Reviewerly/exHarmony huggingface dataset:
Classification of small triorthogonal codes

Triorthogonal codes are a class of quantum error correcting codes used in magic state distillation protocols. We classify all triorthogonal codes with $n+k \le 38$, where $n$ is the number of physical qubits and $k$ is the number of logical qubits of the code. We find $38$ distinguished triorthogonal subspaces and show that every triorthogonal code with $n+k\le 38$ descends from one of these subspaces through elementary operations such as puncturing and deleting qubits. Specifically, we associate each triorthogonal code with a Reed-Muller polynomial of weight $n+k$, and classify the Reed-Muller polynomials of low weight using the results of Kasami, Tokura, and Azumi and an extensive computerized search. In an appendix independent of the main text, we improve a magic state distillation protocol by reducing the time variance due to stochastic Clifford corrections.
Increasing the effectiveness of intelligent module by enlarging training dataset from real data

This paper concentrates on the design of the intelligent module and neural networks in the area of intelligent data processing. It raises the following issues: what real problems are faced in training datasets for neural networks, how they can be solved, and how an intelligent module can be useful in this area. During the development of a neural network system, it is important to improve the network performance after the system’s release. Additional training data need to be taken from real-life data, combined with existing training data to produce a better version of the neural network’s weights. Since the number of these samples can be large, it’s useful to filter out samples similar to those already included in training data. We designed a module architecture and an algorithm that employ cascading filters in order to find the best samples from real data for the training dataset. The key feature of the intelligent module is that it does not generate a completely new dataset, but uses saved data from the real-life samples and samples from the existing database. Weights are then updated during the training of the neural network and then filtered using a special algorithm.
Internet of UAVs Based Remote Health Monitoring: An Online eHealth System

The recent COVID-19 pandemic has brought significant challenges to the traditional healthcare industry. A novel eHealth system is required to cope with infectious and chronic diseases. This article proposes an online eHealth system to monitor patients and provides edge computing services based on the Internet of Unmanned Aerial Vehicles (UAVs). In order to minimize the health monitoring latency and guarantee the resource utilization efficiency of UAVs, the Lyapunov optimization method is utilized to decompose the long-term optimization problem into a series of instantaneous optimization problems. Then the decomposed sub-problem is formulated as a minimum cost maximum flow problem by constructing a bipartite graph that maps medical analysis requests to target UAVs. Extensive experimental results demonstrate the effectiveness of our system. Finally, system analyses illustrate that the robustness and scalability of our eHealth system is favorable, and our system can provide agile edge computing services for dispersed patients.
Self-Adaptive Synergistic Optimization for Parameters Extraction of Synchronous Reluctance Machine Nonlinear Magnetic Model

For mechanism analysis and high-performance control of synchronous reluctance machine (SynRM), accurate and reliable parameter identification of nonlinear magnetic model is always required. However, the accuracy and robustness of traditional heuristic algorithms are restricted by incomplete individual performance evaluation and single population evolution mechanism. In this paper, we propose a self-adaptive synergistic optimization (SSO) algorithm for extracting the parameters of the model. A novel synergistic-performance evaluation is first established to classify candidates automatically. Then, a self-organized mechanism is proposed to select optimal evolution strategies designed for classified candidate solutions. Around the current best candidate, the exploration is guaranteed in priority. Meanwhile, a self-adaptive mechanism is introduced to select other candidates to construct more promising evolutionary direction. Thus, achieving a good balance between exploration and exploitation. The parameter estimation performance of SSO algorithm is evaluated through standard datasets of SynRM magnetic model obtained by the finite element analysis. Comprehensive experiment results demonstrate the competitiveness and effectiveness of the proposed SSO algorithm compared with other algorithms, especially in terms of the accuracy and robustness. According to these superiorities, it can be concluded that the proposed algorithms are promising parameter identification methods for SynRM nonlinear magnetic model.
Detection of Malaria Parasites in Thin Red Blood Smear Using a Segmentation Approach with U-Net

Malaria is infected by Plasmodium parasite which was a plague in many countries in the world. An alternative way to decrease the number of deaths caused by malaria parasites was that conducting advanced diagnostic for detecting malaria parasite. However, this diagnostic required expert skills and was inclined to human errors. In this situation, automation of this process could greatly help lot of laboratory to handle the disease. In this paper, we presented an automated way to segment the Plasmodium parasites infected in red blood cells using U-net. We conducted our training approach in the three different color spaces which are RGB, HSV and GGB. The proposed method achieved accuracy of 0.9940, 0.9936 and 0.9947 in RGB, HSV and GGB color space respectively.
SPPS: Secure Policy-based Publish/Subscribe System for V2C Communication

The Publish/Subscribe (Pub/Sub) pattern is an attractive paradigm for supporting Vehicle to Cloud (V2C) communication. However, the security threats on confidentiality, integrity, and access control of the published data challenge the adoption of the Pub/Sub model. To address that, our paper proposes a secure policy-based Pub/Sub model for V2C communication, which allows to encrypt and control the access to messages published by vehicles. A vehicle encrypts messages with a symmetric key while saving the key in distributed shares on semi-honest services, called KeyStores, using the concept of secret sharing. The security policy, generated by the same vehicle, authorizes certain cloud services to obtain the shares from the KeyStores. Here, granting access rights takes place without violating the decoupling requirement of the Pub/Sub model. Experimental results show that, besides the end-to-end security protection, our proposed system introduces significantly less overhead (almost 70% less) than the state-of-the-art approach SSL when reestablishing connections, which is a common scenario in the V2C context due to unreliable network connection.
Robotics and Automation: The Rescuers of COVID Era

Today, when whole world is fighting against novel coronavirus and some countries are struggling to control this pandemic, robotics and automation can play a significant role not only by saving people but can also aid in controlling and preventing the spread of the virus. These advanced technologies are not only being applied widely to pharmaceutical and medical device manufacturing processes but are also helping the doctors and nurses in identifying and diagnosing the COVID patients. In the era of science and technology, people have realized the importance of robotics and automation and are using these technologies exclusively in field of education, grocery stores, restaurants and food delivery outlets etc. to maintain the norm of social distancing. Due to rapid increase in the number of epidemics and now this corona pandemic, demand for robotics and automation has increased manifold in healthcare, medical and pharmaceutical sectors due to shortage of medical staff and equipments and fear of spreading the disease from one person to another. Moreover, robots can also be used by the manufacturing industries to revive their production and to reduce the impact of present and future epidemic challenges. Although the use of robots and automation is not new, but people still have certain apprehensions of using these technologies on large scale. Some of the problems associated with robotics and automation are lack of funding, high initial cost, reduction in job, adaptability, lack of human interaction, regular updation, cyber virus threats etc. To deal with coronavirus robots and automation are being used in different forms and types such as telerobots for interaction and communication with patients, medical robots for disinfection purpose, drones and patrol robots for public safety, robot vans for delivering food and other essential items, 3D printing for manufacturing medical equipments etc. We cannot predict how long this coronavirus will last, but these cutting-edge technologies can be used for the revival of industrial production, and it’s high time for manufacturing industries to upgrade their production and assembly lines with automation tools and digital technologies for safe and continuous operations. These technologies can be used with aim to decrease the workload of existing workforce and not to replacing them. Existing robots can be repurposed to save time and money and there is no need for stockpile the robots which can make them outdated. It is very well known that resources cannot be developed in one day. It takes number of years to build them so that they can be used in case of emergency. So, it is high time that governments and agencies all over the world should come up with a proper plan to integrate robotic and automation with the existing workforce for the fight against the next pandemic and enhancing the efficiency.
Optimum HEVC Quantization Parameter for Cloud Gaming

Cloud Gaming (CG) is getting its popularity amongst teenagers, since it opens a new opportunity to play video games without using consoles. Despite being so interesting, it has encountered serious challenges that may prevent it from being widely used by the gamers. Since the gamers can rarely exploit a dedicated bandwidth and the network is not normally manageable at the gamer's side, satisfying the Quality of Service (QoS) and Quality of gamer Experiences (QoE) are very difficult. In this paper, the goal is to find an encoder option which satisfies the acceptable QoE for the gamers with minimum achievable bandwidth. Two High Efficiency Video Coding (HEVC) encoder profiles, namely main and main10 have been investigated for the experiments. The experimental results showed that main10 profile with better coding gain achieved a remarkable performance on CG compared to main profile and a better QoE has been achieved by a lower data rate.
Privacy Preserving Text Representation Learning Using BERT

The availability of user generated textual data in different activities online, such as tweets and reviews has been used in many machine learning models. However, the user generated text could be a privacy leakage source for the individuals' private-attributes. In this paper, we study the privacy issues in the user generated text and propose a privacy-preserving text representation learning framework, $${DP}_{BERT}$$ , which learns the textual representation. Our proposed framework uses BERT to extract the sentences embedding to learn the textual representation that (1) is differentially private to protect against identity leakage (e.g., if a target instance in the data or not), (2) protects against leakage of private-attributes information (e.g., age, gender, location), and (3) maintains the high utility of the given text.
Explainable Artificial Intelligence-Based Competitive Factor Identification

Competitor analysis is an essential component of corporate strategy, providing both offensive and defensive strategic contexts to identify opportunities and threats. The rapid development of social media has recently led to several methodologies and frameworks facilitating competitor analysis through online reviews. Existing studies only focused on detecting comparative sentences in review comments or utilized low-performance models. However, this study proposes a novel approach to identifying the competitive factors using a recent explainable artificial intelligence approach at the comprehensive product feature level. We establish a model to classify the review comments for each corresponding product and evaluate the relevance of each keyword in such comments during the classification process. We then extract and prioritize the keywords and determine their competitiveness based on relevance. Our experiment results show that the proposed method can effectively extract the competitive factors both qualitatively and quantitatively.
Presupposition, attention and cognitive load

The paper deals with the relation between linguistic presupposition and epistemic vigilance. It proposes evolutionary and cognitive reasons why presuppositions should induce shallower processing, that proves effective in persuasion and manipulation. It shows examples of presuppositions being associated with reduced critical attention from commercial advertising and political propaganda. It summarizes the available experimental verification of this assumption, commenting on the fact that behavioural evidence confirms reduced attention for presupposed contents, while neurophysiological evidence, on the contrary, shows increased processing effort. An explanation for the apparent contradiction is proposed, namely that the measured effort accounts for the mismatch between the cognitive and contextual status of new information and its being linguistically presented as presupposed. The fact that processing resources are devoted to repairing the mentioned mismatch is seen as further reducing the attentional resources available for epistemic vigilance and critical evaluation.
Human Action Recognition for Pose-based Attention: Methods on the Framework of Image Processing and Deep Learning

This paper presents an overview of some approaches of Human action recognition (HAR) for pose-based attention. The paper focus is on algorithms that use video processing on a given dataset. A list of the best HAR datasets is given in order to show the variety of the available videos online. Local and Global feature extraction are reviewed. Also some of the most common Deep Learning methods are studied: Recurrent Neural Network (RNN), Convolutional Neural Network (CNN) and Generative Adversarial Network (GAN). All of the methods are directed to recognise the pose and the focus of the person in a recording.
GazeXR: A General Eye-Tracking System Enabling Invariable Gaze Data in Virtual Environment

Controlling and standardizing experiments is imperative for quantitative research methods. With the increase in the availability and quantity of low-cost eye-tracking devices, gaze data are considered as an important user input for quantitative analysis in many social science research areas, especially incorporating with virtual reality (VR) and augmented reality (AR) technologies. This poses new challenges in providing a default interface for gaze data in a common method. This paper propose GazeXR, which focuses on designing a general eye-tracking system interfacing two eye-tracking devices and creating a hardware independent virtual environment. We apply GazeXR to the in-class teaching experience analysis use case using external eye-tracking hardware to collect the gaze data for the gaze track analysis.
Authentication Management of Home IoT Devices

The number of IoT devices in the home has been increasing rapidly. With the popularity comes different security vulnerabilities. One of the main causes for some vulnerabilities is users' weak password management strategies. In this paper, we explored end-users' password management for home IoT devices. We conducted a literature survey examining previous works on security and privacy concerns of home IoT devices and password management. We also conducted an online survey with 93 home IoT device users to determine their security and privacy concerns, authentication management, and feature preferences for a new authentication management tool. We found out that our participants were very concerned about security/privacy issues, but they followed insecure security steps in practice. However, they were found to be welcoming towards a new security tool for managing their passwords. We used the findings to suggest design principles for the design of an authentication management tool for home IoT devices.
Sustainable Educational Robotics. Contingency Plan during Lockdown in Primary School

New technologies have offered great alternatives for education. In this context, we place robotics and programming as innovative and versatile tools that adapt to active methodologies. With the arrival of COVID-19 and lockdowns, physical resources were kept out of use, and the virtual lectures did not propose to incorporate these elements in a meaningful way. This recent situation raises as an objective of study the need to evaluate if robotics and programming are content that can be taught virtually in these circumstances, without physical resources and without face-to-face lectures. To do this, a mixed methodology consisting of questionnaires and interviews has been incorporated, aimed at primary education teachers, families, and primary education grade students. The results suggest that the virtualization of robotics and programming is a feasible and beneficial alternative for students, which allows the development of digital skills, while it is enhanced with the use of audiovisual materials and online resources. Even though face-to-face classes have other benefits not offered by virtualization, and teacher training needs to be up to the task to face this situation, it is a matter of time to respond to these situations and to guarantee a high-quality distance education.
The Implementation of a Novel Augmented Reality (AR) Based Mobile Application on Open Platform Controller for CNC Machining Operation

Manufacturing technology has reached to a point where there is a need for online platform for users' easy access. Essentially, the trend for Industry 4.0 towards automation and Cyber Physical System (CPS) has become a compulsory addition to any system, manufacturing is not an exception to the need of that technological improvement. With the number of mobile users growing by the day, open CNC system has risen to merge the idea of controlling, monitoring, and utilizing the mobile platform to its advantage. This paper proposes the implementation of an Augmented Reality (AR) based mobile application for guiding users to improve CNC machining operation as well as remotely control the system from mobile phone. The CNC system is developed from conventional PROLIGHT 1000 Milling CNC machine from Light Intelitek based on novel controller established as UTHM Open CNC Controller (UOCC) under the ISO 14649 of STEP-NC environment. The UOCC have efficient ability to operate and monitor the CNC machine from a distance which would reduce the risk of accidents and hazards from occurring while eliminating the use of paper and increasing machining efficiency.
Hybrid Manta Ray Foraging—Particle Swarm Algorithm for PD Control Optimization of an Inverted Pendulum

This paper presents a hybrid Manta ray foraging—particle swarm optimization algorithm. Manta Ray Foraging Optimization (MRFO) algorithm is a recent algorithm that has a promising performance as compared to other popular algorithms. On the other hand, Particle Swarm Optimization (PSO) algorithm is a well-known and a good performance algorithm. The proposed hybrid algorithm in this work incorporates social interaction and elitism mechanisms from PSO into MRFO strategy. The mechanisms help search agents to determine their new search direction. The proposed algorithm is tested on various dimensions and fitness landscapes of CEC2014 benchmark functions. In solving a real world engineering problem, it is applied to optimize a PD controller for an inverted pendulum system. Result of the benchmark function test is statistically analyzed. The proposed algorithm has successfully improved the accuracy performance for most of the test functions. For optimization of the PD control, result shows that the proposed algorithm has attained a better control performance compared to MRFO.
The Language of Engineering

Since the introduction of Word2Vec in 2013, so-called word embeddings, dense vector representation of words that are supposed to capture their semantic meaning, have become a universally applied technique in a wide range of Natural Language Processing (NLP) tasks and domains. The vector representations they provide are learned on huge corpora of unlabeled text data. Due to the large amount of data and computing power that is necessary to train such embedding models, very often, pre-trained models are applied which have been trained on domain unspecific data like newspaper articles or Wikipedia entries. In this paper, we present a domain-specific embedding model that is trained exclusively on texts from the domain of engineering. We will show that such a domain-specific embeddings model performs better in different NLP tasks and can therefore help to improve NLP-based AI in the domain of Engineering.
Improving the Competitiveness of Russian Universities and the Role of State Programs in the Development of Higher Education

The project aimed to increase the competitiveness of Russian universities among the world’s leading research and educational centers, which was called “5–100”, was completed in 2020. The aim of this project assumed the entry of five Russian universities into the TOP 100 world institutional rankings. However, due to the fact that there are currently a large number of international rankings among higher education institutions, both global and industry-specific or subject-specific, the indicators achieved within the framework of the “5–100” project are still being discussed by the expert community. For the further effective implementation of the national project “Science“ in terms of achieving the priority state development goals in the field of science and education, it is important to formulate proposals and mechanisms for realization the strategic academic leadership program” Priority-2030“ on the basis of available data and the results of the analysis of the experience gained, which are presented in this paper.
Efficient Utility Tree-Based Algorithm to Mine High Utility Patterns Having Strong Correlation

High Utility Itemset Mining (HUIM) is one of the most investigated tasks of data mining. It has broad applications in domains such as product recommendation, market basket analysis, e-learning, text mining, bioinformatics, and web click stream analysis. Insights from such pattern analysis provide numerous benefits, including cost cutting, improved competitive advantage, and increased revenue. However, HUIM methods may discover misleading patterns as they do not evaluate the correlation of extracted patterns. As a consequence, a number of algorithms have been proposed to mine correlated HUIs. These algorithms still suffer from the issue of the computational cost in terms of both time and memory consumption. This paper presents an algorithm, named Efficient Correlated High Utility Pattern Mining (ECoHUPM), to efficiently mine the high utility patterns having strong correlation items. A new data structure based on utility tree (UTtree) named CoUTlist is proposed to store sufficient information for mining the desired patterns. Three pruning properties are introduced to reduce the search space and improve the mining performance. Experiments on sparse, very sparse, dense, and very dense datasets indicate that the proposed ECoHUPM algorithm is efficient as compared to the state-of-the-art CoHUIM and CoHUI-Miner algorithms in terms of both time and memory consumption.