Reviewerly/exHarmony huggingface dataset:
Analysis and Classification of Vaccine Dialogue in the Coronavirus Era

As the coronavirus tears through our global community, the world pins its hopes on the expedient availability of a safe and effective vaccine.In the U.S, however, mere mention of vaccines galvanizes a community that remains steadfastly opposed to them.This paper analyzes the vaccine dialogue on Twitter in the coronavirus era, using the data collected a week after President Trump's announcement of Operation Warp Speed.These tweets are explored in three ways.Informal opinion mining reveals both concerns and support; the antivaxx community is vociferous in opposing the vaccine, spreading misinformation, spinning conspiracies and whipping hysteria.Significant hesitation about the safety of the Covid-19 vaccine is also expressed in particular because of its rapid deployment.The pro-vaxx community counters this opposition by pointing to prior successes of immunizations as well as by mocking the anti-vaxx attitudes.A comparison of the social features of the anti-vaxx and pro-vaxx tweets suggests that the anti-vaxx community has gained steam on social media platforms and is better connected than the pro-vaxx community, which may lead to a penetration of discordant information through the online world.Identifying and labeling tweets that sow discordant information is one way to prevent their spread, which is facilitated by our classification framework that can distinguish between the anti-vaxx and provaxx tweets with an accuracy of over 80%.Taken together, our results suggest that unless a concerted effort is made to dispel these myths and misgivings, the fringe anti-vaxx minority is likely to become an outspoken majority by dragging many skeptics into their fold, and hence, hinder herd immunity.
Poster: Engineering Education: Outcomes Assessment

The last ten years have observed the significant changes in the structure of Russian system of higher education. Nowadays, there are three levels of university education: Bachelor’s, Master’s and PhD programs. The system of higher education in Russia has been developing according to new educational standards. One of the main parameters of the standards are competences of graduates which are to meet the requirements of professional standards. To provide a high level of educational programs it is necessary to create a system of outcomes assessment which is based on international standards and requirements. The article presents the principles of outcomes assessment system for all levels of university education. The authors propose the descriptors of outcomes assessment of Foreign Language Course for Bachelor’s, Master’s and PhD students majoring in chemical engineering at the Technological university. Each descriptor is characterized by the actions of students and is a component for the whole scale of students’ marks.
Preserving Privacy of Temporal Big Data

In the current technological era, huge amounts of big data are generated and collected from a wide variety of rich data sources. Embedded in these big data are useful information and valuable knowledge to be utilized. With the popularity of initiatives of open data, more big data have been published on open data platforms and made accessible to the public. To preserve privacy while maintaining the utility of data, research on privacy-preserving publishing has focused on preserving privacy of sensitive personal data such as patient data for health related applications. However, there are many other real-life situations, in which personal data of individual citizens and their daily routines need to be preserved when publishing. In this paper, we examine the problem of preserving privacy of temporal big data. Specifically, we present a temporal hierarchy privacy preserving model (THPPM) for some common daily routines-for example, parking. The model adapts and extends temporal hierarchy to generalize temporal data related to timestamp and spatial data related to check-in location. It also makes good use of generalized temporal representative points to preserve privacy of specific temporal data points. Evaluations on two real-life datasets on parking tickets for the US city of Buffalo and the Canadian city of Toronto shows that effectiveness and practicality of our THPPM in preserving privacy of temporal big data. Although this model is demonstrated and evaluated on parking ticket data, it would be applicable to preserving privacy of temporal big data for many other real-life applications and services.
Development of soft skills of teachers of Physics and Mathematics

Abstract The article considers the problem of the development of soft skills of teachers of Physics and Mathematics in higher educational institutions in the process of certification training in the system of postgraduate pedagogical education of Ukraine. The experience of foreign countries on the relevant teachers’ course training has been analyzed, the essence and significance of such soft skills as sociability, creativity, empathy for the professional development of teachers of Physics and Mathematics in higher educational institutions in the process of certification training has been characterized. The state of these skills development in teachers of Physics and Mathematics was studied, the average level of the development these skills has been stated. The possibilities of the soft skills development were determined, the four-stage process of their transition from unconscious incompetence to unconscious competence in the system of postgraduate education was substantiated. The principles, forms, methods, technologies of the development of soft skills in the system of certification training of teachers of Physics and Mathematics were discussed with the emphasis placed on the role of heuristic methods and forms, creative technologies of the soft skills development.
D-MONA: A dilated mixed-order non-local attention network for speaker and language recognition

Attention-based convolutional neural network (CNN) models are increasingly being adopted for speaker and language recognition (SR/LR) tasks. These include time, frequency, spatial and channel attention, which can focus on useful time frames, frequency bands, regions or channels while extracting features. However, these traditional attention methods lack the exploration of complex information and multi-scale long-range speech feature interactions, which can benefit SR/LR tasks. To address these issues, this paper firstly proposes mixed-order attention (MOA) for low frame-level speech features to gain the finest grain multi-order information at higher resolution. We then combine that with a non-local attention (NLA) mechanism and a dilated residual structure to balance fine grained local detail with convolution from multi-scale long-range time/frequency regions in feature space. The proposed dilated mixed-order non-local attention network (D-MONA) exploits the detail available from the first and the second-order feature attention analysis, but achieves this over a much wider context than purely local attention. Experiments are conducted on three datasets, including two SR tasks of Voxceleb and CN-celeb, and one LR task, NIST LRE 07. For SR, D-MONA improves on ResNet-34 results by at least 29% and 15% for Voxceleb1 and CN-celeb respectively. For the LR task, a large improvement is achieved over ResNet-34 of 21% for the challenging 3s utterance condition, 59% for the 10s condition and 67% for the 30s condition. It also outperforms the state-of-the-art deep bottleneck feature-DNN (DBF-DNN) x-vector system at all scales.
MSDP: multi-scheme privacy-preserving deep learning via differential privacy

Abstract Human activity recognition (HAR) generates a massive amount of the dataset from the Internet of Things (IoT) devices, to enable multiple data providers to jointly produce predictive models for medical diagnosis. That the accuracy of the models is greatly improved when trained on a large number of datasets from these data providers on the untrusted cloud server is very significant and raises privacy concerns. With the migration of a deep neural network (DNN) in the learning experience in HAR, we present a privacy-preserving DNN model known as Multi-Scheme Differential Privacy (MSDP) depending on the fusion of Secure Multi-party Computation (SMC) and 𝜖 -differential privacy, making it very practical since existing proposals are unable to make all the fully homomorphic encryption multi-key which is very impracticable. MSDP inputs a secure multi-party alternative to the ReLU function to reduce the communication and computational cost at a minimal level. With the aid of experimental verification on the four of the most widely used human activity recognition datasets, MSDP demonstrates superior performance with very good generalization performance and is proven to be secure as compared with existing ultramodern models without breach of privacy.
Petri Nets Android application as a mobile aid for students’ mastering modelling

Abstract Building models of various processes and their further investigation has been always in focus of different specialists’ training. There are some quite well known mechanisms and tools of modelling. However, Petri nets theory has found its wide application to the real-life parallel processes modelling. Petri nets give powerful facilities for dynamic models building and enable comprehensive learning of the process peculiarities. Hence, it is important to have relevant tools which allow to apply Petri nets potential to educational practice on purpose of earning by students skills of models building and investigation. The aim of the paper is to depict the functionality of the authors’ mobile Android application “Petri Nets Tool-Kit”, and to specify facilities and examples of its using for mastering modelling by students. Developed and represented in the paper mobile application provides students with the set of tools which enables to create, edit, save their own Petri nets as well as to change their parameters, visualize changing and play various scenarios of the modelled process. The “Petri Nets Tool-Kit” also contains relevant theoretical materials and the set of ready-made Petri nets examples, which makes the application attractive for mobile learning both in classroom activity and in students’ independent work in the context of their vocational training in terms of different specialties. The proper stages of learning activity for step-by-step mastering by students the basics of modelling and simulation are disclosed. It is shown that “Petri Nets Tool-Kit” is available exactly in its portable form for Android OS, which encourages students to mobile learning and arms them with a convenient simulation tool provoking them to improve their modelling and investigative skills anywhere during the day. The prospects of the work are outlined in terms of the empirical research as for validating the impact of modelling activity in the elaborated application on the trainees’ level of modelling skills.
Label-Aware Document Representation via Hybrid Attention for Extreme Multi-Label Text Classification

Extreme multi-label text classification (XMTC) aims at tagging a document with most relevant labels from an extremely large-scale label set. It is a challenging problem especially for the tail labels because there are only few training documents to build classifier. This paper is motivated to better explore the semantic relationship between each document and extreme labels by taking advantage of both document content and label correlation. Our objective is to establish an explicit label-aware representation for each document with a hybrid attention deep neural network model(LAHA). LAHA consists of three parts. The first part adopts a multi-label self-attention mechanism to detect the contribution of each word to labels. The second part exploits the label structure and document content to determine the semantic connection between words and labels in a same latent space. An adaptive fusion strategy is designed in the third part to obtain the final label-aware document representation so that the essence of previous two parts can be sufficiently integrated. Extensive experiments have been conducted on six benchmark datasets by comparing with the state-of-the-art methods. The results show the superiority of our proposed LAHA method, especially on the tail labels.
Analyzing the sensitivity of multi-objective software architecture refactoring to configuration characteristics

Software architecture refactoring can be induced by multiple reasons, such as satisfying new functional requirements or improving non-functional properties. Multi-objective optimization approaches have been widely used in the last few years to introduce automation in the refactoring process, and they have revealed their potential especially when quantifiable attributes are targeted. However, the effectiveness of such approaches can be heavily affected by configuration characteristics of the optimization algorithm, such as the composition of solutions. In this paper, we analyze the behavior of EASIER, which is an Evolutionary Approach for Software archItecturE Refactoring, while varying its configuration characteristics, with the objective of studying its potential to find near-optimal solutions under different configurations. In particular, we use two different solution space inspection algorithms (i.e., NSGA−II and SPEA2) while varying the genome length and the solution composition. We have conducted our experiments on a specific case study modeled in Æmilia ADL, on which we have shown the ability of EASIER to identify performance-critical elements in the software architecture where refactoring is worth to be applied. Beside this, from the comparison of multi-objective algorithms, NSGA−II has revealed to outperform SPEA2 in most of cases, although the latter one is able to induce more diversity in the proposed solutions. Our results show that the EASIER thoroughly automated process for software architecture refactoring allows to identify configuration contexts of the evolutionary algorithm in which multi-objective optimization more effectively finds near-optimal Pareto solutions.
Super-Resolution Enhancement Method Based on Generative Adversarial Network for Integral Imaging Microscopy

The integral imaging microscopy system provides a three-dimensional visualization of a microscopic object. However, it has a low-resolution problem due to the fundamental limitation of the F-number (the aperture stops) by using micro lens array (MLA) and a poor illumination environment. In this paper, a generative adversarial network (GAN)-based super-resolution algorithm is proposed to enhance the resolution where the directional view image is directly fed as input. In a GAN network, the generator regresses the high-resolution output from the low-resolution input image, whereas the discriminator distinguishes between the original and generated image. In the generator part, we use consecutive residual blocks with the content loss to retrieve the photo-realistic original image. It can restore the edges and enhance the resolution by ×2, ×4, and even ×8 times without seriously hampering the image quality. The model is tested with a variety of low-resolution microscopic sample images and successfully generates high-resolution directional view images with better illumination. The quantitative analysis shows that the proposed model performs better for microscopic images than the existing algorithms.
On Exposing the Challenging Long Tail in Future Prediction of Traffic Actors

Predicting the states of dynamic traffic actors into the future is important for autonomous systems to operate safely and efficiently. Remarkably, the most critical scenarios are much less frequent and more complex than the uncritical ones. Therefore, uncritical cases dominate the prediction. In this paper, we address specifically the challenging scenarios at the long tail of the dataset distribution. Our analysis shows that the common losses tend to place challenging cases sub-optimally in the embedding space. As a consequence, we propose to supplement the usual loss with a loss that places challenging cases closer to each other This triggers sharing information among challenging cases and learning specific predictive features. We show on four public datasets that this leads to improved performance on the challenging scenarios while the overall performance stays stable. The approach is agnostic w.r.t. the used network architecture, input modality or viewpoint, and can be integrated into existing solutions easily. Code is available at github.
Automatic Vehicle Counting and Tracking in Aerial Video Feeds using Cascade Region-based Convolutional Neural Networks and Feature Pyramid Networks

Unmanned aerial vehicles, or drones, are poised to solve many problems associated with data collection in complex urban environments. Drones are easy to deploy, have a great ability to move and explore the environment, and are relatively cheaper than other data collection methods. This study investigated the use of Cascade Region-based convolutional neural network (R-CNN) networks to enable automatic vehicle counting and tracking in aerial video streams.The presented technique combines feature pyramid networks and a Cascade R-CNN architecture to enable accurate detection and classification of vehicles.The paper discusses the implementation and evaluation of the detection and tracking techniques and highlights their advantages when they are used to collect traffic data.
The agency of computer vision models as optical instruments

Industry and governments have deployed computer vision models to make high-stake decisions in society. While they are often presented as neutral and objective, scholars have recognized that bias in these models might lead to the reproduction of racial, social, cultural and economic inequity. A growing body of work situates the provenance of bias in the collection and annotation of datasets that are needed to train computer vision models. This article moves from studying bias in computer vision models to the agency that is commonly attributed to them: the fact that they are universally seen as being able to make biased decisions. Building on the work of Bruno Latour and Jonathan Crary, the authors discuss computer vision models as agential optical instruments in the production of contemporary visuality. They analyse five interconnected research steps – task selection, category selection, data collection, data labelling and evaluation – of six widely cited benchmark datasets, published during a critical stage in the development of the field (2004–2020): Caltech 101, Caltech 256, PASCAL VOC, ImageNet, MS COCO and Google Open Images. They found that, despite all sorts of justifications, the selection of categories is not based on any general notion of visuality, but depends heavily upon perceived practical applications, the availability of downloadable images and, in conjunction with data collection, favours categories that can be unambiguously described by text. Second, the reliance on Flickr for data collection introduces a temporal bias in computer vision datasets. Third, by comparing aggregate accuracy rates and ‘human’ performance, the dataset papers introduce a false dichotomy between the agency of computer vision models and human observers. In general, the authors argue that the agency of datasets is produced by obscuring the power and subjective choices of its creators and the countless hours of highly disciplined labour of crowd workers.
Relaxed Peephole Optimization: A Novel Compiler Optimization for Quantum Circuits

As in classical computing, compilers play an important role in quantum computing. Quantum processors typically support a limited set of primitive operations or quantum gates and have certain hardware-related limitations. A quantum compiler is responsible for adapting a quantum program to these constraint environments and decomposing quantum gates into a sequence of the primitive ones. During the compilation process, it is also critical for the compiler to optimize the quantum circuits in order to reduce the noise in the computation results. Since the noise is introduced by operations and decoherence, reducing the gate count is the key for improving performance. In this paper, we propose a novel quantum compiler optimization, named relaxed peephole optimization (RPO) for quantum computers. RPO leverages the single-qubit state information that can be determined statically by the compiler. We define that a qubit is in a basis state when, at a given point in time, its state is either in the X-, Y-, or Z-basis (|+) / |-〉, |L〉 / R〉 and 10〉 / |1〉). When basis qubits are used as inputs to quantum gates, there exist opportunities for strength reduction, which replaces quantum operations with equivalent but less expensive ones. Compared to the existing peephole optimization for quantum programs, the difference is that our proposed optimization does not require an identical unitary matrix, thereby named `relaxed' peephole optimization. We also extend our approach to optimize the quantum gates when some input qubits are in known pure states. Both optimizations, namely the Quantum Basis-state Optimization (QBO) and the Quantum Pure-state Optimization (QPO), are implemented in the IBM's Qiskit transpiler. Our experimental results show that our proposed optimization pass is fast and effective. The circuits optimized with our compiler optimizations obtain up to 18.0% (11.7% on average) fewer CNOT gates and up to 8.2% (7.1% on average) lower transpilation time than that of the most aggressive optimization level in the Qiskit compiler. When running on real quantum computers, the success rates of 3-qubit quantum phase estimation algorithm improve by 2.30X due to the reduced gate counts.
The lower bound of the weightwise nonlinearity profile of a class of weightwise perfectly balanced functions

Boolean functions satisfying good cryptographic criteria when restricted to the set of vectors with constant Hamming weight play an important role in the recent FLIP stream cipher. Although the nonlinearity of Boolean functions can be calculated rapidly by Walsh transform, the nonlinearity of the Boolean functions with restricted input seems to be very difficult to calculate. In this paper, a class of weightwise perfectly balanced Boolean functions with very simple algebraic normal form is proposed. Then, the cryptographic properties of the weightwise perfectly balanced Boolean functions are discussed. Especially, the lower bound of the weightwise nonlinearity of these 2m-variable functions is given for any positive integer m.