Reviewerly/exHarmony huggingface dataset:
Towards Probabilistic Session-Type Monitoring

We present a tool-based approach for the runtime analysis of communicating processes grounded on probabilistic binary session types. We synthesise a monitor out of a probabilistic session type where each choice point is augmented with a probability distribution. The monitor observes the execution of a process, infers its probabilistic behaviour and issues warnings when the observed behaviour deviates from the one specified by the probabilistic session type.
LIORI at SemEval-2021 Task 8: Ask Transformer for measurements

This work describes our approach for subtasks of SemEval-2021 Task 8: MeasEval: Counts and Measurements which took the official first place in the competition.To solve all subtasks we use multi-task learning in a questionanswering-like manner.We also use learnable scalar weights to weight subtasks' contribution to the final loss in multi-task training.We finetune LUKE to extract quantity spans and we fine-tune RoBERTa to extract everything related to found quantities, including quantities themselves.
Toward data-driven solutions to interactive dynamic influence diagrams

Abstract With the availability of significant amount of data, data-driven decision making becomes an alternative way for solving complex multiagent decision problems. Instead of using domain knowledge to explicitly build decision models, the data-driven approach learns decisions (probably optimal ones) from available data. This removes the knowledge bottleneck in the traditional knowledge-driven decision making, which requires a strong support from domain experts. In this paper, we study data-driven decision making in the context of interactive dynamic influence diagrams (I-DIDs)—a general framework for multiagent sequential decision making under uncertainty. We propose a data-driven framework to solve the I-DIDs model and focus on learning the behavior of other agents in problem domains. The challenge is on learning a complete policy tree that will be embedded in the I-DIDs models due to limited data. We propose two new methods to develop complete policy trees for the other agents in the I-DIDs. The first method uses a simple clustering process, while the second one employs sophisticated statistical checks. We analyze the proposed algorithms in a theoretical way and experiment them over two problem domains.
YFCC-CelebA Face Attributes Datasets

The scales of the data accessible through internet search engines can reach hundreds of millions, or even billions. The existence of such large weak-labeled databases has gained importance in the training of face recognition algorithms. Starting with the publicly available YFCC100M, we propose a weaklylabeled subset for multi-label face recognition for self-supervised methods. A 392K image subset of YFCC100M of 128x128 images was obtained by querying for the 40 facial attributes. We made this dataset publicly available for other face recognition studies, by sharing the IDs, the links and the bounding boxes1. To reduce outliers with respect to CelebA, we apply the Elliptic Envelope algorithm, in the the latent feature space learned over CelebA, obtaining 353K face images. MixMatch algorithm is applied to this last set, to obtain pseudo labels. Pretraining with these pseudo-labels and final fine-tuning with CelebA brings an improvement of 0.4% points in the Area Under the ROC Curve (AUC) score over the system trained only with CelebA.
YOLOMask, an Instance Segmentation Algorithm Based on Complementary Fusion Network

Object detection and segmentation can improve the accuracy of image recognition, but traditional methods can only extract the shallow information of the target, so the performance of algorithms is subject to many limitations. With the development of neural network technology, semantic segmentation algorithms based on deep learning can obtain the category information of each pixel. However, the algorithm cannot effectively distinguish each object of the same category, so YOLOMask, an instance segmentation algorithm based on complementary fusion network, is proposed in this paper. Experimental results on public data sets COCO2017 show that the proposed fusion network can accurately obtain the category and location information of each instance and has good real-time performance.
Secure integer division with a private divisor

Abstract We consider secure integer division within a secret-sharing based secure multi-party computation framework, where the dividend is secret-shared, but the divisor is privately known to a single party. We mention various applications where this situation arises. We give a solution within the passive security model, and extend this to the active model, achieving a complexity linear in the input bit length. We benchmark both solutions using the well-known MP-SPDZ framework in a cloud environment. Our integer division protocol with a private divisor clearly outperforms the secret divisor solution, both in runtime and communication complexity.
Optimal Cryptocurrency and BIST 30 Portfolios with the Perspective of Markowitz Portfolio Theory

We apply the Markowitz mean-variance framework to assess risk-return benefits of cryptocurrency-portfolios. Using daily data of the three major cryptocurrencies for the time span 1/1/2019 to 27/04/2021, we relate risk and return of different mean-variance portfolio strategies to Bitcoin, Etherium, Ripple and BIST 30 benchmark. We find that combining cryptocurrencies crowds out BIST 30 index to maximize return and Sharpe ratio while cryptocurrencies are crowded out if the optimization problem is changed to a risk minimization problem rather than a return maximization problem. Furthermore, according to rolling-window approach shift from Bitcoin to Etherium is important.
OLVA: Optimal Latent Vector Alignment for Unsupervised Domain Adaptation in Medical Image Segmentation

This paper addresses the domain shift problem for segmentation. As a solution, we propose OLVA, a novel and lightweight unsupervised domain adaptation method based on a Variational Auto-Encoder (VAE) and Optimal Transport (OT) theory. Thanks to the VAE, our model learns a shared cross-domain latent space that follows a normal distribution, which reduces the domain shift. To guarantee valid segmentations, our shared latent space is designed to model the shape rather than the intensity variations. We further rely on an OT loss to match and align the remaining discrepancy between the two domains in the latent space. We demonstrate OLVA’s effectiveness for the segmentation of multiple cardiac structures on the public Multi-Modality Whole Heart Segmentation (MM-WHS) dataset, where the source domain consists of annotated 3D MR images and the unlabelled target domain of 3D CTs. Our results show remarkable improvements with an additional margin of \(12.5\%\) dice score over concurrent generative training approaches.
Blockchain Technology Investment and Sharing Strategy of Port Supply Chain Under Competitive Environment

Abstract Blockchain technology plays a very positive role in promoting the development of the port supply chain. Although there are some practical examples of blockchain in the port supply chain (eg. Trade Len or Cargo Smart), there are few application scenarios. Therefore, blockchain technology has yet to be widely used in the port industry. This may be related to the construction of blockchain. This paper analyzes the impact of blockchain technology on the port supply chain and the technology sharing on the shipping market. It is found that the investment of blockchain technology in a competitive environment has different characteristics. When the efficiency of technology investment and the proportion of market expansion reach a certain combination, there are two different equilibrium strategy choices: Only technology investment or technology sharing. Based on the above research results, this paper further investigates the results of the comprehensive influence of different factors such as competition intensity and market expansion proportion.
hub at SemEval-2021 Task 2: Word Meaning Similarity Prediction Model Based on RoBERTa and Word Frequency

This paper introduces the system description of the hub team, which explains the related work and experimental results of our team's participation in SemEval 2021 Task 2: Multilingual and Cross-lingual Word-in-Context Disambiguation (MCL-WiC).The data of this shared task is mainly some cross-language or multi-language sentence pair corpus.The languages covered in the corpus include English, Chinese, French, Russian, and Arabic.The task goal is to judge whether the same words in these sentence pairs have the same meaning in the sentence.This can be seen as a task of binary classification of sentence pairs.What we need to do is to use our method to determine as accurately as possible the meaning of the words in a sentence pair are the same or different.The model used by our team is mainly composed of RoBERTa and Tf-Idf algorithms.The result evaluation index of task submission is the F1 score.We only participated in the English language task.The final score of the test set prediction results submitted by our team was 84.60.
Analytical hierarchy processes and Pareto analysis for mitigating cybercrime in the financial sector

Purpose The purpose of this study is to use a decision support model based on the Analytical Hierarchy Process (AHP) and Pareto analysis (PA) for ranking the impact of different kinds of cybercrime in organisations in the financial sector to support decisions on cybercrime mitigation. Design/methodology/approach From a structured questionnaire to the staff of 17 licensed banks in South Africa in charge of management, administration and operations, the perceived effect of cybercrime on the organisation’s goals, namely, organisation’s profitability, goodwill, customers’ satisfaction and risk management was derived. The pairwise comparison of the organisation’s goals and identified forms of cybercrime was done using the AHP. Findings The results obtained indicate that there was a consensus (100% of the answers) that the effect of cybercrime has negatively impacted the organisation’s objectives profitability and goodwill. Also, still 95.23% of the respondents agreed that the effect of cybercrime has negatively impacted the level of customers’ satisfaction, while only 7.15% saw an impact on the organisation’s risk management processes. Using these results in the AHP, analysis delivers a hierarchical order about the relevance of prevalent forms of cybercrime for the organisation´s cybercrime mitigation. The PA further shows the magnitude of the forms of cybercrime relative to each other. Practical implications Hence, this study provides a decision support framework for organisational management in the quest to explore the impact of cyber fraud. It can serve as a practical guided approach for the application of AHP analysis for the existing and emerging forms of cybercrime. Originality/value The novelty of this study lies in the fact that the combination of the AHP and PA to support solving a multi-criteria decision problem relating to the prevalence of cybercrime has not been sufficiently highlighted by the existing literature.
JUST-BLUE at SemEval-2021 Task 1: Predicting Lexical Complexity using BERT and RoBERTa Pre-trained Language Models

Predicting the complexity level of a word or a phrase is considered a challenging task.It is even recognized as a crucial step in numerous NLP applications, such as text rearrangements and text simplification.Early research treated the task as a binary classification task, where the systems anticipated the existence of a word's complexity (complex versus uncomplicated).Other studies had been designed to assess the level of word complexity using regression models or multi-labeling classification models.Deep learning models show a significant improvement over machine learning models with the rise of transfer learning and pre-trained language models.This paper presents our approach that won the first rank in the SemEval-task1 (sub stask1).We have calculated the degree of word complexity from 0-1 within a text.We have been ranked first place in the competition using the pre-trained language models BERT and RoBERTa, with a Pearson correlation score of 0.788.
Safer Reinforcement Learning through Transferable Instinct Networks

Random exploration is one of the main mechanisms through which reinforcement learning (RL) finds well-performing policies. However, it can lead to undesirable or catastrophic outcomes when learning online in safety-critical environments. In fact, safe learning is one of the major obstacles towards real-world agents that can learn during deployment. One way of ensuring that agents respect hard limitations is to explicitly configure boundaries in which they can operate. While this might work in some cases, we do not always have clear a-priori information which states and actions can lead dangerously close to hazardous states. Here, we present an approach where an additional policy can override the main policy and offer a safer alternative action. In our instinct-regulated RL (IR^2L) approach, an instinctual network is trained to recognize undesirable situations, while guarding the learning policy against entering them. The instinct network is pre-trained on a single task where it is safe to make mistakes, and transferred to environments in which learning a new task safely is critical. We demonstrate IR^2L in the OpenAI Safety gym domain, in which it receives a significantly lower number of safety violations during training than a baseline RL approach while reaching similar task performance.
Digitalization of education in the function of economic development

This paper conducts theoretical and empirical research with the aim of analyzing the opportunities provided by digitalized education systems and the use of modern technologies in education in the current economic crisis and global pandemic and how it affects the process of teaching and developing future needs of the workforce towards developing economies. The fundamental goal of this paper is a meticulous study of the impact of digital literacy on the digitalization of education and the use of new approaches in teaching and learning to develop knowledge, abilities and skills by which the economy should improve business efficiency and effectiveness.
Testing a hardware random number generator using NIST statistical test suite

Random number generators are required for the operation of cryptographic information protection systems. For а correct application of the generator in the field of information security, it is necessary that its output sequence to be indistinguishable from a uniformly distributed random sequence. To verify this, it is necessary to test the generator output sequence using various statistical test suites such as Dihard and NIST. The purpose of this work is to test a prototype hardware random number generator. The generator is built on the basis of the ND103L noise diode and has a random digital sequence of binary numbers at the output. In the prototype there is a possibility of regulating the amount of reverse current through the noise diode, as well as setting the data acquisition period, i.e. data generation frequency. In the course of operation, a number of sequences of random numbers were removed from the generator at various values of the reverse current through the noise diode, the period of data acquisition and the ambient temperature. The resulting sequences were tested using the NIST statistical test suite. After analyzing the test results, it was concluded that the generator operates relatively stably in a certain range of initial parameters, while the deterioration in the quality of the generator's operation outside this range is associated with the technical characteristics of the noise diode. It was also concluded that the generator under study is applicable in certain applications and to improve the stability of its operation, it can be improved both in hardware and software. The results of this work can be useful to developers of hardware random number generators built according to a similar scheme.
Challenges and Opportunities of English Grammar Acquisition by Distance at the Tertiary Level

Distance learning introduced in almost all educational establishments in spring 2020 has raised interest among researchers how to deal with it in a more productive way. Thus, the goal of the research was to find out undergraduates’ views on distance learning of English grammar and the use of online revision materials in developing students’ independent study skills at a tertiary institution in Latvia. the research tool was a questionnaire consisting of open-ended and closed questions. Twenty-three students answered the survey questions. the research results indicate that studying by distance is preferred because it allows the students to choose the study time and place, to develop their timemanagement, independent study skills, as well as improve their motivation. the main drawbacks of studying by distance, according to the students, are insufficient feedback, inability to ask questions directly and receive immediate answers and problems with their own time management and independent study skills.
A Study on the Reduction in VR Cybersickness using an Interactive Wind System

This paper presents an interactive wind system that generates artificial winds in a virtual reality (VR) environment according to online user inputs from a steering wheel and an acceleration pedal.Our system is composed of a head-mounted display (HMD) and three electric fans to make the user sense touch from the winds blowing from three different directions in a racing car VR application.To evaluate the effectiveness of the winds for reducing VR cybersickness, we employ the simulator sickness questionnaire (SSQ), which is one of the most common measures for cybersickness.We conducted experiments on 13 subjects for the racing car contents first with the winds and then without them or vice versa.Our results showed that the VR contents with the artificial winds clearly reduce cybersickness while providing a positive user experience.
Real Time Implementation of Face Recognition based Smart Attendance System

This research work addresses the issue of incorporating an automatic attendance system to the frame of an institution using face detection and recognition techniques. The proposed system aims at reducing computational time with available hardware to yield more efficient results. The proposed model utilizes Histogram Oriented Gradients and facial encodings derived from facial landmarks. It also addresses the problems related to accuracy of facial recognition and the resource requirement for quick, real-time facial recognition by applying multi-processing. The improvement in performance in terms of accuracy across two different methods, and the improvement in terms of time requirement for the same method using different strategies have also been documented for demonstration. The designed system demonstrates the effectiveness of task parallelization with a minimum amount of hardware desiderata. The system has been designed to an optimum self-sustaining ecosystem which can efficiently operate on its own accord and compute comprehensible feedback without the requirement of any third-party human interference. A Graphical User Interface has been incorporated into the system for maximum user comprehensibility
Directions of robotics in the system of basic and additional education of children

The article reveals the basic concepts associated with robotics: robot, manipulative and mobile (autonomous) robots, robotics. The main directions of the implementation of robotics in the system of basic and additional education of children are considered: educational, competitive, design. The stages of the implementation of the direction of educational robotics, the approximate structure of a lesson in teaching children robotics are presented. The features of the organization of some competitions in robotics, types of tasks for tournament participants, organizational stages are described. The characteristics of the project direction in robotics, the approximate structure of the student's project are given. The importance of each of the considered directions of teaching robotics for the development of the student, its place in the system of general education and the system of additional education is indicated. All three described directions of robotics are in demand among children and parents, teachers, but, despite this, most often only competitive and creative design is implemented in the system of additional education. Unfortunately, in this case, a large number of children capable of robotics may not be identified for objective reasons, and, therefore, lost to engineering professions in the future. Thus, the modern system of basic education faces an acute task of introducing elements of educational robotics into the educational process.
What’s in a name and does CUI matter?

In this paper, I provide some thoughts on the moniker that adorns our conference and technology: CUI. The name has proven to be problematic, as evidenced through many provocation papers in prior CUI conferences. The name presents a mismatch or a promise that is not necessarily fulfilled by the technology. But what of this? The name serves us well as a provocation itself, to think and reflect. However, we must still be cautious: to learn from the past, pre-occupying ourselves with metaphor risks a poor user experience.
Leaving the Butler Behind: The Future of Role Reproduction in CUI

Speech technologies are increasing in popularity by offering new interaction modalities for their users. Despite the prevalence of these devices, and the rapid improvement of the underlying technology, how we actually interact with these devices has remained wrapped up in the metaphors of command and control based around the problematic reproduction of the role of butler, maid, or personal assistant. In this paper we explore the issues around focusing our development and research on making a 'better' subordinate, and point to some opportunities to replace and refresh the status quo.
An Innovative Smart Grid Framework for Integration and Trading

Smart Grids (SGs) are at the forefront of the renewable resources transformative change for power generation. Due to its decentralised energy generation approach and potential reduction of the cost of power, its relevance for the energy sector is insurmountable. However, new business models and processes are necessary, and they come with integration, implementation, and operation-specific challenges. This work offers a broad analysis of SG’ main architectural aspects concerning security issues, integration bottlenecks and standardisation shortcomings in the development of an efficient platform for local energy (generation and storage) surplus trading. Through a multi-layered smart grid architecture description, this work develops an in-depth depiction of the interoperability between these layers (from top business layer passing through information and communications layers, and down to generation and storage layers). Therefore, this paper encompasses a comprehensive framework to address central smart grids design aspects and suggests a path to integrate the smart grid components into a cohesive and manageable trading platform. Finally, this work demonstrates how the proposed framework can be applied to real market study cases to highlight its solutions, provide a critical evaluation of potential implementation pitfalls, and identify opportunities for further stateof-the-art research.