Reviewerly/exHarmony huggingface dataset:
Cervical Cancer Cell Detection Based on Deep Convolutional Neural Network

The precise location of cancerous cells in thousands of cervical squamous epithelial cells can effectively reduce the workload of doctors and improve the accuracy of cervical cancer diagnosis. In this paper, we propose a new network structure for cervical cancer cells detection, named Series-parallel fusion network (SPFNet). Compared with traditional frameworks that use classification networks as the backbone for image feature extraction, we use different combination strategies in the series module and design five different head components to find the most suitable network structure for the detection task. In addition, data preprocessing such as RoI sliding window clipping is carried out for the thinprep cytologic images in cervical cancer. In order to compare the proposed framework with the state-of-the-art detection, we test these object detection algorithms on the same cervical cancer dataset. The experimental results show that our detection framework generates the optimum performances better than any others.
Developmental changes in infants’ and children’s attention to faces and salient regions vary across and within video stimuli.

Visual attention in complex, dynamic scenes is attracted to locations that contain socially relevant features, such as faces, and to areas that are visually salient. Previous work suggests that there is a global shift over development such that observers increasingly attend to faces with age. However, no prior work has tested whether this shift is truly global, that is, consistent across and within stimuli despite variations in content. To test the global shift hypothesis, we recorded eye movements of 89 children (6 months to 10 years) and adults while they viewed 7 video clips. We measured the extent to which each participant attended to faces and to salient areas for each video. There was no evidence of global age-related changes in attention: Neither feature showed consistent increases or decreases with age. Moreover, windowed analyses within each stimulus video revealed significant moment-to-moment variations in the relation between age and each visual feature (via a bootstrapping analysis). For some time windows, adults looked more often at both feature types compared to infants and children. However, for other time windows, the pattern was reversed-younger participants looked more at faces and salient locations. Lack of consistent directional effects provides strong evidence against the global shift hypothesis. We suggest an alternative explanation: Over development, observers increasingly prioritize when and where to look by learning to track which features are relevant within a scene. Implications for the development of visual attention and children's understanding of screen-based media are discussed. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
Design and validation of a music technology course for initial music teacher education based on the TPACK framework and the project-based learning approach

This project outlines a design for, and presents an evaluation and validation of, an information and communication technology (ICT) training course on an initial teacher education programme for pre-service music teachers at a Spanish University. The primary objective was to improve initial teacher education in music technology through a course design with two key components: (1) constructivist learning through collaborative projects (PBL) and (2) the technological pedagogical content knowledge (TPACK) integration framework. The course saw students carry out four real-life projects, three of which involved the elaboration of educational material through the exclusive use of musical technology, and one of which involved an ICT research project. Results were obtained through an end-product evaluation and a self-assessment questionnaire and indicated that the course was well received and highly valued by the participants. In their self-assessment responses, students spoke of the academic value of the course, a confidence in their own skills, their willingness to use ICTs in future teaching contexts, the importance of ICTs in their degree programme, the fact that the course surpassed their expectations in terms of achievement (a fact corroborated through data triangulation in the end-product evaluation) and the transferability of the knowledge acquired to the music classroom. Finally, they provided suggestions for potential improvements to the course.
Fast eyes detection in thermal images

Abstract In recent years many methods have been proposed for eye detection. In some cases however, such as driver drowsiness detection, lighting conditions are so challenging that only the thermal imaging is a robust alternative to the visible light sensors. However, thermal images suffer from poor contrast and high noise, which arise due to the physical properties of the long waves processing. In this paper we propose an efficient method for eyes detection based on thermal image processing which can be successfully used in challenging environments. Image pre-processing with novel virtual high dynamic range procedure is proposed, which greatly enhances thermal image contrast and allows for more reliable computation of sparse image descriptors. The bag-of-visual-words approach with clustering was selected for final detections. We compare our method with the YOLOv3 deep learning model. Our method attains high accuracy and fast response in real conditions without computational complexity and requirement of a big dataset associated with the deep neural networks. For quantitative analysis a series of thermal video sequences were recorded in which eye locations were manually annotated. Created dataset was made publicly available on our website.
On Repetitiveness Measures of Thue-Morse Words

We show that the size \(\gamma (t_n)\) of the smallest string attractor of the n-th Thue-Morse word \(t_n\) is 4 for any \(n\ge 4\), disproving the conjecture by Mantaci et al. [ICTCS 2019] that it is n. We also show that \(\delta (t_n) = \frac{10}{3+2^{4-n}}\) for \(n \ge 3\), where \(\delta (w)\) is the maximum over all \(k = 1,\ldots ,|w|\), the number of distinct substrings of length k in w divided by k, which is a measure of repetitiveness recently studied by Kociumaka et al. [LATIN 2020]. Furthermore, we show that the number \(z(t_n)\) of factors in the self-referencing Lempel-Ziv factorization of \(t_n\) is exactly 2n.
Teaching and Learning in resource-limited settings in the face of the COVID-19 pandemic

Although the extent of the damage caused by the COVID-19 pandemic in Africa is unknown, its effect on the socioeconomic and geopolitical spheres is becoming palpable. Its impact on communication and social formations is evident as it has shifted such to a more spatial and virtual sphere. The closure of schools does not only interfere with the traditional patterns of socialization but intercepts the academic sequence and plunges ill-prepared educational systems reminiscent of African universities into a cosmos of teaching and learning uncertainties. How prepared are African universities to cope with the threats to its educational systems and adapt or influence the transition? Despite having a teeming youth birth into technology, are they cognitively and technically adept to teach or be taught within the accredited online e-learning platforms? While the safety measures announced by African governments in relation to the closing of educational institutions and the banning of gatherings have a negative impact on the continuation of teaching, learning and research activities of higher education institutions, it could also be seen as an opportunity.
Multitask deep learning for native language identification

Identifying the native language of a person by their text written in English (L1 identification) plays an important role in such tasks as authorship profiling and identification. With the current proliferation of misinformation in social media, these methods are especially topical. Most studies in this field have focused on the development of supervised classification algorithms, that are trained on a single L1 dataset. Although multiple labeled datasets are available for L1 identification, they contain texts authored by speakers of different languages and do not completely overlap. Current approaches achieve high accuracy on available datasets, but this is attained by training an individual classifier for each dataset. Studies show that joint training of multiple classifiers on different datasets can result in sharing information between the classifiers, leading to an increase in the accuracy of both tasks. In this study, we develop a novel deep neural network (DNN) architecture for L1 classification; it is based on an adversarial multitask learning method that integrates shared knowledge from multiple L1 datasets. We propose several variants of the architecture and rigorously evaluate their performance on multiple datasets. Our results indicate the proposed multitask architecture is more efficient in terms of classification accuracy than previously proposed methods.
Enhancing Short Text Topic Modeling with FastText Embeddings

Over the past few years, we have experienced the rapid development of online social media, which produced a variety of short texts. It is important to understand the topic patterns of these short texts. Because of data sparsity, traditional topic models are not suitable for short text topic analysis. In this paper, we proposed a novel topic model, referred as FastText-based Sentence-LDA (FSL) model, which extends the Sentence-LDA topic model for short texts. We first utilize the FastText model to train a word embedding replacement model, which can alleviate the problem of lacking word co-occurrence information over short texts. Secondly, we propose a new latent feature topic model which integrates latent feature word embeddings into Sentence-LDA. Experimental results demonstrate that our new model has produced significant improvements in topic coherence by using information from external corpora.
Using a context-aware approach to recommend code reviewers

Code review is a commonly used practice in software development. It refers to the process of reviewing new code changes before they are merged with the code base. However, to perform the review, developers are mostly assigned manually to code changes. This may lead to problems such as: a time-consuming selection process, limited pool of known candidates and risk of over-allocation of a few reviewers. To address the above problems, we developed Carrot, a machine learning-based tool to recommend code reviewers. We conducted an improvement case study at Ericsson. We evaluated Carrot using a mixed approach. we evaluated the prediction accuracy using historical data and the metrical Mean Reciprocal Rank (MRR). Furthermore, we deployed the tool in one Ericsson project and evaluated how adequate the recommendations were from the point of view of the tool users and the recommended reviewers. We also asked the opinion of senior developers about the usefulness of the tool. The results show that Carrot can help identify relevant non-obvious reviewers and be of great assistance to new developers. However, there were mixed opinions on Carrot's ability to assist with workload balancing and the decrease code review lead time.
Multi-Degree of Freedom Isolation System With High Frequency Roll-Off for Drone Camera Stabilization

The technological developments in the field of image based sensing have led to a vast growth in the use of drones in various domains. The drone is usually equipped with an image sensor (camera) which collect images over the target area. These images are then post-processed to extract the important information. Efficiency and accuracy of the image based sensing are largely dependent on the captured image quality. Therefore, it is important to prevent the transmission of the drone vibrations to the camera. Most of the current camera mounting systems use passive rubber mounts for isolation. However, these mounts are effective only in vertical direction and essentially adds damping to the system which degrades the performance of the isolation at high frequency. In this paper, a multi-degree of freedom isolation system, based on a Stewart platform configuration, is proposed for drone camera stabilization. The important features of the proposed isolation system are - (i) high frequency roll-off, (ii) no use of flexible joints, (iii) uses non-contact voice coil actuator thus avoiding spurious resonances of the legs, (iv) adjustable stiffness, (v) 3D printed lightweight parts and (vi) centralized control using a single sensor (inertial measurement unit). A prototype of the proposed system has been manufactured and validated experimentally. The proposed isolation system is found to reduce the response of the isolation system near resonance without compromising performance at high frequency. The application of the isolation system can be easily extended to other fields which require high quality image acquisition.
Baseline System of Voice Conversion Challenge 2020 with Cyclic Variational Autoencoder and Parallel WaveGAN

In this paper, we present a description of the baseline system of Voice Conversion Challenge (VCC) 2020 with a cyclic variational autoencoder (CycleVAE) and Parallel WaveGAN (PWG), i.e., CycleVAEPWG. CycleVAE is a nonparallel VAE-based voice conversion that utilizes converted acoustic features to consider cyclically reconstructed spectra during optimization. On the other hand, PWG is a non-autoregressive neural vocoder that is based on a generative adversarial network for a high-quality and fast waveform generator. In practice, the CycleVAEPWG system can be straightforwardly developed with the VCC 2020 dataset using a unified model for both Task 1 (intralingual) and Task 2 (cross-lingual), where our open-source implementation is available at this https URL. The results of VCC 2020 have demonstrated that the CycleVAEPWG baseline achieves the following: 1) a mean opinion score (MOS) of 2.87 in naturalness and a speaker similarity percentage (Sim) of 75.37% for Task 1, and 2) a MOS of 2.56 and a Sim of 56.46% for Task 2, showing an approximately or nearly average score for naturalness and an above average score for speaker similarity.
Scientific and Pedagogical Projections of the Transformation of University Education

University education has entered the stage of transformation; therefore, a scientific understanding of the future development paths is required. The purpose of the article is to present the experience of the scientific and pedagogical school of the university in studying the new challenges of society. Transformation of university education relates to such spheres as subjectivity and competence, student’s personality self-determination, the model of interaction between subjects of university education, digital personality socialization in university’s open educational space. The specificity and methodological basis of the study is the axiology of educational innovation. The scientific and pedagogical school fosters the processes of polytechnic university transformation into a multidisciplinary university in the conditions of humanization of engineering education. The article reveals the resources of the scientific and pedagogical activity of the humanitarian section of the university – the research activities of the team of pedagogical departments and the Dissertation Council, the implementation of grant projects and experimental sites of various levels.
An Integrated Vulnerability Assessment of Electronic Commerce Websites

This paper examines the security issues on electronic commerce websites in Ghana using technical and nontechnical procedures.The study assessed e-commerce websites for the security tools employed to protect user data and other related privacy issues on the websites.It also analyzed e-commerce websites for encryption security tools that protect customer data and test e-commerce websites for the presence of security vulnerabilities that could threaten the security of the sites and their users using w3af.The study used a combination of three methods; web content analysis, information security audit and testing of the websites using w3af, a vulnerability assessment tool.Web application attack and audit framework (w3af) was used to test and identify possible vulnerabilities on the e-commerce websites that could be used by malicious users to steal customer data for fraudulent intent.The research focused to reveal the security vulnerabilities present on e-commerce websites that could affect the trust of clients, the satisfaction of clients, and patronage of e-commerce services by customers.The study found credit card number disclosures, full path disclosures vulnerabilities, cross-site request forgery vulnerabilities and social security number exposures of clients on the e-commerce websites.These security weaknesses in these e-commerce websites have been highlighted as findings in the study that would inform policy direction on electronic data collection, protection and use in the e-commerce industry in Ghana.The findings will also inform industry players in the e-commerce sector on the need to strengthen security on their websites and caution customers to be security conscious on all e-commerce websites.The major significance of the study is the fact that majority of the electronic commerce websites have a lot of vulnerabilities making them unsecure for customers to trust their private data into their care.This study as such informs the customer society and the electronic commerce industry of these security weaknesses and the urgent need to get them fixed.Some solutions have been suggested in the paper to assist in fixing these security vulnerabilities.These solutions have provided the best results.A diligent application of these methods in addressing the vulnerabilities would provide a more secure and less vulnerable e-commerce websites for users.The precautions suggested could assist protect customers and reduce cyber threats during online shopping.
Paired Representation Learning for Event and Entity Coreference.

Co-reference of Events and of Entities are commonly formulated as binary classification problems, given a pair of events or entities as input. Earlier work addressed the main challenge in these problems -- the representation of each element in the input pair by: (i) modelling the representation of one element (event or entity) without considering the other element in the pair; (ii) encoding all attributes of one element (e.g., arguments of an event) into a single non-interpretable vector, thus losing the ability to compare cross-element attributes. In this work we propose paired representation learning (PairedRL) for coreference resolution. Given a pair of elements (Events or Entities) our model treats the pair's sentences as a single sequence so that each element in the pair learns its representation by encoding its own context as well the other element's context. In addition, when representing events, PairedRL is structured in that it represents the event's arguments to facilitate their individual contribution to the final prediction. As we show, in both (within-document & cross-document) event and entity coreference benchmarks, our unified approach, PairedRL, outperforms prior state of the art systems with a large margin.
Transparent and Accountable Vehicular Local Advertising With Practical Blockchain Designs

Vehicular local advertising enables roadside retailers to provide timely and location-aware advertisements to on-road vehicular users to attract more in-store visits. However, the prevalence of the vehicular local advertising has raised increasing transparency and accountability concerns on the spatial keyword query process, such as why a vehicular user receives advertisements from specific spatial entities as well as the timely detection of the advertising misbehavior. In this paper, we develop a transparent and accountable vehicular local advertising system by utilizing the tamper-proof and open nature of the blockchain technology. Considering the large scale of the spatial-keyword database and the expensive computation and storage cost on the blockchain, we introduce two design strategies, digest-and-verify and divide-then-assemble. In specific, a large-scale spatial keyword database is digested and stored on the blockchain. The spatial keyword query is then conducted with modular executions of two off-chain spatial keyword query functions, the results of which are efficiently assembled and verified in an advertising smart contract. By doing so, expensive on-chain computation and storage overheads are significantly reduced at a cost of acceptable off-chain overheads. We define the security requirements of the vehicular local advertising system as Auditing Security and achieve the notion with the security analysis. We also conduct real-world experiments to show the efficiency of the blockchain-based vehicular local advertising system.
Average convergence rate of evolutionary algorithms in continuous optimization

The average convergence rate (ACR) measures how fast the approximation error of an evolutionary algorithm converges to zero per generation. It is defined as the geometric average of the reduction rate of the approximation error over consecutive generations. This paper makes a theoretical analysis of the ACR in continuous optimization. The obtained results are summarized as follows. According to the limit property, the ACR is classified into two categories: (1) linear ACR whose limit inferior value is larger than a positive and (2) sublinear ACR whose value converges to zero. Then, it is proven that the ACR is linear for evolutionary programming using positive landscape-adaptive mutation, but sublinear for that using landscape-invariant or zero landscape-adaptive mutation. The relationship between the ACR and the decision space dimension is also classified into two categories: (1) polynomial ACR whose value is larger than the reciprocal of a polynomial function of the dimension for any generation, and (2) exponential ACR whose value is less than the reciprocal of an exponential function of the dimension for an exponential long period. It is proven that for easy problems such as linear functions, the ACR of the (1 + 1) adaptive random univariate search is polynomial. But for hard functions such as the deceptive function, the ACR of both the (1 + 1) adaptive random univariate search and evolutionary programming is exponential.
Novel Hybrid Algorithm of Improved CKF and GRU for GPS/INS

In order to ensure that Inertial Navigation System/Global Positioning System integrated navigation system (INS/GPS) can still provide high precision positioning results when GPS outages, a novel hybrid algorithm based on Gated Recurrent Unit (GRU) and interacting multiple model adaptive robust cubature Kalman filter (IMM-ARCKF) is proposed. Firstly, the IMM-ARCKF algorithm is proposed to solve the uncertainty of system model and measurement noise statics in the application of INS/GPS on the road. Then, GRU neural network is introduced into INS/GPS system which includes two modes of training and prediction. When GPS signal can be received, the GRU neural network works in the training mode. When GPS outages, the GRU neural network predicts the GPS position increment. Finally, the effectiveness of the algorithm is evaluated by the experiment and analysis. From the data of the experiment, the proposed algorithm can improve the positioning accuracy during GPS outages.