Reviewerly/exHarmony huggingface dataset:
CiPSI and CiPSINet: a New Dataset and Network for Finding Cars in Panchromatic Satellite Images

The low availability of high quality, labeled datasets of panchromatic satellite imagery featuring passenger vehicles for both academic and commercial applications led to the creation of CiPSI, a new dataset to fill exactly that gap. Using this new dataset, we evaluated six existing state-of-the-art classification networks and three detection methods. Ultimately, we introduce a novel network architecture, CiPSINet, that achieves higher performance than all other methods for vehicle classification while using fewer learnable parameters. Further, using this network, we improve the detection accuracy of the Faster R-CNN method by replacing the ResNet-50 backbone with CiPSINet.
Heavy vehicle defense procurement use cases and system design using blockchain technology

Both light and heavy vehicle technologies are required for the battlefield. This work studies the usage of heavy vehicle technologies, their transportation, and other applications in the defense landscape. Various case studies that explore vehicle technologies in the defense landscape are examined. These case studies provide the latest technological trends in different geographical regions. The proposed system eliminates one of our society’s fundamental problems: the need for a middleman. That is, we aim to propose and simulate a reliable, trustworthy, and robust network for peer-to-peer connection. This connection runs on the idea of mutual trust and dependency. The blockchain concept is used and its requirements in the field of defense procurement are depicted. Simulation of this work is performed using various resources at our disposal to ensure a working and reliable blockchain.
Research on the Method of Landscape Creation Based on Virtual Reality

Abstract The landscape creation process based on the concept of virtual reality, the landscape creation as a dynamic process, from subjective and objective two aspects discusses the evolution history of landscape creation, system creation process model. Based on the concept of process method, this paper summarizes and puts forward the concept and form, rules and operation, output and result oriented process method and related technology import mode. The overall analysis of the whole process of the innovative landscape creation has formed a comprehensive, innovative, holistic and process of the system creation method, to provide overall guidance for the further research of landscape space and form creation method. This paper discusses and analyses the application of VR technology from the aspects of landscape creation and landscape expression. On the one hand, the application of virtual reality technology in landscape creation can promote the diversification of design thinking and creation process, stimulate the multi direction thinking, further emancipate the creative thinking and improve the design quality. On the other hand, the virtual reality technology has created a more powerful expressive force with its vividness, interactivity, authenticity and accuracy, and expanded the content and meaning of the architectural expression. In addition, the author also discusses the landscape design and the essence of landscape in the virtual reality era.
Monitoring 5G networks in Security Operation Center

5G mobile network is still a new technology in the telecommunication and this new technology brings new challenges into IT security. It is essential to capture, monitor and analyze the traffic of 5G networks. The 5G network is gaining more and more popularity in the different areas of the industry and everyday life. The diversity of this new technology raises new security challenges and new vulnerabilities. Currently 5G networks are not properly protected from malicious traffic. To protect their systems and networks, companies more widely set up Security Operations Centers (SOC) for monitoring and analyzing network traffic and events. However, there is no proper methodology yet to use SOC in 5G networks. In this paper, we study the possible methods of network monitoring focusing on the RAN, especially on Radio interface and end devices of 5G networks and search for potential extension solutions to our existing SOC.
Towards an automatic SPARQL query generation from ontology competency questions

Ontologies are omnipresent and are actively used as explicit, formal, and consensual knowledge representations. Hence, ensuring ontological quality is considered a crucial issue. One of the well-known domain ontologies validation approaches proposes a set of competency questions, covering the domain represented by the ontology, translating these questions into SPARQL queries, running them on the ontology, and comparing the obtained results with the expected ones. This paper presents an approach to translate natural language competency questions into SPARQL queries to assist domain experts in testing ontologies and users considering exploring ontologies for further exploitation. The implementation and experimentation of the proposed approach are described. The resulting tool proposes a fully automatic and semi-automatic execution, enabling end-users to parameter the approach phases.
OROnto: An Ontology for Recognition of Grasping Objects

This paper reports development of an ontology using Web Ontology Language (OWL), focused to the task of object recognition by prosthetic hands. The ontology named as OROnto (Object Recognition Ontology) is comprised of attributes, concepts and relationships among the human hand grasp types and structural entities of objects. After validation by a reasoner, cases have been presented in this work where the inferred ontology was able to retrieve object types against the user’s Description Logic (DL) queries. A grasp experiment was performed to study the effectiveness of the ontological attributes towards object recognition. Classification results using data from semantically suggested features showed a 2-4% higher recognition accuracy in comparison to the results using data with the features selected by the popular random forest based feature selection method. This reveals that apart from the extraction of implicit and explicit information of the domain knowledge, ontologies can also be used as a feature selection method for classification problems.
Improved Faster R-CNN Based On CSP-DPN

For the feature extraction in deep-learning based model of object detection, although DenseNet has the strong ability of exploiting features, its learned feature may result in high redundancy. In contrast, ResNeXt can reduce the redundancy of feature effectively, however, it has the difficult to use high-level information to re-discover low-level features. In this paper, inspired by Dual Path Network (DPN), we proposed a new feature extraction strategy that make full use of DenseNet and ResNeXt to improve the feature extraction ability of original Faster R-CNN. In the feature extraction part, ResNeXt and DenseNet alternately take first half channels and second half channels of feature map to perform feature fusion, which can learn more comprehensive information and reduce model's computational complexity. In addition, we also apply the Switchable Atrous Convolution (SAC) in Faster RCNN to enlarge receptive field, which can further improve the accuracy of the object detection. We evaluated our proposed model on coco2014 dataset, the experimental results show that we achieved about 9% higher of mAP than original DPN92-based Faster R-CNN.
Fall Detection System Using IoT

Nowadays, people are most familiar with the use of the Fall Detection System, which is extensively utilized among senior individuals over the age of sixty, who come to the hospital on a regular basis with injuries caused by falls. Here, the proposed research work has improved the existing fall detection system into a wearable gadget. The proposed wearable device alerts friends and family members if an elderly person in the home is in danger, as well as a specific feature that records the elderly person’s movements and constantly evaluates it with the pre-programmed calculation in order to detect whether the elderly person has fallen or not and accordingly it notifies the responsible person about the situation. Here, the information will be notified by sending an SMS to the responsible phone number of relatives and friends.
Customization of e-textile sensory tools for people with dementia

Sensory activities are used to increase social engagement for people with dementia, who may struggle to participate in activities due to deteriorating cognitive functions. Sensory tools are used but these can be inappropriate since they are not designed for people with dementia. This paper details the design of an e-textile sensory tool for people with dementia, with stakeholders in Mainland China. The concept, a sensory wall, was derived from a previous co-design project in Hong Kong. However, the design context differs, and this work highlights barriers to implementing co-design in sensory tool design. Instead, this project took a collaborative customization approach. This paper describes the design processes and evaluations for the first and second sensory wall, with context on dementia care in Mainland China. This paper highlights considerations in the design of a sensory tool for people with dementia, and the benefits of stakeholder engagement in sensory tool design.
Indian Currency Recognition System Using CNN And Comparison With YOLOv5

Computer vision is the most anticipated technology of the 21st century. Object detection is the basic functional block in Convolution Neutral Network. The objective of the proposed methodology is to design a system to detect Indian currencies which can help visually impaired people to recognize and read out the value of all possible Indian paper currencies with more than 79.83% accuracy. In this approach, the features of the notes are extracted in separate Red Green Blue (RGB) layers, normalized and quantized into machine readable data that is later trained with Adam optimizer which gives Probabilistic Prediction of each type of currency with a loud audio output. The Convolution Neural Network model is further compared with Yolov5 model which is considered to be the fastest algorithm for object detection. After comparison the accuracy of Convolution Neural Network model was found to be just 1% lesser than that of the YOLOv5 model.
Control Design for State transition of Open Quantum System

Abstract State transition control of open quantum systems is a research hotspot and difficulty. The control method in the light of the Lyapunov stability theory is a common method. Current research shows that if the open quantum systems meet the constraints that the free Hamiltonian is strong regular and the interaction Hamiltonian is fully connected, by devising appropriate control fields in the light of the Lyapunov stability theory, the open quantum system can move to the desired final state. In view of the fact that most practical open quantum systems do not meet these two constraints, this paper propose a control method. The implicit function perturbation terms are introduced into the control fields to resolve the problem that the open quantum systems do not meet these two convergence constraints. And the main control fields are devised in the light of the Lyapunov stability theory to make the state transition of the open quantum systems converging to the desired final state.
Tree Adjoining Grammar

In Chap. 3 , we introduced the preliminaries of GP, a generic optimization tool that can generate and search for optimal solutions in a general solution space. GP is the first key ingredient that will be used to realize the conceptualized identification methodology. Recall that in the context of automated SI, the desired solution space would consist of all model structures (with varying degrees of model complexity) that are relevant for the identification task at hand. Furthermore, when the user has prior knowledge of, or preference for the desired model structure, it should be possible to suitably adapt the search space of the desired SI method. These ideas connect to Research Sub-questions 1.1 and 1.2 proposed in Chap. 1 .
Immersive Virtual Training for Vocational Training High School Students’ Milling Machine Practice

The aim of this study was to evaluate if virtual reality (VR) technology is an effective medium for non-contact practice and explore the benefits of VR education and training for vocational training. We designed and tested a VR system that interacts with natural sensory recognition in a computer-generated virtual world for vocational training high school students. The proposed virtual training system was designed so students can learn how to operate the technology and handle the milling machine in the field. A milling machine is a mandatory training device in industrial education. Experienced vocational training teachers evaluated the usefulness of the VR content as an educational technology medium for vocational students. Their satisfaction with the VR-based training system as an educational media was relatively high. They reported that the proposed system included the necessary information for education and training for the milling machine learning bur they also made suggestions for more detailed content. More than 70% teachers hoped to use the VR system in future classes and responded that it was very appropriate as an educational media. The results indicate that the proposed virtual training system for milling machines is an effective training and medium that can be used for hands-on training if after further refining the scenarios for detailed operation.
How is the demand for important professional qualities of a future doctor changing in the context of digitalization?

Aim . To analyze, revise and supplement important professional qualities of a future doctor in the context of digitalization. Material and methods . The study involved 74 1st year students of General Medicine faculty of Krasnoyarsk State Medical University. A doctor’s self­assessment form was used, which includes a list of important professional qualities (IPQs) of a doctor in three blocks. The ranking method was used to measure the significance of doctor’s IPQs, taking into account the preferences of students. An assessment of the IPQs of future specialists was carried out using a heuristic evaluation method based on self­-assessments, classmates’ and expert assessments. Mathematical data processing was carried out using Excell. Results . The analysis showed that 82,14% of future specialists identified the first rank of IPQ block as contributing to development of professional culture of doctors. The second rank was assigned by 67,86% to IPQ block related to social responsiveness of doctors, while 75% of students assigned the third rank to IPQ block related to information competence. According to the average value of heuristic self­-assessments, the IPQs of social responsiveness of a doctor are optimally formed (5,72), while the IPQs of working with information (4,27) and doctor professional culture (3,50) are underdeveloped. Conclusion . Despite the digitalization of medicine, the social role of a doctor remains the same. The conducted research has revealed the following tendencies. Future specialists understand the demand for IPQs that contribute to the social responsiveness of doctors. Students recognize that they have the IPQs necessary to interact with patients at an optimal level. Underdeveloped IPQs were those related to working with data, digital etiquette, and practical experience of working in the digital space. In addition, it was revealed that the IPQs related to the development of doctor professional culture are insufficiently formed. The list of the doctor’s IPQs was revised and supplemented with new competencies related to digital interaction.
Target Aggregation Regression based on Random Forests

In the era of big data, data volume is growing explosively, and the proportion of unlabeled data is increasing due to the high cost of label acquisition and privacy protection policy. Therefore, weakly labeled learning has drawn much attention to recognize patterns in the real-world data. Learning with target aggregation is an emerging technique in the last few years. However, to the best of our knowledge, the target aggregation regression problem has not been discussed. In this paper, we make use of random forest to build regression model for data with aggregated targets. The experiment shows the promising result of the proposed model.
Using Digital Technologies to Implement Advanced Professional Education Programs

Advanced professional education is an integral part of the lifelong education system and training “throughout life”. Authors analyze the experience of the Kazan National Research Technological University in the implementation of continuing education programs, taking into account the exceptional circumstances of the year 2020. Although in recent years technical universities have gained extensive experience in the implementation of educational programs using information and communication technologies and e-learning, the lock-down and distance learning have brought many innovations in educational activities. These processes also touched upon advanced professional education, advanced training of engineers, as well as the development of competence and competencies of the engineering universities teachers themselves. The preliminary analysis shows that training with the predominant use of digital technologies has many advantages for students of the continuing education system, provides them with the new opportunities to update their professional knowledge and acquire new professional skills in the most accessible form, using new digital formats and digital educational resources. It is obvious that the activities of educational organizations aimed to development of digital competencies of teachers in 2020 brought measurable results on the basis of the previously created foundation for distance learning.
The tyranny of student satisfaction: Cruel optimistic fantasies in education

In this theoretical paper, we reflect on the optimistic neoliberal fantasies that are played out in today’s education, even though that they rarely live up to their promises. Inspired by Lauren Berlant’s concept of cruel optimism, and psychoanalytical thinkers as Slavoj Žižek and Ilan Kapoor, and their focus on concepts such as fantasy, desire, enjoyment, and the unconscious, we argue that there is a contemporary tendency for critical thinking and complicated conversations to be neglected or avoided in education, especially these forms of thinking and conversations that can question the neoliberal fantasmatic order and the cruelnss as well as the enjoyment that come with it. Instead, educational institutions and educators must live up to the demands of the big (neoliberal market) Other and its desire for positive student evaluations that mirror satisfaction and quality regarding the educational “product” that students are promised. How it looks will be illustrated by means of examples that derive from a Danish educational context. On that basis, we claim that we are witnessing a form of satisfaction tyranny in education and discuss what it means if educational institutions and educators cannot release themselves from their neoliberal involvement.
An Enhanced Lattice Algorithm for Range Estimation Using Noisy Measurement With Phase Ambiguity

The problem of range estimation based on noisy measurements with phase ambiguity can be solved using signals across multiple frequencies by existing algorithms based on either the Chinese Remainder Theorem or lattice theory. The performances of these algorithms are constrained by the trade-off between SNR of measurements and required estimation accuracy. In this paper, we firstly propose an algorithm to find a set of wavelengths satisfying the conditions to apply the closed-form lattice algorithm from a given interval. Then we propose an enhanced lattice algorithm for range estimation based on existing lattice approaches. In the conventional lattice algorithm, the selection of an integer vector by the estimator often fails to be the ground truth because of noise. As a result, the true value can be a neighbouring lattice point of the estimated point where neighbours are defined in terms of a distance depending on the noise characteristics. In this paper, all neighbouring lattice points of the estimated point obtained by a conventional lattice algorithm are regarded as candidates for the new estimate. Then an efficient estimator is presented to choose one of those candidates to be the new estimate. The performance of the proposed algorithm, i.e. the successful probability of reconstruction, is derived mathematically. Additionally, a computation-reduced algorithm is presented to reduce the overhead of computation. The calculated probability and simulation results demonstrate the efficiency of the proposed algorithm.
Software Psychology of Control Software Design and Development

This paper discusses the psychological aspects of control software design and development and information complexity concept. In order to demonstrate the usefulness of the proposed concept, we show features of programming control systems, give the basics of process-oriented programming, and list its advantages.
Attack Path Analysis and Cost-Efficient Selection of Cybersecurity Controls for Complex Cyberphysical Systems

The increasing integration of information technology with operational technology leads to the formation of Cyber-Physical Systems (CPSs) that intertwine physical and cyber components and connect to each other. This interconnection enables the offering of functionality beyond the combined offering of each individual component, but at the same time increases the cyber risk of the overall system, as such risk propagates between and aggregates at component systems. The complexity of the resulting systems in many cases leads to difficulty in analyzing cyber risk. Additionally, the selection of cybersecurity controls that will effectively and efficiently treat the cyber risk is commonly performed manually, or at best with limited automated decision support. In this paper, we extend our previous work in [1] to analyze attack paths between CPSs on one hand, and we improve the method proposed therein for selecting a set of security controls that minimizes both the residual risk and the cost of implementation. We use the DELTA demand-response management platform for the energy market stakeholders such as Aggregators and Retailers [2] as a use case to illustrate the workings of the proposed approaches. The results are sets of cybersecurity controls applied to those components of the overall system that have been identified to lie in those attack paths that have been identified as most critical among all the identified attack paths.
Anomaly Detection of actual IoT traffic flows through Deep Learning

The detection and classification of Internet traffic was studied in depth in the last twenty years, but this is still an open research issue as pertains the Internet of Things (IoT), mainly because real IoT traffic dataset are not very widespread. With this paper, we make public an integrated dataset, made of actual IoT network flows, built using six different network sources, which could represent a research reference for further investigations. Furthermore, we exploited it to optimize the hyper-parameters of a deep neural network and evaluate its performance for both distinguishing normal and abnormal traffic and discriminating different types of attacks, achieving very good results.