Reviewerly/exHarmony huggingface dataset:
A Survey on Various Approaches Used in Named Entity Recognition for Indian Languages

Named Entity Recognition (NER)” is a application of Artificial Intelligence , Machine Learning and “Natural Language Processing (NLP”).In NER, various classes of Named entity such as name of a person , an organization name, name of location, name of designation etc., are find out which is required in many NLP activities like question-answering system ,machine translation, artificial intelligence, summarization of documents, academics, robotics, Bioinformatics etc. Mostly NER task was evident for foreign languages but for Indian constitutional languages, due to some challenges present for example scarcity of resources, ambiguity present in languages, morphologically rich behavior of languages etc. ,NER work has been done for few of languages. In our paper, we presented several challenges available in NER for Indian languages and compared them by measuring various standard evaluation metric values like precision, recall and F-measure. In future extension, we would develop a efficient system, which would be more accurate, and which will cater many more Named Entity tags than existing systems ,for Indian languages NER tools.
Security of multiple RGB images in the time domain and frequency domain

Secure and efficient transmission of images from a sender to a receiver through the public channel has become a prime agenda. An encryption algorithm to secure three color images simultaneously using matrix affine cipher, factional discrete cosine transform and Arnold transform is proposed in this paper. In earlier proposed algorithms, pixel intensities were not changed but rather shifted. In our proposed algorithm, pixel intensities are both shifted and changed. The color maps of the three color images are extracted to convert three color images into three indexed images respectively. The three indexed images are taken as R(red), G(green) and B(blue) channels of a color image. The color image is encrypted using the proposed encryption algorithm. The encryption algorithms for the security of color images proposed so far are secure either in time domain or in frequency domain, but the proposed encryption algorithm is secure in both the time and the frequency domain without any loss of data. The security of the proposed encryption algorithm depends not only upon secret keys but also upon the correct arrangement of the secret keys as compared to existing similar encryption algorithms whose security depends only upon secret keys. The encrypted(cipher) image is a single color image and is favorable for secure and efficient transmission over the public channel. Simulation is done with standard examples to analyze the validity of the proposed encryption scheme. We have provided the complete security analysis, statistical analysis and comparison with the existing similar encryption algorithms.
Study of Clouds in the Basic Geography Course

The aim of the research is to solve the methodological problem of studying clouds in the school Geography course for grades 5-6 by analyzing “classical” school textbooks, as well as by interviewing students and Geography teachers. The survey results show that the vast majority of students demonstrate cognitive interest to studying the shapes of clouds, but there is little information on this topic in school Geography textbooks. The article suggests ways to solve this methodological problem, which the author has been implementing in practice during the twenty years of observing and studying clouds with students. The scientific originality of the research consists in formulating the guidelines to study clouds in the basic Geography course. As a result of a pedagogical experiment carried out in St. Petersburg, as well as in Leningrad and Nizhny Novgorod regions, positive dynamics in the implementation of formulated guidelines has been revealed.
Textile D-forms and<i>D<sub>4d</sub></i>

D-forms were originally created from inflexible materials and have subsequently been considered as abstract mathematical objects. This paper describes a textile instance of a D-form, with ornamentation of the constituent surfaces as the highlighted feature. A set of 11 biscornu has been fashioned to provide a 3D sampler of the axial point group D4d and its subgroups, using hitomezashi. Thus, this paper provides a link between the D-form literature and that of complete symmetry samplers in the fibre arts.
Research on Guideline Navigation System Based on Deep Learning

In order to solve the problem of the environmental information that the vision impairment cannot perceive itself due to its own vision defects, a navigation system based on Faster RCNN depth learning is proposed. The navigation system utilizes the improved RESNET50 for the backbone network training data, and the visual sensor recognizes the environmental target, the identified blind, obstacles, etc. will be admitted to the thresholded thresholded threshold, the determination position, follow the blind trajectory and avoid the obstacle. Complete the path planning of the four-wheeled mobile robot to the outdoor environment. Through the ROS robot operating system, build an experimental platform, verify the improved algorithm, effectively prove to meet the navigation needs of the vision impairment.
Automated Insights on Visualizations with Natural Language Generation

Quantitative data, such as a 10k financial report, requires cognitive effort to scan the columns and rows and identify patterns and important takeaways, whether novice or subject matter expert. Visualizations can be used to summarize and reveal patterns. However, unless a visualization contains arrows or other callouts, it still requires cognitive effort to understand and rank the important conclusions to which a reader should pay attention. In this research, we aim to reduce the cognitive effort in understanding tabular data by combining charts with ranked natural language generated (NLG) bullet point statements that summarize the top takeaways. The contribution of this work is an NLG pipeline to computationally extract insights from tabular data and provide textual comments, which are then integrated with visualizations of the same data set.
Cybersecurity and Data Privacy in the Digital Age

The COVID-19 pandemic has brought to the fore a number of issues regarding digital technologies, including a heightened focus on cybersecurity and data privacy. This chapter examines two aspects of this phenomenon. First, as businesses explore creative approaches to operate in the “new normal,” the security implications of the deployment of new technologies are often not considered, especially in small businesses, which often possess limited IT knowledge and resources. Second, issues relating to security and data privacy in monitoring the pandemic are examined, and different privacy-preserving data-sharing techniques, including federated learning, secure multiparty computation, and blockchain-based techniques, are assessed. A new privacy-preserving data-sharing framework, which addresses current limitations of these techniques, is then put forward and discussed. The chapter concludes that although the worst of the pandemic may soon be over, issues regarding cybersecurity will be with us for far longer and require vigilant management and the development of creative solutions.
Interactive Book Augmented Reality (IBAR) for Lesson Physics on STEM

Abstract The scientific development process can be carried out with STEM (Science Technology Engineering Mathematics) education where this interdisciplinary approach trains students to learn where the concept of science is combined with the context in applying science, technology, engineering, and mathematics. The purpose of the study is the development of Interactive Book Augmented Reality (IBAR) for Lesson Physics on STEM. The research method is the ADDIE Model which consists of the stages of Analysis, Design, Development, Implementation, and Evaluation. The sample for the implementation of IBAR used in the study amounted to 90 students aged 19-22 years from a province in Indonesia. The results showed that IBAR contributed to students’ concepts in physics. In the questionnaire given to students, information was obtained that the IBAR application was more useful, realistic, and interesting for their learning; help them understand and analyze problems and STEM scenarios. It has been suggested that IBAR technology could be a potent and effective tool to enable Lesson Physics students in the STEM process. In addition, the implications of using IBAR for physics education and recommendations for further studies are also discussed in this study.
A User Experience Design Process in Mobile Applications Prototypes: A Case Study

More and more importance is being given to the best usability and user experience that a user should have when using digital products. In this context, it is essential to recognize its importance in the design and prototyping process of user applications. As such, a Workshop was structured and developed, supported by a UX Design process, to create high-fidelity prototypes of applications for mobile devices. As a result of this Workshop, six prototypes were obtained and for each one of them the generic functionalities prototyped are indicated. One of these prototypes was selected and presented in this article as a case study. It is possible to demonstrate the use of a UX Design method for its creation, based on 5 plans: Strategy, Scope, Structure, Skeleton and Surface. Thus, it is verified that the adoption of a UX Design method can enhance the best user experience of a mobile App.
Construction of English Multi-module Online Learning System Based on Intelligent Analysis Technology

Big data brings changes and innovations in learning styles. The significance of big data technology is not only the huge amount of data information, but also the professional processing of data with specific meaning. Based on the experimental platform of intelligent analysis technology and management decision making, College English practical teaching in the era of big data integrates big data technology into the knowledge system. It optimizes experimental teaching content, in-school practice and out-of-school practice, highlights students’ innovative practical ability, and rebuilds the practical teaching system of College English under the background of big data. Based on the OBE concept, this paper analyses the OBE ability training model consisting of “three kinds of education” and “four kinds of abilities”. It designs the OBE concept-based teaching process consisting of “on-campus circulation” and “off-campus circulation”, constructs a practical teaching system consisting of multiple modules, and puts forward specific guarantee measures.
Analysis of the 3D Application Evaluation System of Landscape Based on Hybrid Cooperation of VR and AI

The traditional urban landscape planning and design method cannot integrate the design into the whole planning scene and cannot express the planning effect of multiple schemes and large scenes in an efficient, intuitive, and coordinated way. With the development of computer science and technology, virtual reality technology based on AI is playing an important role in many aspects of urban landscape planning and design. In this paper, on the basis of 3D visualization landscape planning and design, 3D visualization urban landscape modeling is carried out using VR combined with the constructed deep neural network (DNN) to establish a 3D database and virtual scene model of the landscape, and 3D representation of the virtual scene landscape is realized. The designed solution allows the user to grab the object and release it into the workspace. If any placement is needed, the user can pick up the layer again and move it to a new location. Finally, the simulation experiment and its result analysis were conducted to effectively analyze the site of the real model and its matching rate, to realize the integration of the 3D image, 3D stereo, and system integration technology, and to finally complete the landscape design task.
Application of Steganography Imaging by AES and Random Bit

The goal of steganography is to hide the data in another medium, meaning disguising the data, so that the existence of the messages can be concealed. Steganography can be applied to many formats of data, including audio, video, and images and can hide any kind of digital information through data hiding techniques. In this work, we propose an application of steganography imaging that would ensure the secure transfer of data along with integrity and confidentiality because steganography relies on hiding messages in unsuspected multimedia data. In this paper, we providing a steganography imaging application which is based on the Advanced Encryption Standard (AES) and random bit technique.
Private Decision Tree Evaluation with Constant Rounds via (Only) Fair SS-4PC

Multiparty computation (MPC) is a cryptographic method that enables a set of parties to compute an arbitrary joint function of the private inputs of all parties and does not reveal any information other than the output. MPC based on a secret sharing scheme (SS-MPC) and garbled circuit (GC) is known as the most common MPC schemes. Another cryptographic method, homomorphic encryption (HE), computes an arbitrary function represented as a circuit by using ciphertexts without decrypting it. These technologies are in a trade-off relationship for the communication/round complexities, and the computation cost.The private decision tree evaluation (PDTE) is one of the key applications of these technologies. There exist several constant-round PDTE protocols based on GC, HE, or the hybrid schemes that are secure even if a malicious adversary who can deviate from protocol specifications corrupts some parties. There also exist other protocols based only on SS-MPC that are secure only if a semi-honest adversary who follows the protocol specification corrupts some parties. However, to the best of our knowledge, there are currently no constant-round PDTE protocols based only on SS-MPC that are secure against a malicious adversary.In this work, we propose a constant-round four-party PDTE protocol that achieves malicious security. Our protocol provides the PDTE securely and efficiently even when the communication environment has a large latency.
Classification of bone marrow cells in the diagnosis of acute lymphoblastic leukemia

Abstract The paper presents approaches to automated classification of bone marrow cells in the diagnosis of acute lymphoblastic leukemia and minimal residual disease using image recognition procedures. The classification methods that show the best accuracy in the recognition of eight types of bone marrow cells were experimentally determined. Recommendations for their use are given.
Provably secure and biometric-based secure access of e-Governance services using mobile devices

All the citizens of a country avail e-Governance services through the Internet, where it inherently does not offer security services. Accessing these services securely and transparently is a significant concern for both the citizen and the government. The multi-server authentication scheme with user biometric and personal mobile devices is an efficient way to reduce security issues related to unauthorized access. It is difficult for a user to remember multiple credentials. If a user performs a login attempt with an incorrect credential, then she is denied access to the service. Therefore, one credential to access every server is a convenient option. Nowadays, most mobile devices are compatible with the biometric scanner; accordingly, it can quickly record the biometric features during the authentication process. This paper devises an elliptic curve-based secure multi-server authentication scheme with the user biometric, password, and mobile device. A user can access various e-Governance services from any location through a dedicated mobile app in our scheme. We perform some formal security analysis of our scheme in the random oracle model, BAN logic, and AVISPA tool. The performance evaluation shows that our scheme accomplishes the known security and functionality features concerning other comparable schemes. Thus, our scheme will be suitable for providing secure access to e-Governance services to every citizen through their mobile devices.
Real-Time Moving Target Detection System Based on FPGA

With the rapid development of social economy, video surveillance technology has attracted more and more attention. The proliferation of video data has made the calculation and storage of image data more difficult. However, to process images with software cannot meet the growing demand of video business because it is slow. Current moving target detection algorithms consume a lot of resources and power in PC (Personal Computer). In view of these problems, this paper aims to deploy the motion target detection algorithm on the ZYNQ hardware platform with the combination of hardware and software. On the ZYNQ hardware platform, the video image acquisition, frame difference algorithm, morphological image processing and bounding box design are completed in the PL (Programmable Logic) part, and the camera and VDMA (Video Direct Memory Access) related configuration are completed in the PS (Processing System) part. The experimental results show that the system can detect moving targets in real time with lower power consumption and fewer resources, which can be applied to the field of video surveillance.
Short-Term Hydrothermal Scheduling Using Improved Social Group Optimization Algorithm

Social Group Optimization (SGO) was introduced in 2016, whose algorithm is made by seeing the social interaction of human beings. SGO is divided into two-phase for searching for the best fitness value, i.e. phase of improvement and phase of acquisition. In the improving phase, each human in the social group gains information by communicating with each other, whereas in acquiring phase human beings of the social group communicate with a random individual and gain more information to give the best fitness value. In the improved version of SGO, i.e. Improved Social Group Optimization (ISGO) the self-awareness probability (SAP) component is introduced as a modification in acquiring phase, which improves an individual ability to learn from the best knowledgeable human of a social group to give more accurate fitness value. The core goal of using this modification in SGO is to get the best minimum total production cost for the thermal plant by meeting all the operating and physical constraints for Short-Term Hydrothermal problems using simulation.
The NIOSH Agricultural Centers’ YouTube Channel: Time Series Modeling of Viewership of Agricultural Health and Safety Videos

We sought to understand the mechanism underlying the growth trajectory in the United States Agricultural Safety and Health Centers YouTube channel. We also explored the benefits and limitations of using YouTube analytics to evaluate the impacts of public health interventions involving YouTube. Time series analysis of total views, total watch hours, average duration of watch time, and number of subscribers were assessed to determine the monthly patterns of non-seasonal and seasonal components in the data from 2013 to 2020. Health, safety, and animal handling video views were summarized descriptively across time and season. Lastly, time series regressions were used to determine the type of video that best predicted growth in the channel viewership metrics. The time series were not random but could be explained by autoregressive and moving average correlation structures. Health videos were the strongest predictors of future growth but were not the most watched type of video. Strong seasonality components indicated that videos were most watched during periods of high agricultural activity, but less so during the winter months. Generally, growth in YouTube viewership metrics was explained by past month viewership predicting future viewership. Outreach and media content may produce spikes of increased interest, but in order to sustainably grow the channel over time, Ag Centers and other agricultural stakeholders should continue to focus on the value of particular content to potential viewers, how and when content is released, and strategic promotion of the channel and its videos.
Multiple Roles of Middle Managers in Agile Project Governance: An Activity Theory Perspective

Abstract Project governance (PG) is an important activity in agile software development (ASD) projects. Middle managers (MMs) are part of the governance structure in ASD projects. PG and middle management phenomena in ASD projects are under-researched and not fully understood. This ongoing study aims to fill a gap by investigating the roles of MMs in agile PG through the lens of Activity Theory. The study adopts a qualitative and interpretive case study approach. To date, the study has identified 24 roles that MMs perform during agile PG.
Optimizing a tableau reasoner and its implementation in Prolog

One of the foremost reasoning services for knowledge bases is finding all the justifications for a query. This is useful for debugging purpose and for coping with uncertainty. Among Description Logics (DLs) reasoners, the tableau algorithm is one of the most used. However, in order to collect the justifications, the reasoners must manage the non-determinism of the tableau method. For these reasons, a Prolog implementation can facilitate the management of such non-determinism. The TRILL framework contains three probabilistic reasoners written in Prolog: TRILL, TRILLP and TORNADO. Since they are all part of the same framework, the choice about which to use can be done easily via the framework settings. Each one of them uses different approaches for probabilistic inference and handles different DLs flavors. Our previous work showed that they can sometimes achieve better results than state-of-the-art (non-)probabilistic reasoners. In this paper we present two optimizations that improve the performances of the TRILL reasoners. The first one consists into identifying the fragment of the KB that allows to perform inference without losing the completeness. The second one modifies which tableau rule to apply and their order of application, in order to reduce the number of operations. Experimental results show the effectiveness of the introduced optimizations.
EmotionNet: ResNeXt Inspired CNN Architecture for Emotion Analysis on Raspberry Pi

Facial Expression Recognition has seen remarkable advancements in scientific research and development in recent years. Furthermore, these advancements have enabled to efficiently extract facial features for psychological analysis, improvement of consumer experience, and research on human-computer interaction. Facial expressions play a vital role in human existence because they are a significant part of non-verbal communication. However, existing FER systems require large computational capacity, rendering them unusable for use in either small-scale systems or large-scale deployments. Presented work aims to find a solution to aforesaid problem by developing an FER system capable of running on affordable and modular devices specifically Raspberry pi. In presented work, the FER system is developed that identifies and extracts the face using OpenCV, then classifies the expressions using a ResNeXt inspired CNN architecture named EmotionNet. The classifier network was trained on the FERPlus dataset, and it achieved 70.22% micro-accuracy on the test set. An interactive GUI platform was designed using KivyMD to control the overall system. The classification of facial expression takes place into the following five categories: neutral, happy, sad, angry, and surprised. The detected emotion is used to generate a descriptive report consisting of qualitative material, in order to apprise the user of their state of emotion, and alter it in case of negative emotions. The developed system running on Raspberry Pi provides a high throughput rate of 1.33 frames per second.