dynamical systems that are known to produce a wealth of dynamical regimes. This includes different kinds of non-periodic sounds, which are either played on purpose or avoided depending on the cultural and musical context. We investigate non-periodic sounds produced by two types of flute-like instruments, namely, an alto recorder and traditional pan-like flutes from Central Chile. We adopt a nonlinear dynamics point of view to characterize the multiphonics produced by the alto recorder and the sonidos rajados produced by the Chilean flutes. Our results unveil the common quasiperiodic nature of the two types of sound regimes and suggest that they result from a similar physical sound production mechanism. This paves the way for a better control of non-periodic sound regimes by the instrument makers.
Whole Slide Images Based Cervical Cancer Classification Using Self-supervised Learning and Multiple Instance Learning

Currently, most whole slide images classification models rely on manual pixel-level annotations, which requires specific domain experts to annotate that is delicate and time-consuming. To overcome this problem, we propose to combine self-supervised learning with multiple instance learning to deal with large WSIs datasets only with the reported diagnoses as labels. In WSIs classification task, it’s a key challenge to learn good image representation, where self-supervised learning has held tremendous potential. In our study, we propose to use self-supervised learning network Bootstrap Your Own Latent as the pre-trained network, which can be trained using unlabeled data and learn the deep domain-specific features. We evaluated our proposed framework at scale on a uterine cervical dataset of 3,063 whole slide images(720GB). Our results have shown that the combination of self-supervised learning model and multiple instance learning model can match and exceed the performance of former methods.
Development of Morphological Segmentation for the Kyrgyz Language on Complete Set of Endings

The problem of word segmentation of source texts in the training of neural network models is one of the actual problems of natural language processing. A new model of the morphology of the Kyrgyz language based on a complete set of endings (CSE) is developed. Based on the developed CSE-model of the morphology of the Kyrgyz language, a computational data model, algorithm and a program for morphological segmentation are developed. Experiments on morphological segmentation of Kyrgyz language texts showed 82% accuracy of text segmentation by the proposed method.
Overview of PAN 2021: Authorship Verification, Profiling Hate Speech Spreaders on Twitter, and Style Change Detection

The paper gives a brief overview of the three shared tasks to be organized at the PAN 2021 lab on digital text forensics and stylometry hosted at the CLEF conference. The tasks include authorship verification across domains, author profiling for hate speech spreaders, and style change detection for multi-author documents. In part the tasks are new and in part they continue and advance past shared tasks, with the overall goal of advancing the state of the art, providing for an objective evaluation on newly developed benchmark datasets.
Texture-Based Face Recognition Using Grasshopper Optimization Algorithm and Deep Convolutional Neural Network

Face recognition is an active research area in biometric authentication, which has gained more attention among researchers due to the availability of feasible technologies, including mobile solutions. However, the human facial images are high dimensional, so the dimensionality reduction methods are often adapted for face recognition. However, the facial images are corrupted by the noise and hard to label in the data collection phase. In this study, a new GOA-DCNN model is proposed for face recognition to address those issues. Initially, the face images are collected from two online datasets FEI face and ORL. Next, modified local binary pattern (MLBP) and speeded up robust features (SURF) are used to extract the feature vectors from the collected facial images. The extracted feature values are optimized using grasshopper optimization algorithm (GOA) to decrease the dimensionality of data or to select the optimal feature vectors. At last, deep convolutional neural network (DCNN) was applied to classify the person’s facial image. The experimental result proves that the proposed model improved recognition accuracy up to 1.78–8.90% compared to the earlier research works such as improved kernel linear discriminant analysis and probabilistic neural networks (IKLDA + PNN) and convolutional neural network (CNN) with pre-trained VGG-Face.
Cloud Telemetry Modeling via Residual Gauss-Markov Random Fields

Can probabilistic graphical models characterize cloud telemetry? This paper promotes the affirmative view. Cloud systems are large, connected, and dynamic. Consequently, databased techniques to model their telemetry are high-dimensional, spatial, and unsupervised. Undirected probabilistic graphical models seem natural, but remain unexplored. We discuss one way around the limitation that cloud measurements violate usual assumptions of normality, and give a tractable estimation procedure for a candidate data model. As a preliminary test, we fit the model and use it to detect and localize anomalies in a synthetic environment and for a small-scale software system.
An Efficient Multi-Object Tracking and Counting Framework Using Video Streaming in Urban Vehicular Environments

Object counting is an active research area that gained more attention in the last few years. Since deep learning methods outperformed all other object detection algorithms, the design of efficient object counting algorithms became more realistic and achievable. Numerous algorithms targeting various challenges associated with object counting have been introduced. In a smart transportation system, vehicle counting plays a crucial role as it helps in creating autonomous systems, and better planning for roads. In this paper, we present an efficient object counting system and assess its performance using a dataset of 20 different videos. The proposed system leverage an efficient object detector, and object tracker to perform the counting. This paper combines different approaches to count objects by tracking them, but performs the tracking operation efficiently. Therefore, the proposed systems achieve high accuracy values with low processing time.
A Privacy Preserving E-Voting System Based on Blockchain

An electronic voting system can enable greater democracy by allowing virtual and remote voting and facilitating greater participation. However, the e-voting systems have experienced allegations due to corruption and unfair practices including voter impersonation, fraud, or duplicate votes. This has caused a decline in transparency and faith in the electoral process, resulting in reduced participation of citizens in the democratic process. To address these challenges, this paper proposes a blockchain-based e-voting system to ensure the fairness of the electoral process and restore people's faith while protecting the users' privacy. First, we utilize face recognition technology to authenticate voters after extracting feature sets from images while users actively make actions. Second, we design a smart contract to secure voter information during the election. Our approach involves analyzing the limitations of the existing electoral voting system and applies the blockchain-based solution to the vulnerable aspects of the existing system. We implement our approach by using Ethereum, smart contract, and OpenCV Haar Cascade detection classifiers, and our evaluation based on the prototype shows that our approach is effective and efficient.
Augmented Reality Application to Assist in On-Field Activities on the Electrical Sector

Workforces in electrical power sector is highly qualified. It is due to the continuous improvement effort of these professionals and many rules they have to follow daily. Their activity presents risks, and it needs to be done carefully to safeguard their life. The electrical power sector has a short history and it has been considered a conservative sector, meaning, that the assets and the way they are operated have not been following all technological trends in industry. Those analysis lead to a major concern regarding the aging of workforces and how to transfer knowledge and experiences to a new workforce. In that sense, this paper proposes a smartphone application aiming to simplify the access to information during the execution of the on-field activities. The aforementioned app can automatically recognize some assets and show relevant information about them in Augmented Reality. Some benefits brought by this application include reducing time for asset identification, faster information finding, improving and agile problem solving avoiding the use of paper documents, etc. This application is implemented within the scope of Urban Futurability Project, an innovation project that ENEL Distribuiç ão Sao Paulo is carrying out in Brazil in the framework of the Brazilian regulator's R&D) program.
Pre-computation Scheme of Window $$\tau $$NAF for Koblitz Curves Revisited

Let $$E_a/ \mathbb {F}_{2}: y^2+xy=x^3+ax^2+1$$ be a Koblitz curve. The window $$\tau $$ -adic non-adjacent form (window $$\tau $$ NAF) is currently the standard representation system to perform scalar multiplications on $$E_a/ \mathbb {F}_{2^m}$$ utilizing the Frobenius map $$\tau $$ . This work focuses on the pre-computation part of scalar multiplication. We first introduce $$\mu \bar{\tau }$$ -operations where $$\mu =(-1)^{1-a}$$ and $$\bar{\tau }$$ is the complex conjugate of $$\tau $$ . Efficient formulas of $$\mu \bar{\tau }$$ -operations are then derived and used in a novel pre-computation scheme. Our pre-computation scheme requires 6M $$\,+\,6$$ S, 18M $$\,+\,17$$ S, 44M $$\,+\,32$$ S, and 88M $$\,+\,62$$ S ( $$a=0$$ ) and 6M $$\,+\,6$$ S, 19M $$\,+\,17$$ S, 46M $$\,+\,32$$ S, and 90M $$\,+\,62$$ S ( $$a=1$$ ) for window $$\tau $$ NAF with widths from 4 to 7 respectively. It is about two times faster, compared to the state-of-the-art technique of pre-computation in the literature. The impact of our new efficient pre-computation is also reflected by the significant improvement of scalar multiplication. Traditionally, window $$\tau $$ NAF with width at most 6 is used to achieve the best scalar multiplication. Because of the dramatic cost reduction of the proposed pre-computation, we are able to increase the width for window $$\tau $$ NAF to 7 for a better scalar multiplication. This indicates that the pre-computation part becomes more important in performing scalar multiplication. With our efficient pre-computation and the new window width, our scalar multiplication runs in at least 85.2% the time of Kohel's work (Eurocrypt'2017) combining the best previous pre-computation. Our results push the scalar multiplication of Koblitz curves, a very well-studied and long-standing research area, to a significant new stage.
Hand Gesture Recognition and Voice Conversion for Speech Impaired

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