Reviewerly/exHarmony huggingface dataset:
Superconducting quantum computer: a hint for building architectures

We discuss the scalability of superconducting quantum computers, especially in a wiring problem. The number of wiring inside a cryostat is almost proportional to the number of qubits in the current wiring architecture. We introduce "The three-Y’s": regularity, modularity, and hierarchy to an architecture design of superconducting quantum computers. The key to the wiring elimination is found in the quantum error correction codes having thresholds and spatial translational symmetry, i.e., the surface code. We show a superconducting-digital-logic-based architecture and introduce a stacked heterogeneous structure of the quantum module.
Masked Face Identification by Ensuring Safe Social Distancing, Temperature Screening and Crime Detection

COVID -19 pandemic is the defining global health crisis of our time which eventually led to the use of face mask and maintaining safe social distancing, which became mandatory for reducing the rate of transmission of virus. This has parallelly raised a challenge in identifying people since most of the face regions are hidden inside the mask. So we came up with a system which identifies maked face along with which it ensures whether people follows safe social distancing or not. For this purpose we are using deep convolutional neural network (CNN) along with this MLP is also used for classification process. We also incooperates an efficient system that makes real time automated monitoring of people to detect safe social distancing and use of thermal cameras for detecting the body temperature. Thus the entire system favours the society by saving time and the automated inspection reduces the manpower to inspect the public.
Person Re-Identification using Ensemble of Networks on Pose Transferred Images

Person Re-identification is one of the most important and fundamental tasks in automated video surveillance. It is the task of recognizing a specific person inside an image/video given images/videos taken from different points of view or at different times. Many recent deep learning methods try to learn visual features and metrics proper for the task of detection and identification. One of these features is person's pose which is a function of the person's characteristics, actions and environmental conditions. In this paper, using a GAN based on bipartite graphs, it is tried to transfer images to specific canonical poses in order to ignore undesired side effects of the poses. Finally, an ensemble of feature extraction networks, with different canonical poses, is used for re-identification. The proposed method could outperform state-of-the-art related methods on standard benchmarks. As a result, 89.79% mAP is achieved on Market-1501 dataset and 81.1% mAP is achieved on DukeMTMC dataset.
Construction of Agent Model Based on Emotional Interaction

Emotion, as a crucial special ability of human beings, plays an important role in the cognitive process. With the development of biology and psychology, more and more attention has been paid to computer science by adding emotion into Agent. At present, most researches on the formation of human tissue groups are based on rational behaviour, which is not very good to consider the influence of affective factors on individual. Incorporating affective factors into cognitive behavioural modelling can significantly improve the authenticity and adaptability of cognitive behavioural models. Based on the related knowledge of computer science and psychology, this paper proposes a technical model of emotional Agent based on emotion and behaviour. From the perspective of interaction between emotion and behaviour, this paper constructs a basic framework of emotional Agent model and further analyses the application of emotional Agent.
On the consistency of Bayes estimates for the infinite continuous mixture of Dirichlet distributions

In this paper, we introduced the infinite continuous mixture of Dirichlet distributions as a generalization of the infinite mixture of Dirichlet ones, in order to avoid the limitation of choosing the a priori sample size for the expectation \textit{a posteriori} estimator. Monte-Carlo sampling was used in order to obtain the \textit{posterior} distributions mixture, since this mixture is difficult to get analytically. A new parametrization of this proposed distribution was achieved. Then, we suggested a mixture expectation \textit{a posteriori} estimator of the unknown parameters. The proposed estimator solves the problem of how to construct a Bayesian estimation of proportions without specifying particular parameters and sample size of the prior knowledge. Some asymptotic properties of this estimator were derived, specifically, its bias and variance. The consistency and asymptotic normality of the estimator were also established when the sample size tends to infinity and its credible interval was determined. The performance of the proposed estimator was illustrated theoretically and by means of a simulation study. Ultimately, a comparative simulation study between the learned estimates, the proposed mixture expectation \textit{a posteriori}, standard Bayesian estimator, maximum likelihood and Jeffreys estimator, was established. According to this simulation, we were able to conclude that the prior infinite mixture of Dirichlet distributions offers higher accuracy and flexibility for modeling and learning data.
Handwriting Based Personality Analysis: An Image Processing Based Tool

Your handwriting reveals much more than you might imagine. The science of analyzing handwriting for personality traits is called graphology. A lot of people want to know what your handwriting says about your personality. The key attraction about handwriting analysis is that it reveals many things about your personality. We propose a smart way of handwriting analysis using image processing approach. Our system uses methods viz. line segmentation, word segmentation and character extraction for behavioural analysis. Such system will be very beneficial for analysis and a direction to make our personality better.
Three-Dimensional Dynamic Path Planning for Drone Delivery System using Enhanced Interfered Fluid Dynamical System Analogy

The availability of a swift, reliable and efficient algorithm to design the path of UAVs is very critical in overall mission planning of any task. This study utilises the concepts of fluid analogy to design an algorithm for path planning of UAVs to move around the static and dynamic obstacles in a 3D environment. The algorithm has been structured around the improved and modified version of Interfered Fluid Dynamical System (IFDS). Firstly the concept has been established and validated for static obstacles, and subsequently multiple obstacles in motion have been introduced. Simulation results demonstrate the effectiveness of the model.
Sprague–Grundy theory in bounded arithmetic

We will give a two-sort system which axiomatizes winning strategies for the combinatorial game Node Kayles. It is shown that our system captures alternating polynomial time reasonings in the sense that the provably total functions of the theory corresponds to those computable in APTIME. We will also show that our system is equivalently axiomatized by Sprague–Grundy theorem which states that any Node Kayles position is provably equivalent to some NIM heap.
Security Concerns at Various Network Phases Through Blockchain Technology

Among various recent techniques, IoT is considered as the most popular technique which connects various heterogeneous devices such as vehicles, smart phones, and tickets in order to automate their tasks according to the environment. In most of the IoT applications, the data management and entities control is done by a centralized authority where the security is considered to be a major issue. In order to secure the data from various centralized threats such as man-in-middle attack, single failure etc., it is a needed to propose a decentralized security scheme. Recently, Blockchain is considered as most secured decentralized network that ensures security and transparency while transmitting the data among entities. In this paper, we have discussed the need of blockchain security at different phases of network like data, users, and device by analyzing their different security metrics and key challenges.
Construction strategy and performance simulation of quantum satellite wide area network based on cobweb structure

Quantum satellite communication is a research hotspot and frontier in the field of communication. It has the advantages of ideal information security and wide coverage, which is of great significance in constructing a global quantum satellite wide area network. However, problems such as network reliability, security and routing relay still need to be improved when transmitting information over long distances. In this paper, the spider web is used as a unique natural communication topology to transform the natural spider web into an artificial spider web topology. The quantum information transmission adopts N-order quantum teleportation routing scheme, and the transmission delay is basically unchanged. On this basis, the spider web topology quantum wide area network transmission model is constructed. The bit error rate, throughput rate and security key generation rate of the network model are simulated and analyzed. Taking 9-node ring network and 9-node cobweb for example, the quantitative analysis and qualitative analysis are both conducted in this paper. The results show that the cobweb topology has higher reliability. When the average power spectral density of the noise is given and there is no relay, the bit error rate increases with the transmission distance increasing, so the introduction of relay should be considered. When the transmission distance and noise power spectral density are constant, the bit error rate decreases with the number of relay nodes increasing, so the appropriate routing process should be selected in the spider web topology. With the increase of the probability of transmitting entangled photon pairs, the throughput rate gradually increases. With the increase of transmission delay in the network, the throughput rate Q gradually decreases. However, the transmission delay is basically unchanged in this routing scheme, and the transmission delay of cobweb structure is very small. Therefore, the throughput rate of the topology quantum WAN of cobweb network based on N-order quantum teleportation proposed in this paper will not significantly decrease. When the transmission distance of quantum information increases, the network key generation rate decreases gradually. With the increase of the number of network relay nodes, the key generation rate increases gradually. Thus, it can be seen that using cobweb topology and N-order quantum teleportation routing scheme to construct a quantum satellite WAN has good advantages.
Agile Architecture Frameworks: Challenges and Open Issues

Agile architecture frameworks are flexible, adaptable to change and allows for a faster delivery of projects; these make them very important in delivery of projects. Agile Architecture frameworks are therefore of great advantage when implemented in Information and Communication Technology (ICT) environments as technology is ever changing and evolving. Although agile architecture frameworks have been explored, there is still a need for more research to be done on their challenges. This paper aims to explore the challenges and open issues for the agile architecture frameworks, various frameworks such as Agile Architecture Framework (AAF) and distributed agile or adaptive development (DAD). Agile architecture frameworks have a lot of challenges varying from communication, cost, and agile architecture processes being neglected. Even though agile architecture frameworks have a lot of challenges, there are various ways I which these can be overcome .
Experimental Analysis of Cross-Layer Sensing for Protocol-Agnostic Packet Boundary Recognition

Radio-frequency (RF) sensing is a key technology for designing intelligent and secure wireless networks with high spectral efficiency and environment-aware adaptation capabilities. However, existing sensing techniques can extract only limited information from RF signals or assume that the RF signals are generated by certain known protocols. As a result, their applications are limited if proprietary protocols or encryption methods are adopted, or in environments subject to errors such as unintended interference. To address this challenge, we study protocol-agnostic cross-layer sensing to extract high-layer protocol information from raw RF samples without any a priori knowledge of the protocols. First, we present a framework for protocol-agnostic sensing for over-the-air (OTA) RF signals, by taking packet boundary recognition (PBR) as an example. The framework consists of three major components: OTA Signal Generator, Agnostic RF Sink, and Ground Truth Generator. Then, we develop a software-defined testbed using USRP SDRs, with eleven benchmark statistical algorithms implemented in the Agnostic RF Sink, including Kullback-Leibler divergence and cross-power spectral density, among others. Finally, we test the effectiveness of these statistical algorithms in PBR on OTA RF samples, considering a wide variety of transmission parameters, including modulation type, transmission distance, and packet length. It is found that none of these benchmark statistical algorithms can achieve consistently high PBR rate, and new algorithms are required particularly in next-generation low-latency wireless systems.
Class-Incremental Learning for Semantic Segmentation - A study

One of the main challenges of applying deep learning for robotics is the difficulty of efficiently adapting to new tasks while still maintaining the same performance on previous tasks. The problem of incrementally learning new tasks commonly struggles with catastrophic forgetting in which the previous knowledge is lost.Class-incremental learning for semantic segmentation, addresses this problem in which we want to learn new semantic classes without having access to labeled data for previously learned classes. This is a problem in industry, where few pre-trained models and open datasets matches exactly the requisites. In these cases it is both expensive and labour intensive to collect an entirely new fully-labeled dataset. Instead, collecting a smaller dataset and only labeling the new classes is much more efficient in terms of data collection.In this paper we present the class-incremental learning problem for semantic segmentation, we discuss related work in terms of the more thoroughly studied classification task and experimentally validate the current state-of-the-art for semantic segmentation. This lays the foundation as we discuss some of the problems that still needs to be investigated and improved upon in order to reach a new state-of-the-art for class-incremental semantic segmentation.
An Improved Denoising Method for Optical Fiber Signal

Abstract In the large-capacity FBG sensor network demodulation system, the process of collecting the light source is often accompanied by noise interference. Signal denoising is an important factor that affects the accurate demodulation of the FBG sensor system. Aiming at the problem of constant deviation of soft and hard threshold functions in denoising of traditional wavelet threshold algorithm and the shortcomings of modal aliasing easily in empirical mode decomposition, a modified semi-soft threshold-VMD denoising algorithm is proposed. The simulation results show that the function in this paper is better than the traditional threshold function in continuity and signal reduction degree, and has achieved good results.
Relative Position Control in 3D Space of Cooperating Multirotor UAVs Using Modular Relative Jacobian

This paper implements a new approach of controlling cooperative unmanned aerial vehicles (UAVs) using relative Jacobian. A new Jacobian-based formulation in controlling UAVs is shown, such that they become components of the modular relative Jacobian that is used to coordinate the motion between two UAVs. This new approach affords a holistic control on the two UAVs that is based on the relative motion of one UAV with respect to the other. In this way the two UAVs are now controlled as a single manipulator with an end-effector and posture control. Thus, any control strategy of a single manipulator can now be applied to the control of the two UAVs. Simulations in Gazebo RotorS shows the relative position control between the two UAVs as well as the posture control of the reference UAV.
How Will Illinois' Push for Mail-In Balloting Affect Voter Confidence in the November 2020 Election

The COVID-19 pandemic has resulted in most states making numerous changes to electoral procedures, in an effort to assist those voters wishing to avoid crowded polling places, and the health risks they pose. Illinois, like most others, will permit in-person voting, both on Election Day and in advance, but will also encourage voters to take advantage of mail-in voting. Survey data from recent years reveal that many voters, not without cause, worry slightly more about absentee or mail ballots not being counted, and may believe that fraud is more likely when voting is remote. To boost confidence in the integrity of the 2020 election, accordingly, we recommend that Illinois officials publicize details of the new rules, caution about some likely consequences, including delayed final results, and strive to ensure uniform application of rules. Generally, any important change in voting procedures should be accompanied by some public-service education campaign. With many changes being made, on a temporary, emergency basis, election officials and the voting public alike will face unusual challenges, and could require more help than usual from the state.
A Cognitive Approach to Deep Neural Cryptosystem in Various Applications

Deep Learning-based cryptosystem is an emerging field of cryptography with less computational overhead and improved accuracy. ‘Deep Learning in Cryptography’ is an attempt to study various Deep Learning applications in the field of cryptography. Transforming plaintext into ciphertext requires complex mathematical calculations and an algorithmic approach for making the system more difficult and secure. This additional overhead can be minimized using the concept of data science such as Artificial Intelligence, Machine Learning and Deep Learning. The complex network structure in neural networks makes the system more secure. Deep Learning-based model predictions can be applicable for malware classification, intrusion detection, and multichannel attacks. The efficient feature extraction process can produce an accurate result from the given set of input data. The featured input structure passes through various layered architecture and produces better classification output with good accuracy.
Transformation of Teaching Pedagogical Practices in Higher Education in the Situation of Covid-19 (Based on the example of transforming academic practices in the Pedagogical Faculty of the Thracian University)

When COVID-19 pandemic hit Bulgarian education was in the middle of its reform. Health requirements, the long lockdown, have expelled a huge surge of the need not only for a survival but also for the preservation and transformation of education. Education on all levels including higher academic education took quick steps to reorient to online learning. In a short time, university electronic platforms became the daily place for learning. This online reorientation has led to a number of changes in teaching models, online learning management and more. Practically all methodologies and methodologies that the pedagogical students get acquainted with have been rewritten. It is in this direction that the article traces the challenges facing higher education and examines an empirical study of the attitudes of student educators trained in an online environment.
Investigating the Technical and Social Affordances of Moodle Usage among Senior High Schools Students in the Greater Accra Region

The justification for conducting this study was to investigate the technical and social affordances of Moodle usage among Senior High School students in the Greater Accra region of Ghana.The positivist research paradigm and quasi-experimental research design was adopted for the study.The purposive sampling procedure was used to sample 301 respondents for the study.The Cronbach alpha value of 0.83 was obtained on the structured questionnaire.The findings of the study revealed that students were able to submit project works on time, viewed their grades and received prompt feedback.Also, students were able to connect with teachers and other students, shared ideas or communicate within the Moodle platform effectively.It is recommended that the government, National Council for Curriculum and Assessment (NaCCA), educational policymakers provide workshops on Moodle usage for students to acquire the needed technical skills in operating the Moodle platform.Moodle manuals should be designed for students to serve as a guide to aid students to navigate smoothly when using the Moodle platform.
Natural language guided object retrieval in images

Abstract The ability to understand the surrounding environment and being able to communicate with interacting humans are important functionalities for many automated systems where visual input (e.g., images, video) and natural language input (speech or text) have to be related to each other. Possible applications are automatic image caption generation, interactive surveillance systems, or human robot interaction. In this paper, we propose algorithms for automatic responses to natural language queries about an image. Our approach uses a predefined neural net for detection of bounding boxes and objects in images, spatial relations between bounding boxes are modeled with a neural net, the queries are analyzed with a syntactic parser, and algorithms to map natural language to properties in the images are introduced. The algorithms make use of semantic similarity and antonyms. We evaluate the performance of our approach with test users assessing the quality of our system’s generated answers.
Position Estimation of Occluded Fingertip Based on Image of Dorsal Hand from RGB Camera

Development of Virtual Reality (VR) and popularization of motion capture enables a user’s hands and fingers to directly interact with virtual objects in a VR environment without holding any equipment . However, an optical motion capture device cannot track the user’s fingers where the fingers are occluded from the device by the hand. Self-occlusion hinders correct estimation of the fingertips, resulting in failure to achieve natural interaction. This paper proposes a method for estimating 3D position of occluded fingertips using an image of the dorsal hand which a head-mounted camera can capture. The method employs deep neural networks to estimate 3D coordinates of the fingertips based on an image of the dorsal hand. This paper describes a processing pipeline used to build training datasets for the deep neural networks. Hand segmentation is used to preprocess the image, and coordinate alignment is applied to transform coordinates in two camera coordinate systems. The collected dataset includes data with different hand types of subjects, different interaction methods, and different lighting conditions. Then, this paper evaluates the effectiveness of the proposed method based on the collected dataset. The model based on ResNet-18 had a smallest average 3D error of 7.19 mm among three models used to compare. The same model structure is used to build a model to estimate 3D coordinates from an image of a palm as a baseline. An evaluation on the models shows that the difference between errors of the models for palm and dorsal hands is smaller than 1.5 mm. The results show that can effectively predict a fingertip position with a low error from an image of dorsal hand even if the fingertip is occluded from a camera.