Reviewerly/exHarmony huggingface dataset:
Characterize the Speed of a Photon-Number-Resolving Superconducting Nanowire Detector

Superconducting series nanowire single-photon detectors (SNDs) have been used for resolving photon numbers. But it can also exhibit a higher counting rate than a single superconducting nanowire single-photon detector (SNSPD), because the serial architecture allows multiple detectors working in parallel and the inductance of each pixel is reduced. In this paper, we found each pixel of the SND could reset fast although the whole detector took a much longer time for recovering. Therefore, the speed of an SND with a large kinetic inductance of 1.25 μH was characterized and compared to a standard SNSPD with the same total inductance. By measuring the detected photon rate over the input photon rate, we obtained a photon counting rate enhancement by 7.5 times. Meanwhile, the timing jitter of the SND was 63 ps at single photon detection regime. Our results show that the series structure and the readout scheme are quite practical in realizing high speed single-photon detectors besides resolving photon numbers.
CopyBERT: A Unified Approach to Question Generation with Self-Attention

Contextualized word embeddings provide better initialization for neural networks that deal with various natural language understanding (NLU) tasks including question answering (QA) and more recently, question generation (QG).Apart from providing meaningful word representations, pre-trained transformer models, such as BERT also provide self-attentions which encode syntactic information that can be probed for dependency parsing and POStagging.In this paper, we show that the information from self-attentions of BERT are useful for language modeling of questions conditioned on paragraph and answer phrases.To control the attention span, we use semidiagonal mask and utilize a shared model for encoding and decoding, unlike sequence-tosequence.We further employ copy mechanism over self-attentions to achieve state-of-the-art results for question generation on SQuAD dataset.
The Unreasonable Volatility of Neural Machine Translation Models

Recent works have shown that while Neural Machine Translation (NMT) models achieve impressive performance, questions about understanding the behaviour of these models remain unanswered.We investigate the unexpected volatility of NMT models where the input is semantically and syntactically correct.We discover that with trivial modifications of source sentences, we can identify cases where unexpected changes happen in the translation and in the worst case lead to mistranslations.This volatile behaviour of translating extremely similar sentences in surprisingly different ways highlights the underlying generalization problem of current NMT models.We find that both RNN and Transformer models display volatile behaviour in 26% and 19% of sentence variations, respectively.
Geochemical approach in assessing the technogenic impact on soils

The soil assessment was carried out in the technogenically-affected area of Irkutsk Oblast with the geochemical approach as a key geoecological method using physical and chemical techniques of analysis and ecodiagnostics. Diagnostic signs of the disturbed natural properties of the soil were revealed up to a depth of 40 cm in the profile based on macro- and micromorphometric parameters. The content of heavy metals (HM) – Pb, Zn, Hg, and Cu with an excess of standards was determined, and empirical HM – pH correlations were obtained by statistical clustering of the data array. The contributions of additional factors affecting the chemical element distribution in the soil layer were investigated. Significant soil contamination with sulfates and the possibility of implementing the ion-exchange of HM andfor element immobilization were revealed. It was shown that reactions with sulfates and the influence of pH, HM exchange processes involving mobile K and P can determine the nature of the described chemical element distribution in the multi-factor-contaminated technogenic soil. However, the effectiveness of such types of interaction is different for each metal and also depends on the quantitative ratio of substances and soil characteristics, even under a minor change in pH. Two-parameter correlations of HM distribution in sulfate-contaminated soils confirmed the different degrees of involvement of chemical elements in these types of interactions. The results obtained and the identified factors are of applied significance and can be used as the basis for geoecological differentiation of the contaminated soil, as well as for determining local geochemical fields in the technogenesis zone. Areas of advanced research are related to three-dimensional modeling for a more complete study of the cause-and-effect relationships of geochemical parameters.
A proposed system for opinion mining using machine learning, NLP and classifiers

In today’s life consumer reviews are the part of everyday life. User read the reviews before purchase, or stores it for finding the best product through comparison of the product review. From customers view point the reviews play vital role to make a decision regarding an online purchase as well as spammers to write the fake reviews which can increase or defame the reputation of any product. Spammers are using these platforms illegally for financial benefits/incentives are involved in writing fake reviews and they are trying to achieve their motive in terms of financial or to defeat the competitor which causes an explosive growth of sentiment/opinion spamming of writing forged/fake reviews. The present studies and research are used to analyse and categorize the opinion spamming into three different detection targets opinion spam, spammers, and to find the collusive opinion spammer groups so that false opinions can be avoided. Opinion spamming further divided into three different types based on textual and linguistic, behavioral, and relational features. The motivation behind this work is to study the dynamics of spam diffusion and extract the latent features that fuel the diffusion process. The user-based features and content-based features have been used for the categorization of spam/non-spam content. The contributions of this work are building the datasetwhich assists as the ground-truth for classifying/analyzing the variation of fraud/genuine and non-spam/spam information diffusion and to analyze the effects of topics over the diffusibility of non-spam and spam evidences/information. The paper, carried out an in-depth analysis of Twitter Spam diffusion.
On the Security Analysis of a Hopfield Chaotic Neural Network-Based Image Encryption Algorithm

In this paper, the security analysis of a color image encryption algorithm based on Hopfield chaotic neural network is given. The original chaotic image encryption algorithm includes permutation encryption and diffusion encryption. The result of cryptanalysis shows that the chaotic sequences generated by this algorithm are independent of plaintext image, and there exist equivalent permutation key and equivalent diffusion key. Therefore, according to chosen-plaintext attack, the equivalent diffusion key and the equivalent permutation key can be obtained by choosing two special plaintext images and the corresponding ciphertext images, respectively, and the plaintext image is further recovered from the ciphertext image. Theoretical analysis and numerical simulation experiment results verify the effectiveness of the analytical method. Finally, some improved suggestions for the original encryption algorithm are proposed to promote the security.
Blockchain – the accounting perspective

Abstract Blockchain remains still an experimental technology, with current applications addressing only some elements of small-scale projects. Once the technology matures however, it has the potential to transform industries and even the economy, as it begins to integrate complementary technologies such as AI and IoT. It has the potential to bring fundamental changes to financial accounting and auditing, and even to entire financial markets. This paper explores the potential for blockchain applications in accounting, identifying major benefits and shortcomings, and analysing opportunities and possible threats. We investigate mainly the organizational challenges raised by an accounting blockchain systems and its potential to enhance the accounting activity. We discuss blockchain’s potentially disruptive effect on the accounting profession, and conversely, the potential role of this professions in further developing blockchain technology.
A Context-Based Disambiguation Model for Sentiment Concepts Using a Bag-of-Concepts Approach

With the widespread dissemination of user-generated content on different social networks, and online consumer systems such as Amazon, the quantity of opinionated information available on the Internet has been increased. One of the main tasks of the sentiment analysis is to detect polarity within a text. The existing polarity detection methods mainly focus on keywords and their naive frequency counts; however, they less regard the meanings and implicit dimensions of the natural concepts. Although background knowledge plays a critical role in determining the polarity of concepts, it has been disregarded in polarity detection methods. This study presents a context-based model to solve ambiguous polarity concepts using commonsense knowledge. First, a model is presented to generate a source of ambiguous sentiment concepts based on SenticNet by computing the probability distribution. Then the model uses a bag-of-concepts approach to remove ambiguities and semantic augmentation with the ConceptNet handling to overcome lost knowledge. ConceptNet is a large-scale semantic network with a large number of commonsense concepts. In this paper, the point mutual information (PMI) measure is used to select the contextual concepts having strong relationships with ambiguous concepts. The polarity of the ambiguous concepts is precisely detected using positive/negative contextual concepts and the relationship of the concepts in the semantic knowledge base. The text representation scheme is semantically enriched using Numberbatch, which is a word embedding model based on the concepts from the ConceptNet semantic network. The proposed model is evaluated by applying a corpus of product reviews, called Semeval. The experimental results revealed an accuracy rate of 82.07%, representing the effectiveness of the proposed model.
e-JAVA Chatbot for Learning Programming Language: A Post-Pandemic Alternative Virtual Tutor

A chatbot is a tool that provides online communication platform with an agent and is usually used by organizations as a customer service agent to promote their products and deliver help services on their web, apps or instant messaging platforms like Telegram and WhatsApp.It also has been increasingly used in education to assist students as a virtual tutor in different subject areas.However, there are many questions about the potential and the limits of the use of chatbots for learning a programming language.Nowadays, as education is shifting towards Education 4.0, its system must adapt to new circumstances and changes in roles.The use of chatbot as a substitution in the process of learning may act as a virtual learning tutor to fulfil the need of education towards Education 4.0.This project exploits a rule-based technique, to generate a solution for finding a suitable control structure for a given computational problem.While text matching is deployed to automatically give instant responses to the users based on template-based questions such as greetings and general theoretical questions.The initial purpose of this project is to develop a virtual tutoring tool that provides support to students on JAVA programming language problems.The result shows there is a significant output produced by the e-JAVA chatbot.
Use of the Analytical Hierarchy Process to Determine the Steering Direction for a Powered Wheelchair

The Analytical Hierarchy Process (AHP) is utilized to propose a driving course for a powered-wheelchair. A safe route for a wheelchair is proposed by a decision-making system that aims to avoid obstacles. Two ultrasonic transceivers are fitted onto a wheelchair. The area in front of a wheelchair is segmented to left and right zones. The system inputs are distance to an object from the midpoint of the chair, distance to an object from the left of the chair and distance to an object from the right of the chair. The resulting route is a blend between a provided direction from a user’s input device and a proposed direction from the decision-making system that steers a powered-wheelchair to safely avoid obstacles in the way of the wheelchair. The system helps a disabled user to navigate their wheelchair by deciding on a direction that is a compromise between a direction provided by the sensors and a direction desired by the driver. Sensitivity analysis investigates the effects of risk and uncertainty on the resulting directions. An appropriate direction is identified but a human driver can over-ride the decision if necessary.
Cubature Ensemble Kalman Filter for Highly Dimensional Strongly Nonlinear Systems

The Ensemble Kalman filter (EnKF) was introduced for highly dimensional systems, but it has poor performance in the presence of strong nonlinearities. The Cubature Kalman filter (CKF) has outstanding performance in strongly nonlinear systems, however, it is limited by high dimensionality. In this work, we provide a comparison between the EnKF and the CKF to elaborate the problems of each scheme in highly dimensional strongly nonlinear systems. To address these problems, we introduce a Cubature Ensemble Kalman filter (CEnKF) that is a combination between both types of filters making it more suitable for highly dimensional strongly nonlinear systems. These algorithms are tested by extensive computer simulations on several models for chaotic dynamical systems. In addition, the computational complexity of the CKF/EnKF/CEnKF is analyzed. Simulation results show that the CEnKF, given a large enough ensemble size, can perform better than the EnKF and CKF for highly dimensional strongly nonlinear systems with high measurement noise intensity. The CEnKF also has better stability than the EnKF, and it performs as good as the CKF with a large enough ensemble size.
Intelligent Controller Design by the Artificial Intelligence Methods

With the rapid growth of sensor networks and the enormous, fast-growing volumes of data collected from these sensors, there is a question relating to the way it will be used, and not only collected and analyzed. The data from these sensors are traditionally used for controlling and influencing the states and processes. Standard controllers are available and successfully implemented. However, with the data-driven era we are facing nowadays, there is an opportunity to use controllers, which can include much information, elusive for common controllers. Our goal is to propose a design of an intelligent controller–a conventional controller, but with a non-conventional method of designing its parameters using approaches of artificial intelligence combining fuzzy and genetics methods. Intelligent adaptation of parameters of the control system is performed using data from the sensors measured in the controlled process. All parts designed are based on non-conventional methods and are verified by simulations. The identification of the system’s parameters is based on parameter optimization by means of its difference equation using genetic algorithms. The continuous monitoring of the quality control process and the design of the controller parameters are conducted using a fuzzy expert system of the Mamdani type, or the Takagi–Sugeno type. The concept of the intelligent control system is open and easily expandable.
DEEP SEMANTIC SEGMENTATION FOR THE OFF-ROAD AUTONOMOUS DRIVING

Abstract. This paper is devoted to the problem of image semantic segmentation for machine vision system of off-road autonomous robotic vehicle. Most modern convolutional neural networks require large computing resources that go beyond the capabilities of many robotic platforms. Therefore, the main drawback of such models is extremely high complexity of the convolutional neural network used, whereas tasks in real applications must be performed on devices with limited resources in real-time. This paper focuses on the practical application of modern lightweight architectures as applied to the task of semantic segmentation on mobile robotic systems. The article discusses backbones based on ResNet18, ResNet34, MobileNetV2, ShuffleNetV2, EfficientNet-B0 and decoders based on U-Net and DeepLabV3 as well as additional components that can increase the accuracy of segmentation and reduce the inference time. In this paper we propose a model using ResNet34 and DeepLabV3 decoding with Squeeze &amp;amp; Excitation blocks that was optimal in terms of inference time and accuracy. We also demonstrate our off-road dataset and simulated dataset for semantic segmentation. Furthermore, we present that using pre-trained weights on simulated dataset achieves to increase 2.7% mIoU on our off-road dataset compared pre-trained weights on the Cityscapes. Moreover, we achieve 75.6% mIoU on the Cityscapes validation set and 85.2% mIoU on our off-road validation set with a speed of 37 FPS for a 1,024×1,024 input on one NVIDIA GeForce RTX 2080 card using NVIDIA TensorRT.
Patterns in Random Permutations

Every k entries in a permutation can have one of k! different relative orders, called patterns. How many times does each pattern occur in a large random permutation of size n? The distribution of this k!-dimensional vector of pattern densities was studied by Janson, Nakamura, and Zeilberger (2015). Their analysis showed that some component of this vector is asymptotically multi-normal of order $$1/\sqrt n $$ , while the orthogonal component is smaller. Using representations of the symmetric group, and the theory of U-statistics, we refine the analysis of this distribution. We show that it decomposes into k asymptotically uncorrelated components of different orders in n, that correspond to Sk-representations. Some combinations of pattern densities that arise in this decomposition have interpretations as practical nonparametric statistical tests.
Grand Tour Algorithm: Novel Swarm-Based Optimization for High-Dimensional Problems

Agent-based algorithms, based on the collective behavior of natural social groups, exploit innate swarm intelligence to produce metaheuristic methodologies to explore optimal solutions for diverse processes in systems engineering and other sciences. Especially for complex problems, the processing time, and the chance to achieve a local optimal solution, are drawbacks of these algorithms, and to date, none has proved its superiority. In this paper, an improved swarm optimization technique, named Grand Tour Algorithm (GTA), based on the behavior of a peloton of cyclists, which embodies relevant physical concepts, is introduced and applied to fourteen benchmarking optimization problems to evaluate its performance in comparison to four other popular classical optimization metaheuristic algorithms. These problems are tackled initially, for comparison purposes, with 1000 variables. Then, they are confronted with up to 20,000 variables, a really large number, inspired in the human genome. The obtained results show that GTA clearly outperforms the other algorithms. To strengthen GTA’s value, various sensitivity analyses are performed to verify the minimal influence of the initial parameters on efficiency. It is demonstrated that the GTA fulfils the fundamental requirements of an optimization algorithm such as ease of implementation, speed of convergence, and reliability. Since optimization permeates modeling and simulation, we finally propose that GTA will be appealing for the agent-based community, and of great help for a wide variety of agent-based applications.
A Collaborative Augmented Reality Application for Training and Assistance during Shipbuilding Assembly Processes

This paper presents the development of a novel Microsoft HoloLens collaborative application that allows shipyard operators to interact with a virtual clutch during its assembly in a real Turbine workshop.Such an Augmented Reality (AR) experience acts as a virtual guide while assembling different parts of a ship.In particular, the proposed application allows operators to position the clutch on a real environment and interact with it.The application also provides information about the documentation of each part of the clutch, showing its blueprints and physical measurements.The proposed AR application enables collaborative AR experiences, allowing users to visualize the same content and animations at the same time and interact simultaneously with 3D objects from multiple devices.Furthermore, the application is integrated with an Industrial Internet of Things (IIoT) framework, resulting on an AR-IIoT application that is able to receive and display real-time sensor data on information panels, as well as to trigger actions through actuators by making use of virtual user interfaces.
A Deep Learning Based Prediction of Arabic Manuscripts Handwriting Style

With the increasing amounts of existing unorganized images on the internet today and the necessity to use them efficiently in various types of applications. There is a critical need to discover rigid models that can classify and predict images successfully and instantaneously. Therefore, this study aims to collect Arabic manuscripts images in a dataset and predict their handwriting styles using the most powerful and trending technologies. There are many types of Arabic handwriting styles, including Al-Reqaa, Al-Nask, Al-Thulth, Al-Kufi, Al-Hur, Al-Diwani, Al-Farsi, Al-Ejaza, Al-Maghrabi, Al-Taqraa, etc. However, the study classified the collected dataset images according to the handwriting styles and focused on only six types of handwriting styles that existed in the collected Arabic manuscripts. To reach our goal, we applied the MobileNet pre-trained deep learning model on our classified dataset images to automatically capture and extract the features from them. Afterward, we evaluated the performance of the developed model by computing its recorded evaluation metrics. We reached that MobileNet convolutional neural network is a promising technology since it reached 0.9583 as the highest recorded accuracy and 0.9633 as the average F-score
Calibration of mixer amplitude and phase imbalance in superconducting circuits

An important device for modulation and frequency translation in the field of circuit quantum electrodynamics is the in-phase and quadrature mixer, an analog component for which calibration is necessary to achieve optimal performance. In this paper, we introduce techniques originally developed for wireless communication applications to calibrate upconversion and downconversion mixers. A Kalman filter together with a controllable carrier frequency offset calibrates both mixers without removing them from the embedding measurement infrastructure. These techniques can be embedded into room temperature control electronics and hopefully find widespread use as circuit quantum electrodynamics devices continue to grow in complexity.