Reviewerly/exHarmony huggingface dataset:
End-to-End Zero-Shot Voice Conversion Using a DDSP Vocoder

In this paper, we propose a zero-shot voice conversion algorithm using a neural vocoder based on differential digital signal processing. The vocoder does not require auto-regression, and its lightweight, differentiable nature allows the proposed system to be trained in an end-to-end fashion. This enables the use of more perceptually relevant objective functions for model training, and allows feature conversion and vocoder sub-networks to internally learn their own acoustic representation in a data-driven manner. We illustrate the effectiveness of the proposed algorithm by both qualitative and quantitative means, with comparisons to some of our previous works.
A New Sentence Similarity Computing Technique Using Order and Semantic Similarity

Techniques that detect sentence similarity have been a very important domain of research and lately many such techniques have been successfully implemented. With the use of Natural Language Processing (NLP) these techniques have been implemented more efficiently. The concept of semantic analysis is very significant in determining sentence similarity. The model proposed in this paper, deploys a NLP based methodology that works on the Sentence Involving Compositional Knowledge (SICK) dataset. The proposed methodology considers the set of sentencesto be a subset of words and it is split based on the semantic and syntactic structure. A lexical database is used by this model, unlike methods deployed by other models. This is followed by the computation of the word order vector. When this NLP based method is tested on the dataset, the accuracy obtained is 82.7% on the basis of mean absolute error. The obtained results are better than the previously used methods. Also, the proposed method is computationally faster than the existing methods.
Multi-label classification of feedbacks

This work deals with educational text mining, a field of natural language processing applied to education. The objective is to classify the feedback generated by teachers in online courses to the activities sent by students according to the model of Hattie and Timperley (2007), considering that feedback may be at the levels task, process, regulation, praise and other. Four multi-label classification methods of the data transformation approach - binary relevance, classification chains, power labelset and rakel-d - are compared with the base algorithms SVM, Random Forest, Logistic Regression and Naive Bayes. The methodology was applied to a case study in which 11013 feedbacks written in Spanish language from 121 online courses of the Law degree from a public university in Mexico were collected from the Blackboard learning manager system. The results show that the random forests algorithms and vector support machines will have the best performance when using the binary relevance transformation and classifier chains methods.
Physical Layer Authentication Using MmWave Channel State Information

This paper proposes an intelligent physical layer authentication scheme to learn the time-varying features of physical layer for mmWave MIMO communications. Explicitly, we design the physical layer authentication system as a binary classification model based on the kernel machine. We derive the updating methods of the system parameters iteratively. The convergence and performance of the proposed physical layer authentication model are validated by exploiting a mmWave MIMO channel model. Numerical results demonstrate that the proposed intelligent physical layer authentication scheme performs well in mmWave MIMO communications.
An Interdisciplinary Web-based Framework for Data-driven Placement Analysis of CCTV Cameras

This paper describes work in progress of an interdisciplinary research project that focuses on the placement and analysis of public close-circuit television (CCTV) cameras using data-driven analysis of crime data. A novel web-based prototype that acts as a framework for the camera placement analysis with regards to historical crime occurrence is presented. The web-based prototype enables various analyses involving public CCTV cameras e.g., to determine suitable locations for both stationary CCTV cameras as well as temporary cameras that are moved around after a few months to address crime seasonality. The framework also opens up for other analyses, e.g. automatically highlighting crimes that are carried out closed by at least one camera. The research also investigates to what extent it is possible to generate estimates on the amount of detail captured by a camera given the distance to the crime light conditions. The research project includes interdisciplinary competences from various areas such as criminology, computer and data science as well as the Swedish Police.
Research on UAV multi-aircraft path planning based on deep learning

Aiming at the difficulty of traditional unmanned and path planning methods to achieve the task execution of UAVs in complex mountain environments, a three-bit UAV based on multiple algorithms such as A-star algorithm, ant colony algorithm, interpolation algorithm and tolerance algorithm is proposed. Path planning algorithm. By optimizing the UAV path through the algorithm, the UAV path can finally be smoother and the body group coordination can be realized, which improves the airspace utilization rate and flight safety rate.
Method for Protection of PDF Documents against Counterfeiting using Semi-Fragile Watermarking

This paper proposes a new method for protection of PDF documents from counterfeiting, which also provides the ability to localize forged fragments. The method is based on embedding of digital watermark represented as QR code, which encodes a binary string, comprising a secret key and a page number. A unique binary digital image (QR code) is introduced into the least significant bits of the discrete wavelet transform coefficients of each document page. The analysis of the extracted binary string allows to detect and localize such attacks as page addition, deletion, and reordering, while the analysis of the QR code image reveals a fake fragment on a particular page. An experimental study of the method efficiency is performed. According to the experimental results, the method is suitable for PDF document authentication, as well as for steganographic transmission of a secret message.
Active Machine Learning-driven Experiments on Malaria Cell Classification

Malaria is a life-threatening disease caused by Plasmodium parasites that infect the red blood cells. Computer-aided diagnostic(CADx) tools are implemented for identifying Malaria disease. However, it is not satisfying due to limited data. Therefore, in this literature, a pool-based active learning strategy is implemented to solve the problem. Logistic Regression and SVM are used to find out if active learning(AL) do makes the AL smarter and a Pre-trained resnet 50 is used to see if AL is a time-efficient way of training. It is observed that with 26% of the entire dataset, the accuracy reaches 93%. AL using uncertainty sampling has a better performance than random sampling. The time cost ratio determines the performance of three sampling methods under the same time cost.
Evaluating the Efficacy of Small Face Recognition by Convolutional Neural Networks with Interpolation Based on Auto-adjusted Parameters and Transfer Learning

In this work, we propose a new approach for face recognition using low-resolution images. By cleverly combining conventional interpolation methods with the state-of-the-art classification approach, i.e. convolutional neural network, we introduce a new approach to efficiently leverage low-resolution images in classification task, especially in face recognition. Besides, we also do experiments on some recent popular methods, our approach outperforms some of them. Additionally, we propose a specific transfer learning strategy based on the preexisting well-known concept dedicated to low-resolution transfer learning. It boosts performance and reduces training time significantly. We also investigate on scalability by applying Bayesian optimization for hyper-parameter search. Therefore, our approach is able to be widely applied in many kinds of datasets and low-resolution classification tasks due to automatically seeking optimal hyper-parameters, which makes our method competitive to others.
Siamese Network based Pulse and Signal Attribute Identification

Target identification and signal type differentiation is an essential factor in radar systems. Traditional methods to identify targets and signal types require deterministic approaches, which may need a lot of computational power and development time. With the use of the Siamese Network proposed in this work, the time needed to train and simulate the results is significantly reduced. This work presents a method to identify signals and pulses and the number of interleaved target signals in them. First, the received signal is processed through various windowing functions to achieve an appropriate signal-to-noise ratio with a low error percent of data loss. Next, the processed signal's Continuous Wavelet Transform is taken in order to simultaneously capture both the slowly varying fluctuations and the transient phenomena. Finally, the wavelet transform is inputted into the Siamese Network for identification and prediction. The Siamese Network used in this work was developed in MATLAB language for training, testing, and validation. The network simulation shows promising results and robust performance for signal type and number of signals identification.
tx2_fcnn_node: An open-source ROS compatible tool for monocular depth reconstruction

We present tx2_fcnn_node – a Robot Operating System (ROS) compatible tool that is aimed at seamless integration of various monocular depth reconstruction neural networks to the robotic software based on ROS (which is a de-facto standard in the area of robotics). Our tool simplifies the process of deploying, evaluating, and comparing depth reconstruction neural networks both on real robots and in simulation. We complement our software with a set of the precompiled neural networks which can be used off the shelf, with some of them being able to demonstrate near real-time performance when running onboard compact embedded platforms, e.g. Nvidia Jetson TX2, that are often used nowadays both in academia and industry.
LSFD: Lightweight Single Stage Masked Face Detector with a CPU Real-time Speed

Although a large number of face detectors have been developed, one of the remaining challenges is to achieve CPU real-time speed with minimizing performance drop. To address this challenge, we propose a lightweight masked face detector named LSFD. The proposed model is inspired by FaceBoxes yet it is designed to be faster and more accurate. We proceed with various performance evaluations with three datasets: FDDB, WIDER FACE, and MAFA. The proposed model achieves superior accuracy in WIDER FACE validation set and competitive accuracy in FDDB. In addition, we first evaluate class-specific accuracy of masked face in MAFA test set. Our model runs at 46 FPS on CPU (i9-7920X@2.90) for VGA-resolution and 37 FPS on Samsung Galaxy Note20 for 320×320 image.
Cross-Cultural Adaptation of International Students Based on the Use of Online Platforms in the Context of the Internet

As China’s national strength continues to grow, and society continues to develop, China’s international influence is increasing. This has also attracted a large number of foreign students to China. With the increasing number of foreign students in China and the development of the education of foreign students in China, the education of foreign students is also facing more challenges. Many of these come from cultural differences between China and foreign countries. At the same time, with the development of the information age and the popularization of informatization, online platforms have become an important privacy that affects the cross-cultural adaptation of international students. When the online platform media officially enters the campus, it not only promotes the reform of classroom teaching, but is also closely related to the daily life of students. Online platforms is not only an important means for international students to understand China’s national conditions and customs, but also one of the most important social means for international students. Based on the actual situation of the development of the Internet and the various data of the use of online platforms by international students, this article explores the problems of international students in China overcoming cross-cultural communication and adaptation through online platforms, in order to provide a reference for the Chinese government to formulate policies for international students.
A semi‐supervised network based on feature embeddings for image classification

Abstract Deep learning approaches, including convolutional neural networks, are suitable for image classification tasks with well‐labelled data. Unfortunately, we do not always have sufficiently labelled data. Recent methods attempt to leverage labelled and unlabelled data using fine‐tuning or transfer learning. However, these methods rely on low‐level image features. This article departs from recent works and proposes a new semi‐supervised learning network that constitutes a convolutional branch and a neighbour cluster branch. Also, we introduce a new loss function that carefully optimizes the network according to the labelled/unlabelled data. In this way, we reduce any tendency to rely on low‐level features, which is the case in current methods. We use datasets from three different domains (hand‐written digits, natural images, and objects) to analyse the performance of our method. Experimental analysis shows that the network performs better by learning inherent discrimination features when integrating unlabelled data into the model's training process. Our proposed approach also provides strong generalization in the context of transfer learning. Finally, this study shows that the proposed loss function optimizes the network to produce more efficient feature embeddings for domain adaptation.
Signature Analysis of Series–Parallel System

This paper explores the idea of complex system having components in the form of series–parallel combination. Authors extend the idea of general law of addition of probabilities. They use this idea in the system of components having conditional mutual independent events. In this current study, the concept of general law of addition of probabilities is to find the system reliability; signature of components has been discussed. A numerical example has been taken to demonstrate the proposed techniques.
Taxonomy of Interaction Techniques based on F-Formation Patterns for developing Cross Device Applications

Collaboration environments provide the necessary tools to conduct a meeting and perform tasks together through devices and interaction techniques. Currently, investigations about Proxemics and F-Formation patterns show how these concepts can be used to improve the interaction between people and devices, considering the distance between them. However, there is not a broad perspective respect to use them with Cross Device. In this paper, a taxonomy of interaction techniques based on F-Formation patterns is proposed in order to be used as a guide of good practices for the development of future Cross Device Applications.
Development of an Efficient ETL Technique for Data Warehouses

In the area of knowledge science, data warehouse plays an important role in data mining, data analytics, and decision making. Extraction Transformation and Load (ETL) methodology are utilized widely in a developed data warehouse. In today’s competitive business world, mergers and acquisitions unit techniques are quite common. It desires extraction, transformation, and loading of a huge amount of structured data movement. This paper is associated with the improvement of Dynamic ETL (D-ETL) by adding noise-free filtering and missing data handling methods. This existing approach is modified to use the standard technique of extraction. ETL methodology is the progressive extraction procedure among the entire extraction. In this paper, we propose a new Efficient ETL technique which is an updated version of the D-ETL adding an attribute selection and noise reduction technique.
Software Engineering Project-Based Learning in an Up-To-Date Technological Context

In this article we present the design and execution of a software engineering course using the Project-Based Learning (PBL) approach in an up-to-date technological context. For the development of the course, a review of the teaching-learning objectives was carried out, considering not only technical knowledge, but also transversal skills. For the selection of techniques and tools, the state of technological practice in industry was considered. Although our initial focus was on educational redesign, the course started at the same time of the COVID-19 pandemic. We found that some of the software engineering and teamwork technological platforms were also suitable for the online modality. The use of Project-Based Learning in an updated technological context allowed to increase the engagement of the students and the relationship of the learning with the professional practice. In this article we present the rationale for the course educational redesign and lessons learned regarding the interrelation of pedagogical framework and technology context from a software engineering education perspective.
Performance-related Internal Clustering Validation Index for Clustering-based Anomaly Detection

One possible way to improve unsupervised anomaly detection is to use per-cluster models, particularly when the given data includes various cluster-level features. In realizing clustering-based anomaly detection, one natural question is how to determine the number of clusters that will likely lead to the optimal performance. In this paper, we propose a method that reflects the performance of anomaly detection in determining the number of clusters. We first derive an internal clustering validation index using the normality scores of trained per-cluster models for unlabeled training data for cases with different numbers of clusters. Then, we determine the number of clusters by selecting the case whose clustering validation index is the highest, which means that per-cluster models extract useful features for anomaly detection. Through experiments, we show that our proposed clustering validation index is highly correlated with anomaly detection accuracy (i.e., the average Pearson correlation coefficient is 0.965).
Reversible data hiding based on multi‐predictor and adaptive expansion

Adaptive embedding plays an important role in improving the embedding performance of reversible data hiding and it is usually realized by modifying the prediction-errors discriminately. By extending the skewed histogram shifting technique which uses a pair of extreme predictions to determine whether the target pixel should be predicted or not, this paper realizes another form of adaptive embedding, that is, adaptive prediction-error generation. Specifically, it is proposed to adaptively determine the pair of extreme predictions according to image content. Each pair of extreme predictions is first evaluated by the introduced distortion per embedding one bit. Then, an efficient mechanism to determine the best pair of extreme predictions for pixels with a given local complexity is designed. With such a mechanism solving the computational problem, it is also proposed to extend the context to obtain more pairs of extreme predictions such that more precise multi-predictor can be realized. With obtained errors, the best expansion bins are determined to achieve a more comprehensive self-adaption. Experimental results demonstrate that the proposed scheme achieves better capacity-distortion performance and outperforms a series of state-of-the-art schemes.
Controlled and Secure Sharing of Classified Threat Intelligence between Multiple Entities

The Malware Information Sharing Platform (MISP) enables the sharing of cyberthreat information within a community, company or organisation. However, this platform presents limitations if its information is deemed as classified or shared only for a given period of time. This implies that this information should to be handled only in encrypted form. One solution is to use MISP with searchable encryption techniques to impose greater control over the sharing of information. In this paper, we propose a controlled information sharing functionality that features a synchronisation procedure that enables classified data exchange between MISP instances, based on policies and ensuring the required confidentiality and integrity of the shared data. Sequence charts are presented validating the configuration, the data synchronisation, and the data searching between multiple entities.
Image Brightness reduction by canceling bright areas using brightness level and reconstruction by geodesic dilation

Special devices capable of transforming continuous images into digital formats generate digital images. Image digitization enables treating, visualizing and storing images in a computer system. However, the high-intensity light captured by a device of this type can cause unwanted bright areas in the generated images. Since these bright areas can lead to image processing errors, manifested as a false appearance, their deletion and subsequent reconstruction can produce a more faithful image. A conventional approach, based on mathematical morphology, consists of reducing or eliminating unwanted image brightness by an erosion process. The image obtained through this method undergoes a reconstruction process based on successive geodesic dilations. In this paper, a new brightness reduction method is proposed. This method identifies bright areas by applying a defined brightness limit value to remove pixels with brightness values above it, but leaving the remaining pixels unmodified. Then, the image is reconstructed by the usual image reconstruction approach based on mathematical morphology. Compared to the conventional approach, the proposed method in this work enhances brightness, generating an image more faithful to the actual object.
Research on Real-time Virtualization Technology for Control System

In recent years, the combination of multi-core processor and virtualization technology promotes the rapid application of virtualization technology in the field of real-time control. While virtualization technology gives full play to the performance of multi-core processors and improves the robustness and flexibility of application systems, the real-time performance loss caused by application virtualization has always been concerned by people. On the basis of discussing the concept of real-time control system, the principle of virtualization technology and the existing approaches of real-time virtualization technology, the real-time connotation of the virtualized control system is defined. This paper analyzes the real-time and applicable scenarios of two application schemes, namely, large-scale virtualization based on private cloud and single-node virtualization based on HyperVisor, the related real-time virtualization technology and application scenarios are discussed, and builds a test environment, and measures the interrupt response time and event response time of the corresponding schemes. The former can only meet the application with millisecond real-time performance, while the latter still has sub-microsecond real-time performance, which can meet most real-time system requirements.
Effective sample size for a mixture prior

Mixture prior distributions are much used in statistical applications, such as clinical trials, especially to avoid prior-data conflicts. We explicitly prove that the effective sample size (ESS) of a mixture prior rarely exceeds the ESS of any individual mixture component density of the prior.