Reviewerly/exHarmony huggingface dataset:
Design of Dynamic Morphological Analyser for Hindi Nouns Using Rule Based Approach

Background: A grammar checker can be used as a proof reading tool, which depends upon the basic definition of the words. If words are not defined correctly or are not being tagged with correct grammatical meaning, the results will not be accurate. Objective: To attain this accuracy, Morphological analyser plays a crucial role. In Hindi, as the whole structure and meaning of a sentence depends upon a Noun, so it is mandatory to tag a Noun word properly. But, to tag a Noun with its correct grammatical meaning is a challenging chore. Methods: To tag a word, the word is being input in the tool, which is firstly searched inside the dictionary. If the word is not found in the dictionary, then the grammar rules are applied to analyse the word. As noun, contains names also, so some times the rules are not possible to apply on the words. In that scenario, words are manually tagged and then added to dictionary for further use. Grammar tag set of 650 tags is used to generate more accurate results. All the words are stored in a database. The performance is measured by using Precision and Recall. Furthermore, this technique can be extended to define other categories of grammar like verb, adjective, adverb, etc. Results: This paper represents a method for a Rule-Based Morphological Analyser for Hindi Nouns only. It utilizes-a dictionary and a rule-based approach for defining words with their grammatical meanings using the morphological analyser. The designed morph which has been discussed in this work stores all the words in a database. As this morph analyser uses a set of 650 plus grammatical tag sets (for complete Hindi morphological analyser), the user will always get more accurate results. The Authors have preferred both time and accuracy over memory space, which is not a big issue these days. Therefore this approach can be used for both types of morphological approach. Conclusion: Furthermore, this method can be extended to the other categories of the Hindi Grammar like Adverbs, Adjectives, and Verbs, etc. The results are very promising and are expected to provide even more advancement to the existing strategies and methodologies.
Attacks, Vulnerabilities, and Blockchain-Based Countermeasures in Internet of Things (IoT) Systems

The ingenious technology of blockchain has made the Internet of Things (IoT) extremely popular in recent times. Globally, blockchain has evolved with innovative ways to disrupt and transform traditional business activities. Blockchain technology has helped developers in various fields to come up innovatively with better problem-solving techniques. Health care systems, industries, smart cities, smart homes, etc. have been enormously transformed with the help of blockchain technology. Blockchain has provided improved transparency, enhanced security, better traceability, great efficiency, and respectively little expense. In this chapter, we highlight the attacks, vulnerabilities, and blockchain-based countermeasures in IoT systems. The chapter comprehensively surveys blockchain technology, by exploring its architecture, classification, and security threats. Further, the chapter reviews the most commonly launched attacks within IoT systems and outlines the areas of application for blockchain technology and how it contributes to the growth and automation of industries. Finally, the chapter highlights the major challenges faced in using blockchain technology for IoT applications and in this regard, discusses some future directions for research.
Closer to Pre-trained Network Transfer Better

In recent years, Deep Neural Network (DNN) has been widely used in the domain of computer vision, but its further development is restricted because of the lack of train samples. Fine-tuning is one of deep transfer learning methods to solve this problem. Nowadays, some existing Fine-tuning approaches assume that if the fine-tuned network could be as close to the pre-trained network as possible, the source knowledge would be fully utilized to improve the study effect. Although these approaches work pretty well, the assumptions on which they are based have not been systematically proved. Due to the complexity of DNNs, offering a reasonable and accurate explanation of the performance of the network is significant for designing new algorithms. In this paper, we design a series of experiments with or without transfer learning respectively, to characterize the relationship between network differences and network performance. The results show that there is an obvious negative correlation between them under certain circumstances. This conclusion can not only provide a more reasonable explanation for the relevant transfer learning algorithm, but also provide support and inspiration for the future work.
A Data Reconciliation Model Based on Turbo for Satellite-Ground Quantum Key Distribution Network

Abstract In the satellite-ground quantum key distribution network with power IoT, due to the imperfection of the quantum equipment and the noise in the channel, the raw key will have some bit error rate and cannot be used for secure communications. To solve this problem, we propose an improved data reconciliation model based on Turbo for satellite-ground quantum key distribution to extract the raw key, and then the key on both sides of communication are identical. We carry on the optimization and simulation to Turbo and increases its error correction efficiency by 15%. To test and verify the feasibility of the model, we implement satellite-ground quantum key distribution simulation experiments and respectively use the low-density parity check (LDPC), Cascade, and Turbo for data reconciliation. Experimental results show that Turbo can make the final key generation increased by 8% comparing with LDPC and Cascade.
Fundamentals of Quantum Computing, Quantum Supremacy, and Quantum Machine Learning

When we aim to demonstrate that a programmable quantum device can solve complex problems which cannot be addressed by classic computers, this fundamental goal is known as quantum supremacy. This concept has changed every fundamental rule of computation. In this chapter, the detailed concept of quantum computing and quantum supremacy is explained along with various open source tools and real-time applications of this technology. The major base concepts, quantum computing, the difference between classical and quantum computer on physical level, programing quantum device, and the experiment-quantum supremacy are explained conceptually. This chapter also includes an introduction of the tools Cirq and OpenFermion plus the applications like quantum simulation, error mitigation technique, quantum machine learning, and quantum optimization, which are explained with illustrations.
Implementation of Blockchain Based Distributed Architecture for Enhancing Security and Privacy in Peer-To-Peer Networks

Blockchain technology has gained maximum popularity in recent years as compared to the other latest technologies. It works as immutable ledger in which the transactions are done in decentralized way. It has started spreading its wings now as it is not only used in financial applications but many other companies in the industry are trying to leverage the benefits offered by blockchain in their projects. As industries are committing towards blockchain based projects, there is a need of blockchain implementation and associated development. This paper introduces the basic requirements of blockchain development and proposes a generic blockchain that can be considered as a mould for implementation in different use cases. In this paper a distributed architecture is implemented using blockchain with improved security and privacy. The expected contribution of this paper is to provide an overview for researchers and developers to build applications utilizing the blockchain as a basis.
Wireless Eye-Tracking Technology Application and Self-report Measures to Explore Users’ Approach to Smart Home Systems (SHS)

The relation between individual differences in some psychological traits and eye-gaze behavior in participants exploring a Smart Home System (SHS) was considered. Real-time eye-gaze metrics were recorded while subjects were engaged with 5 tech-interaction (TI) areas with different levels of complexity in the SHS. Results suggested easiness to visually process a simpler TI compared to complex ones, in terms of higher number of fixations and of slower Time To First Fixation (TTFF). In two of the TIs (Living Room and Bathroom) we found a negative correlation: between the external Locus of Control (LoC) and the number of fixations, which might indicate a possible tendency for individuals with low external LoC to explore more actively complex TIs. A negative correlation was found between Behavioural Activation System Reward Responsiveness (BAS-RR) and fixation duration, suggesting a tendency for subjects with high BAS-RR to scan the TIs with more quick and dynamic visual patterns.
Attentional single-shot network with multi-scale feature fusion for object detection in aerial images

In recent years, UAV (Unmanned Aerial Vehicles) are widely used in military reconnaissance and traffic control scenarios. Due to the rapid development of UAV technology, the object detection technology for aerial images has drawn the most attention in computer vision. However, detection in aerial images is a non-trivial task due to low target resolution, large scale variation, and occlusion and illumination variation. Our method is derived from the SSD (Single Shot Detector) which is well- balanced between detection speed and accuracy. However, in SSD network, not all feature map channels contain useful information for detection. Moreover, the SSD network use feature map of different scales for predicting objects with different sizes which is not reasonable. Aiming at overcoming these two shortcomings, we introduced the mechanism of channel-wise attention which can help choose channels that contain the most discriminative information. We also propose the multi-scale feature fusion mechanism in which high and low- level feature maps are fused which boost the dis-criminative power of the feature maps for prediction typically for small objects in aerial image. Experimental results shows that our method has remarkably improved the performance in object detection in aerial images.
An Iterative and Incremental Approach to Address Regulatory Compliance Concerns in Requirements Engineering

We propose to address regulatory compliance concerns for a software system in a module (regulatory module) in the requirements engineering phase. The inputs are the SRS document and an initial set of Regulatory Documents. The outputs of the regulatory module are the ‘SRS regulatory version’ and ‘mapping document’. SRS regulatory version will have regulatory requirements expressed as a set of logic and/or plain English text weaved into appropriate parts of the SRS. Mapping Document is used to maintain the SRS regulatory version whenever Regulatory requirements or SRS changes. Regulatory expert’s involvement is restricted only to the activities of the regulatory module in SDLC. The regulatory module contains components in it and generates outputs through a process workflow. The various components in the regulatory module are: Identifying Regulatory Documents, Extracting Regulatory Requirements, Resolving Conflicts, Requirement Specification, Mapping Document. We identify generic interface specifications for these components through an extensive literature survey. These generic interfaces are valid for any SRS and any Regulatory body. We start with the generic version of the components as the initial version of the components. The objective is to get a specific version applicable for a specific regulatory body and a specific SRS along with SRS regulatory version and mapping document. An incremental and iterative solution strategy is outlined to achieve this. We have worked on a proof of concept of the solution strategy for a use case of an SRS as input at the regulatory module level. In literature, the study is done at component levels and not at the regulatory module level.
Fluid-rock interaction of the early Cambrian black shale in the South China Block: Implications for low-temperature mineralisation

Chalcophile elements hosted by black shales of the Lower Cambrian Niutitang Formation, South China Block, contribute to low-temperature mineralisation in southwest China. To study the mobilisation and migration of ore-forming elements, hydrothermal experiments were performed to simulate leaching of Au, As, Sb and Ag from an Au-rich black shale from the Kaiyang Phosphate Mine (KYPM), China. The experiments were designed to simulate low-temperature mineralisation at 150 °C, with solvent concentrations of ~5%, durations of 15 d and 30 d, and water/rock mass ratios of 13:1 and 26:1. Reagent-grade NaCl, NaHCO3 and Na2S were used to provide the common anions found in hydrothermal systems. The results show that solutions containing Cl− ions in oxidised acidic systems leached Au most effectively. Weakly alkaline systems containing HCO3− ions leached Au less effectively. Solutions containing HS− ions did not leach Au effectively, and Au precipitated within the HS−-bearing system. The results for Ag were similar to those for Au, and Ag-leaching was most effective in the oxidised acidic system containing Cl− ions. As-leaching does not occur in the Cl−-bearing system, but As is leached effectively by HS−-bearing and HCO3−-bearing solutions. Sb was not leached by our experiments. Oxidised fluids produced by deep-sourced magma during the Indosinian and Yanshanian tectonic events infiltrated black shales affected by extensional deformation through faults, joints and fractures. Fluid–rock interaction leached a range of elements in these structural settings. These experiments provide insights into migration of chalcophile elements, particularly Au and Ag, and the source of large-scale low-temperature mineralisation within the South China Block.
An Education System for Virtuous Cycle in Extracurricular Activities

This paper reports the educative effect of the student support projects in Salesian Polytechnic Dept. of Mechanical and Electronic Engineering (M.E.). Recently, the drop in student’ s basic scholastic attainments becomes a problem. We started the student support project as an original approach of M.E. department. The student support project is composed of the hands-on education course and the qualification acquisition course. The hands-on education course studies the basic circuit that can be applied to the robot and the electric vehicle. The qualification acquisition course executes lectures by the invited professional engineers. Students who have acquired basic knowledge will challenge many contests. Recent activities have expanded to overseas collaboration and industry-academia-government collaboration. In this paper, outline of the educative effects of this student support project is clarified.
Towards the Law Enforcement of Firearms

The rate of violence in the world has increased tremendously in the last 15 years. Using and even carrying firearms in public places is not legal in most countries, but still Legal and illegal guns play a significant role in violence incidents across the globe on the streets. In the paper, we propose a computer vision-based approach to detect the people carrying firearms in public places using the CCTV camera system. Our approach also classifies the potential threat that can be caused by firearms with adequate quantitative and qualitative analysis. Along with the paper, we also release the high-quality corpora of guns that can be used for various detection and classification tasks by the researcher community for further experimentation. Apart from usage in cities and towns, the army can employ these systems to keep a check on the cross-border connecting roads and high tension areas.
Bernoulli Track-before-detect Algorithm for Distributed Target with Unknown Amplitude Information

Track-before-detect algorithm is an effective solution of detecting and tracking a low signal-to-noise ratio (SNR) target whose amplitude distribution characteristic is needed as the prior information. Under the point target hypothesis in most previous works, the amplitude of target is usually assumed to be known or modeled as a state variable. However, these approaches cannot simply be migrated to the extended target tracking problem because of the unknown target extended length. This paper considers the issue of extended target joint detection and tracking with unknown amplitude distribution information through the use of Bernoulli particle filter based track-before-detect (BPF-TBD) methods. The proposed heuristic algorithm accumulates the multi-frame measurement data along the potential track of the target by a sliding window and then extracts the amplitude distribution information by means of principal component analysis (PCA) method. Simulation results show that the property of the proposed method asymptotically converges to the exact filter with prior correct expected amplitude distribution information, which indicates a superior performance in terms of feasibility and effectiveness.
ACRC: Accurate Credit Rate Control

In data centers, data packet loss will cause high retransmission delays, which is very harmful to some real-time network loads. For this reason, many production data centers have deployed lossless networks. ExpressPass as an advanced forward-looking congestion control protocol, it can achieve losslessness through the credit appointment mechanism, while making full use of network resources. However, due to the instability of the data stream of the transport layer, its rate adjustment mechanism based on credit serial numbers cannot guarantee the real-time nature of credit serial numbers. At the same time, a large amount of credit package storage will occupy the buffer memory, resulting in a decrease in server performance. This paper presents an approach called accurate credit rate control (ACRC), which proposes a new rate adjustment algorithm based on ECN marking based on ExpressPass, which solves the real-time problem of ExpressPass's serial number-based rate adjustment algorithm and save the data bit space consumed by symbolizing the package. It solves the real-time and ends congestion problems of ExpressPass very well.
Best play in Dots and Boxes endgames

We give very simple algorithms for best play in the simplest kind of Dots and Boxes endgames: those that consist entirely of loops and long chains. In every such endgame we compute the margin of victory, assuming both players maximize the numbers of boxes they capture, and specify a move that leads to that result. We improve on results by Buzzard and Ciere on the same problem: our algorithms examine only the current position and do not need to consider the game tree at all.
Analysis on the unit space design of primary and secondary school campus building teaching area

This paper takes the teaching area unit space of primary and secondary schools as the main research object, analyzes, discusses and studies the reasonable use size of teaching unit space, commonly used space combination mode and internal detail design of education space, analyzes the actual use of various space units, and discusses the advantages and disadvantages of different design methods. Finally, from the perspective of teachers and students, combined with the analysis of specific project cases, this paper puts forward the optimization design strategy of school teaching unit space in line with the use requirements and architectural aesthetics.
Visual selection of standard wells for large scale logging data via discrete choice model

It is a time-consuming and laborious task to conduct accurate geological interpretation based on large-scale logging data. A subset of drilling wells are always selected undergoing detailed stratigraphic correlation, aiming to enhance the efficiency of geological interpretation and retain the matching accuracy of stratigraphic structures. However, standard wells are selected by hand in the course of traditional standard well selection, which will easily bring many uncertainties for subsequent interpretation of geological structures. In this paper, we propose a visual analytics system to support supervised standard well selection via a discrete choice model. Firstly, an adaptive blue noise sampling model is applied to determine spatial distribution of standard wells, and a stratigraphic correlation model is designed to measure the similarity between drilling wells from different attribute perspectives. Then, we utilize a discrete choice model to select standard wells with the spatial distribution and multiple attributes taken into consideration. Furthermore, several meaningful visualizations are designed allowing users to get deeper insights into the selection of standard wells, and a rich set of interactions are also provided enabling users to further optimize the discrete choice model. At last, a visualization framework is implemented to integrate the models and visual designs, by means of which the geological interpreters are able to visually select, evaluate and optimize the standard wells. The effectiveness of our work and its application values for geological interpretation are further demonstrated through case studies based on real-world datasets and interviews with domain experts.
A Deep Reinforcement Learning Based Feature Selector

In the field of data mining and machine learning, it is a challenge for researchers and engineers to analyze and classify the high-dimensional data. In order to minimize the classification error, it is critical to identify and select the most characterizing features as well as remove the irrelevant features from the high-dimensional data. Feature selection algorithms provide effective processes to find a compact valuable feature subset from the set of all the candidate features. In past researches, most feature selection methods treat such a problem as a scoring problem, where each feature is evaluated individually and top-ranked features are selected. In this study, adding new features into the feature subset is considered as a Markov Decision Process, and the Feature Selection task is formalized as a reinforcement learning problem. This paper proposes a novel Deep Reinforcement Learning based Feature Selector (DRLFS) along with a dynamic randomness policy and two search protocols to tackle the exploration versus exploitation dilemma for a gradual approach to the optimum subset. The experimental results on various benchmark datasets prove the promising feature selection ability of the proposal of this paper.
A Preview of Augmented Reality as Pioneering Method in Education

In Today's fast paced world, constant changes occur in everything around us especially technologies and this triggers the education sector to embrace the advancements. One such major distinctive approach is 3D Augmented Reality (AR). AR strengthens & supplements what can be seen, heard, smelled and sensed and allows the users to interact with the real & virtual worlds simultaneously. This paper reviews on how AR has been used in various genres in the field of education. Also, the benefits, the barriers faced and tests results over how effective is AR in acquiring knowledge is discussed. Amongst the varied published papers, the documents picked in this survey are from prominent journals, special issue articles and conferences that have implied `Marker based augmented reality'. The paper will give an overview about the prevalent methodology used in developing the AR based applications. Adding to the aforesaid data, the paper also gives information on, how in pandemic situation, AR has brought a new dimension to online classes and has become the head-turner.