Reviewerly/exHarmony huggingface dataset:
Review of Indian Dynamic Sign Language Recognition System

For many years, researchers have been working on developing a sign language recognition system. There have been very little advancements done in Indian sign language recognition systems. As a result, very few systematic literature reviews in this field. We attempted to address this issue by reviewing the most recent (post-2010) literature on dynamic Indian sign language technique on five different parameters. In this paper, we conclude that very few studies on dynamic Indian sign language recognition have been conducted. Hopefully, this literature will provide readers and researchers with a road map for future research as well as aid in knowledge integration and advancements in the field of dynamic ISL recognition.
POWER OF DATA MINING IN SOLVING CRIMES

Data mining is the field containing procedures for finding designs or patterns in a huge dataset.By using a data mining system, we can predict areas that have huge probability.Data Mining is the course of action which includes examining large databases in order to bring about new information which may be vital to the organization.The extraction of new information is predicted using existing datasets.As crime rates keep increasing everyday , new challenges are faced by police and law enforcement officers.Prediction of increases or decreases in crime is necessary for law enforcement agencies and therefore data mining system is important to describe kinds of criminal activities in specific area in a certain month or a certain year or the general number of crimes that occur in a country in a particular year or it can also be applied for fight terrorism for nation security.The aim of this study is to access possible implementation of data mining in actual criminal investigation.We use data mining to find previously unknown and useful information from data that is unstructured.The approach of this paper is between IT and criminal justice to produce data mining procedure to solve crime faster.
Natural Language-Based Naive Bayes Classifier Model for Sentence Classification

The classification of text is one of the basic tasks of natural language processing with wide-ranging applications. This is essentially a process of assigning markers or categories to the text based on its content. The paper aims to use an improved Naive Bayes classifier to identify the fact-worthy sentence. In this paper, authors have implemented an improved Naive Bayes classifier through which we classify the sentences. This proposed method has been tested with the claim buster dataset contains 23,533 sentences where each sentence belongs to either of these three classes, i.e., non-factual statement, unimportant factual statement, and check-worthy factual statement.
Exploiting Feature Diversity for Make-up Temporal Video Grounding

This technical report presents the 3rd winning solution for MTVG, a new task introduced in the 4-th Person in Context (PIC) Challenge at ACM MM 2022. MTVG aims at localizing the temporal boundary of the step in an untrimmed video based on a textual description. The biggest challenge of this task is the fine-grained video-text semantics of make-up steps. However, current methods mainly extract video features using action-based pre-trained models. As actions are more coarse-grained than make-up steps, action-based features are not suffi cient to provide fi ne-grained cues. To address this issue,we propose to achieve fi ne-grained representation via exploiting feature diversities. Specifi cally, we proposed a series of methods from feature extraction, network optimization, to model ensemble. As a result, we achieved 3rd place in the MTVG competition.
A Study on the Change and Development of Comparative Political Studies in Korea(1955-2020): Focused on Researchers

This article analyzes the change and development of comparative political studies in Korea from 1955 to 2020. The study analyzes 6,753 research articles published in 277 academic journals. Firstly, the number of articles was fairly low at 547 and the number of researchers was 327 during the period from 1955 to 1980.&#x0D; Then it increased to 1,965 and 1,084 during the period from 1981 to 2000. It has now increased immensely to 4,241 and 1,789 from 2001 to 2020. Secondly, when we divide the research period by decade, the most productive researcher published 6 articles during the 1950 60s and 8 articles during the 1970s. The numbers were 8 articles during the 1980s and 9 articles during the 1990s.&#x0D; Remarkably, this number has increased up to 37 articles during the 2000s and 28 articles during the 2010s. Thirdly, when we investigate whether a productive researcher publishes a numerous number of articles consecutively over two decades, 36.6% of productive researchers during the 1950 60s continuously published a similar number of articles in the 1970s The percentages were 25% in the 1980s, 12.73% in the 1990s, and 20.75% in the 2000s. This percentage has increased incredibly to 45% during the 2010s. In conclusion, comparative political studies in Korea expanded in the number of articles and researchers during the 1980s and has optimistically developed and matured into higher quantity research since 2000s.
Hematological value of captive Asian Elephants Elephas maximus around Chitwan National Park, Sauraha, Nepal

Veterinary hematology serves as an important screening procedure to assess general health conditions, diagnosis, and treatment of disease. This study aims to interpret and establish a set of hematology reference ranges for Asian Elephants managed by private and government facilities in Nepal. Blood samples from 50 elephants around Chitwan National Park, Sauraha were collected and hematological parameters such as total erythrocyte count and total leukocyte count were determined. The results show that the majority of hematological values were in line with the values previously published by different authors. The mean erythrocyte and leukocyte counts were reported as 3.32±0.93 × 10^6 cell/µL and 10448±335.49 cells/µL respectively. No sex-associated significant difference was observed in the case of total erythrocyte count, whereas total leukocyte counts varied significantly within sexes. Our findings revealed no significant difference in hematological parameters between government and privately owned elephants. The hematological values from our study can be used as reference values for assessing the health condition of elephants in Nepal. Further research work should be conducted to evaluate the factors affecting hematological parameters.
A System and Method for Intelligent Induced Maintenance of Space Application Facilities

The product characteristics and maintenance characteristics of space complex repairable facilities were analyzed to aim at the maintenance requirements with a large number of on-orbit replaceable units, complex systems, and high operation accuracy. An intelligent induced maintenance system for space application facilities was developed, and unique evaluation criteria were established. The system, key technologies, and application effects are verified through typical application cases and comparative experiments. The results show that the implementation of the system can effectively reduce the workload of the astronauts in orbit and improve the work efficiency and provide new cooperation means for the astronauts to solve the urgent and unexpected tasks in orbit.
A Novel Approach to Ensure the Security of Question Papers Using Visual Cryptography

In the recent COVID-19 pandemic situation, physical distributions of University question papers to the affiliated colleges are very difficult. Though online distributions of question papers are a good alternative, they may be vulnerable in respect of security concerns. Hackers may intervene in the process and question papers may be stolen or altered. In this paper, we used K-out-of-N visual cryptography techniques to secure the question papers in between the transmissions. Before creating the shares, we applied an extra password protection to the original question paper using AES encryption. At the college examination center, end multiple shares are received by the separate stakeholders, who were ultimately combined together to create the decrypted question paper only after having the password provided by the head of the Institution.
Research on waterfront landscape design method based on virtual reality technology

The development of digital technology has brought changes to architectural design, and the application of modern computer technology has an impact on architectural design. In recent years, with the rapid development of artificial intelligence and other science and technology, the application of virtual reality technology in architectural design has begun to emerge. Virtual reality technology brings strong participation, interaction and dynamics to architectural landscape design. Virtual reality technology enables architectural landscape design to obtain a realistic three-dimensional virtualized interactive experience, and improves the effectiveness and forward-looking of architectural landscape design through effective data collection and analysis such as simulation feedback information. Based on virtual reality technology, this paper studies the application of virtual reality technology platforms such as Iris VR and Mars for virtual reality visualization processing to restore real visual effects; it studies the waterfront landscape design method based on the interactive experience data and analysis of virtual reality technology.
Remote Authentication of Fingerprints Using Meaningful Visual Cryptography

Nowadays, fingerprint recognition plays a vital role in digital security. We can authenticate any person using a central remote server. Fingerprints have the unique characteristics to identify any person uniquely. In this paper, we propose a technique by which we can remotely transmit any fingerprint securely. Meaningful visual cryptography (VC) scheme is used to implement this method. In VCS, secret image (here fingerprint) is divided into multiple shares, and each of these shares is integrated with some natural images and then transmitted using different network paths. Our model follows the 2 out of 2 secret sharing schemes. The major drawback of any VCS is loss of contrast in the output reconstructed image. Meaningful shares make this more difficult to retrieve the secret image. Our model uses some filtering techniques to overcome the problem and obtain the original fingerprint.
WEAPON DETECTION USING ARTIFICIAL INTELLEGENCE AND DEEP LEARNING FOR SECURITY APPLICATIONS

Security is always a main concern in every domain, due to a rise in crime rate in a crowded event or suspicious lonely areas. In This project we implement automatic gun (or) weapon detection using a Convolution Neural Network (CNN) based SSD and Faster RCNN algorithms. Proposed implementation uses two types of datasets. One dataset, which had pre-labelled images and the other one is a set of images, which were labelled manually
Artificial Neural Networks for Modeling of GaN Devices

Artificial Neural Networks (ANNs) have asserted themselves as one of the most robust and multidimensional tools of the last decade [1]. Their usage is nearly ubiquitous and has improved the efficiency and overhead in achieving their intended goals than other methods. In the area of semiconductor device modeling, physics-based and empirical models have presented themselves as one of the most reliable methods used in academia and industry since their inception [2]. ANNs are quickly becoming a serious contender for being one of the viable options investigated by researchers [3].
Net activism and whistleblowing on YouTube: a text mining analysis

Social media is more and more dominant in everyday life for people around the world. YouTube content is a resource that may be useful, in social computational science, for understanding key questions about society. Using this resource, we performed web scraping to create a dataset of 644,575 video transcriptions concerning net activism and whistleblowing. We automatically performed linguistic feature extraction to capture a representation of each video using its title, description and transcription (downloaded metadata). The next step was to clean the dataset using automatic clustering with linguistic representation to identify unmatched videos and noisy keywords. Using these keywords to exclude videos, we finally obtained a dataset that was reduced by 95%, i.e., it contained 35,730 video transcriptions. Then, we again automatically clustered the videos using a lexical representation and split the dataset into subsets, leading to hundreds of clusters that we interpreted manually to identify a hierarchy of topics of interest concerning whistleblowing. We used the dataset to learn a lexical representation for a specific topic and to detect unknown whistleblowing videos for this topic; the accuracy of this detection is 57.4%. We also used the dataset to identify interesting context linguistic markers around the names of whistleblowers. From a given list of names, we automatically extracted all 5-g word sequences from the dataset and identified interesting markers in the left and right contexts for each name by manual interpretation. The results of our study are the following: a dataset (raw and cleaned collections) concerning whistleblowing, a hierarchy of topics about whistleblowing, the automatic prediction of whistleblowing and the semi-automatic semantic analysis of markers around whistleblower names. This text mining analysis can be exploited for digital sociology and e-democracy studies.
Application of chaotic sequences to expand the spectrum

The modern development of mobile communications applied in remote sensing techniques requires continuous improvement of methods for transmitting information and developing new types of signals in order to achieve better resistance to interference, energy and structural secrecy of radiation, increase the efficiency of radio systems. These features have broad spectrum signals, the main advantages of which are: ensuring confidential transmission of information, extracting the signal from below the noise level, ensuring the reliability of the received information, even with a relatively small signal-to-noise ratio (SNR). Traditionally, the expansion of the spectrum of signals is done by multiplying the information signal by a code sequence, which is a random sequence, most often of binary symbols, formed by a determined law, but having the properties of a random sequence. Different classes of code sequences are known and used, which meet a number of mandatory requirements. However, they all have one or another drawback. An important reason for this is the lack of a universal mathematical apparatus through which to form a random sequence with the necessary structural complexity and correlation properties to ensure confidentiality in the transmission of information. With the emergence of a new direction in the development of communication technologies, based on the theory of dynamic chaos, there are alternative opportunities to expand the spectrum of signals. These are discrete chaotic signals, which, like pseudo-random sequences, are characterized by a quasi-continuous spectrum with uniform spectral density, with a steeply decreasing autocorrelation function and orthogonality. In the present work a structural scheme of a communication system between sensors is proposed, in which the spectrum of the information signal is expanded by a chaotic sequence. The noise immunity of the system is assessed by determining the probability of bit error rate (BER). The dependence of BER on SNR is studied. The connection with remote sensing could be review in the field wireless network using nodes. The methodology is tested in a simulation and in in-situ experiments. Some of the nodes are coated by new material and put into extreme conditions. The obtained results are analyzed and discussed.
An improved method for object detection in raining and foggy conditions for self-driving cars

YOLO (You Only Look Once) is a high-precision real-time object detection algorithm widely adopted in the visual perception of self-driving cars. However, its performance will be dramatically blocked in raining and foggy conditions. In order to address the detection of vehicles and pedestrian targets of self-driving vehicles during their driving on the road, this paper puts forward an improved YOLO model based on Multiscale Retinex with Colour Restoration defogging algorithm. In the proposed approach, different YOLO models (including YOLOv2, YOLOv3, YOLOv4 models) are fabricated to obtain a eligible network for vehicle and pedestrian detection in foggy environments. The experimental results indicate that the aforementioned three YOLO models have better performance based on the hypothesized approach, validating that the approach is effective in raining and foggy conditions. In addition, the YOLOv4 network model trained by the MSRCR algorithm enhances the accuracy of detection. Therefore, it can be estimated that the proposed approach is eligible for the application of real-time object detection in self-driving cars.
Research on human fall detection technology based on improved SSD_MobileNet_v2

Undetected elderly fall will result in serious consequences, therefore, in the informatization of elderly care services, fall detection and intelligent warning for the elderly at home is increasingly significant, which has become a hot spot of the current research. A fall detection scheme for the elderly in embedded devices by using the video stream analysis method based on the target detection model and combined with the change of human posture to determine the fall state is proposed in this paper. The clustering algorithm is used to analyze the centralized value of the aspect ratio of the dataset according to the characteristics of the single-type target detection scene in the application and the aspect ratio of the anchor in the selected SSD algorithm is reset. The method of self-adaptive judgment of positive and negative samples in China improves the identification efficiency of the elderly by using the characteristics of the data set itself. Theoretical analysis and experimental results show that the accuracy of elderly identification is increased by 5.7%, and the accuracy of fall event judgment is increased by 6.3%.
Define Your Content Feeds

With Viva Connections, employees can see the content relevant to them based on the resources they follow. The content is delivered to the users from various sources or feeds including SharePoint, Yammer, and Stream. The feeds provide a tailored experience to users by bringing the most relevant content to them. It is important to get your content ready for the content feeds using SharePoint, Yammer, and Stream.
Federated Architecture for Personal Mobility Service in Autonomous Transportation System

Along with the trend towards an autonomous transportation system (ATS), the intelligence of personal mobility service (PMS) can be further lifted by sensing travelers’ statuses comprehensively, learning behavior patterns accurately, providing travel options appropriately, and giving service responses timely. Such a process relies on a seamless information flow, which shall address data silos caused by laws and regulations about privacy. This paper proposes a federated architecture for PMS, called FPMS, which adopts federated learning, to provide personalized multi-modal options by aggregating personal data in a privacy-preserving way, and utilizing idle resources of personal devices within the service cluster. In general, by analyzing the physical objects involved, functions required, and data processed, a reference architecture of FPMS is designed to guide its construction in ATS effectively and efficiently. Moreover, a performance evaluation between FPMS and conventional centralized PMS is also presented to reveal the advantages of FPMS in saving service costs.
Envisaged Framework and Key Considerations

As a new-generation technology for registration and exchange of value, blockchain technology is most likely to be applied first in securities registration and settlement. That is, the existing securities trading and clearing process will remain unchanged, with trading handled by the stock exchange and clearing by the central counterparty (CCP) at the front end. However, the back end will shift to a blockchain-based new system of securities registration and settlement. With the basic framework in place, the existing financial market infrastructure (FMI) pattern will not change significantly.
Blockchain-Based Over-the-Counter FMIs

Compared with the organized exchange market, the OTC market is characterized by decentralized registration, “peer-to-peer” transactions and distributed operation. Its advantages lie in the flexibility of the mechanism and diversity of products. Its disadvantages are as follows: Fragmented registration easily leads to inconsistencies in registration and impedes the free transfer of product ownership; the business involves many parties, resulting in low efficiency and a high cost of information exchange; and the market is not transparent or fully visible to regulators, putting investors at a risk of loss.
Blockchain-Based Exchange-Traded Derivatives FMIs: Scenarios of Futures and Options

Exchange-traded derivatives have substantially the same business procedure as spot transactions, from trading and clearing to settlement. Therefore, the blockchain scheme proposed in Chap. 1 is also applicable to trading in futures, options and other exchange-traded derivatives. Similarly, BBO smart contracts, trade smart contracts, smart contracts on CCP guaranteed settlement and smart contracts on CCP risk management can be created for derivative trading on the blockchain.
Blockchain-Based OTC Derivatives FMI

The OTC derivatives market has the same pain points as other OTC markets (see Chap. 4), especially low market transparency and high systemic risk. After the outbreak of the global financial crisis in 2008, the regulatory authorities paid close attention to potential risks in the OTC derivatives market and launched a series of market reforms.
Blockchain-Based Cross-Border FMIs: Scenario of Depository Receipts

Compared with traditional ledger technology, blockchain technology is difficult to forge, tamper-proof, efficient, traceable and easy to audit. It also guarantees the consistency of distributed ledgers using transaction signatures, consensus algorithms and cross-chain technology and automatically completes the reconciliation among ledgers, certificates and assets in real time.
Blockchain-Based New Payment Systems: Digital Currencies

Looking back at history, technological changes have transformed the form of money from commodities, metals and paper to electronic forms. The connotations of money have extended from the theory that “although gold and silver are not by nature money, money is by nature gold and silver” to broader monetary dimensions to include credit money and highly liquid financial assets. With the rapid development of digital technologies such as blockchain, big data, cloud computing and artificial intelligence, technology has had a greater effect on the evolution of money, giving birth to a new type of money—digital currency. In line with this trend, the digitalization of fiat currency has become the most important monetary and financial reform in the digital era, attracting broad attention from central banks, industries and academia around the world.
Blockchain Architecture, Components and Considerations

This chapter is the second of three aimed at familiarizing readers with blockchain-related fundamentals and components. While Chapter 1 introduced readers to the foundational concepts of blockchain, its history, as well as some of its basic attributes, this chapter aims at providing readers with more detailed information about blockchain components and foundational concepts; especially as it pertains to its cryptographically secure attributes and mechanisms. Topics discussed in this chapter include: