Reviewerly/exHarmony huggingface dataset:
NEWS TRACING SYSTEM USING BLOCKCHAIN

The current pandemic situation has made us realize how fake news can have detrimental impacts on society.Although we can't ignore the benefits of social media, we surely can't overlook the misuse of these mediums to spread rumors and fake articles.These posts/ articles/ messages can be commonly called "Fake News" and they not only create misinformation but also create panic among the public.Blockchain is a disruptive technology that is being used to make innovative solutions in the sectors of food, fashion, supply chain, and banking.The decentralized and transparent nature of blockchain can not only be used in the above sectors but it can also be used to tackle the problem of fake news.Several solutions have been proposed to combat the problem using blockchain.This paper will propose one such solution to combat the fake news problem that can be used by all the news agencies.The system is called the "News Tracking and Tracing System " and is built using Ethereum.
UAV Navigation System Autonomous Correction Algorithm Based on Road and River Network Recognition

The paper considers an original autonomous correction algorithm for UAV navigation system based on comparison between terrain images obtained by onboard machine vision system and vector topographic map images. Comparison is performed by calculating the homography of vision system images segmented using the convolutional neural network and the vector map images. The presented results of mathematical and flight experiments confirm the algorithm effectiveness for navigation applications.
Spatial trends of maximum and minimum temperatures in different climate zones of Pakistan by exploiting ground-based and space-borne observations

Temperature is a key indicator of a changing climate. A trivial change in temperature can result in significant changes in weather patterns with severe repercussions on ecology and socio-economic conditions of an area. This study aims to assess the changing trends in maximum and minimum temperatures in five climatic zones of Pakistan, and compare ground-based with satellite data from AIRS over these climatic zones of Pakistan. Mann Kendall test has been used to signify the temperature trends in all zones and seasons while Sen's slope value has been used to measure the magnitude of the trends for the time period 1978 to 2016. Ground observations have been compared with AIRS satellite observations for ascending and descending air temperatures by employing correlation and mean bias error. The results show that AIRS product (AIRSX3STM) underestimates Tmax and Tmin in all zones of Pakistan with the underestimation being most prominent in zone A. The trend analysis for maximum and minimum temperatures shows an increase in temperatures for all climate zones. Overall, Pakistan's maximum temperature increased by 0.026°C/year with the maximum increase of 0.05°C/year during pre-monsoon, and the minimum temperature increased by 0.027°C/year.
Automatic Speaker Identification by Voice Based on Vector Quantization Method

In this paper, the systems of speaker identification of a text-dependent and independent nature were considered. Feature extraction was performed using chalk-frequency cepstral coefficients (MFCC). The vector quantization method for the automatic identification of a person by voice has been investigated. Using the extracted features, the code book from each speaker was built by clustering the feature vectors. Speakers were modeled using vector quantization (VQ). Using the extracted features, the code book from each speaker was built by clustering the feature vectors. Codebooks of all announcers were collected in the database. From the results, it can be said that vector quantization using cepstral features produces good results for creating a voice recognition system.
ARTIFICIAL NEURAL NETWORK

Artificial Neural Networking or ANN is the way by which computers can mimic the biological nervous system by building a huge number of simulated neurons, which are joined together in a number of ways to form networks. A neural network has multiple processors instead of just one central processor and thus is very task efficient. They cannot be made to perform a specific task because they learn via experience, which is unlike any other computer processor. They follow certain learning laws while receiving feedback from the environment. This paper gives an overview of Artificial Neural Network, its working and training.
Software Requirements Prioritization Tool using a Hybrid Technique

Requirements Prioritization (RP) helps to discover the most desired requirements. System developers are not always fully able to implement stakeholders' requirements when time, scope and cost are limited. To solve this challenge, requirements must be prioritized. A few prioritization techniques have been proposed but nonehasbeen automated. Furthermore, rank reversals (updating rank status whenever requirements are added or deleted) is a major limitation of existing techniques. This paper uses AHP and Cost-Value methods for requirement prioritization while addressing rank reversal. The techniques are able to rank every requirement based on relative value and implementation cost. The AHP method allows an input from the system developers and system users and makes a pair wise comparison. The Cost value method ranks the requirements in terms of the ratio of value to cost. As requirements increases, human inconsistency in judgment increases. AHP calculates inconsistency and if it is less than 0.1 then stakeholders should consider new input values. The prioritization methods are implemented into a prioritization tool which creates a list of the prioritized requirements as output, then consequently generates a scatter plot to display relative values and implementation costs of input requirements. This tool is evaluated in terms of efficiency (total time taken for prioritization) and ease of use.
Diagnosing the Issues and Challenges in Data Integration Implementation in Public Sector

A reliable data is like the oxygen to the application systems. It keeps the application systems breathing and producing meaningful information for day to day operation and decision-making purpose in an organization. The increasing demand of reliable data in day to day operation sparks a big challenge in data integration implementation in many domains including public sector. Through a successful data integration implementation, a trustable non duplicate data will be provided to the stakeholders. Public sector as a domain which solely rely on creating value through services to the stakeholders critically in needs of reliable data to serve the reliable information to the stakeholders. However, there is lack of research being done in diagnosing the issues and challenges of data integration implementation in public sector. It is crucial to identify the issues and challenges so that we could come out with the best recommendation to ensure the feasibleness of data integration in public sector. Thus, this research had explored the issues and challenges in data integration implementation in public sector. Data has been collected using the qualitative method through the content analysis and expert interview approaches. Four main issues and challenges were identified, namely; (1) lack of management and organization support, (2) policy, standard and politics, (3) human resource incapability, and (4) lack of governance. The finding of this research enlightens the issues and challenges in data integration implementation in public sector and offers the opportunity for future work to propose the solutions to overcome the identified issues and challenges.
A local area quantum teleportation network based on an array of electrically activated graphene waveguides

We present a scheme to generate a continuous variable (CV) multipartite entangled state using an array of plasmonic graphene waveguides that are activated by nonclassical driving microwave modes. Within this scheme, we can exploit the interaction of two light fields coupled to the same microwave mode in each waveguide to produce any type of multipartite Gaussian entangled state. A teleportation network is illustrated using the resultant CV multipartite entangled state. In particular, the proposed setup enables coherent state teleportation across remotely connected nodes with fidelity above a threshold limit of 2/3, providing secure quantum teleportation networking even in the presence of losses.
Using Iterative Pairwise External Validation to Contextualize Prediction Model Performance: A Use Case Predicting 1-Year Heart Failure Risk in Patients with Diabetes Across Five Data Sources

External validation of prediction models is increasingly being seen as a minimum requirement for acceptance in clinical practice. However, the lack of interoperability of healthcare databases has been the biggest barrier to this occurring on a large scale. Recent improvements in database interoperability enable a standardized analytical framework for model development and external validation. External validation of a model in a new database lacks context, whereby the external validation can be compared with a benchmark in this database. Iterative pairwise external validation (IPEV) is a framework that uses a rotating model development and validation approach to contextualize the assessment of performance across a network of databases. As a use case, we predicted 1-year risk of heart failure in patients with type 2 diabetes mellitus.The method follows a two-step process involving (1) development of baseline and data-driven models in each database according to best practices and (2) validation of these models across the remaining databases. We introduce a heatmap visualization that supports the assessment of the internal and external model performance in all available databases. As a use case, we developed and validated models to predict 1-year risk of heart failure in patients initializing a second pharmacological intervention for type 2 diabetes mellitus. We leveraged the power of the Observational Medical Outcomes Partnership common data model to create an open-source software package to increase the consistency, speed, and transparency of this process.A total of 403,187 patients from five databases were included in the study. We developed five models that, when assessed internally, had a discriminative performance ranging from 0.73 to 0.81 area under the receiver operating characteristic curve with acceptable calibration. When we externally validated these models in a new database, three models achieved consistent performance and in context often performed similarly to models developed in the database itself. The visualization of IPEV provided valuable insights. From this, we identified the model developed in the Commercial Claims and Encounters (CCAE) database as the best performing model overall.Using IPEV lends weight to the model development process. The rotation of development through multiple databases provides context to model assessment, leading to improved understanding of transportability and generalizability. The inclusion of a baseline model in all modelling steps provides further context to the performance gains of increasing model complexity. The CCAE model was identified as a candidate for clinical use. The use case demonstrates that IPEV provides a huge opportunity in a new era of standardised data and analytics to improve insight into and trust in prediction models at an unprecedented scale.
Localization at Users' Sites is Not Enough: GhanaPostGPS and Power Reticulations in the Postcolony

Purpose: This study re-examines contemporary localization theory to understand the shortcomings of the theory using the case of a state-sponsored postcolonial technology localization project. I call for centering the analysis of the power reticulations in context-specific technology localization.&lt;br/&gt; Method: I engaged in extensive review of existing research on the subject and conducted ethnographic and digital surveys with users and non-users of the technology I studied. This method provided nuanced perspectives on technology localization for a grounded user experience analysis.&lt;br/&gt; Results: Against current theoretical assumptions that support localization in user contexts as the solution to the chasm between developer culture and user culture, I argue that the reticular nature of power and developers' neglect of users' geo-epistemology also create a chasm within localization at users' sites.&lt;br/&gt; Conclusion: We need to examine the complex work of power in user contexts as part of a holistic theory on technology localization in user contexts. Thus, current assumptions must be revised. This revision must insist on the primary role of users' worldings in localization. The context of the postcolony provides a privileged insight into theorizing technology localization and must not be seen only as a "kingdom of ethnography" (Mbembe, 2021, p. 14).
Association Rule Analysis of Influencing Factors of Literature Curriculum Interest Based on Data Mining

In recent years, the amount of educational data in colleges and universities has increased rapidly. Each university has set up multiple courses to recruit talents. Students cannot choose courses. The emergence of data mining technology and its application in college teaching and curriculum has been preferred particularly to streamline these activities. When analyzing the correlation of courses based on data mining technology, we usually use the correlation between the scores of various subjects to analyze the correlation between courses. Correlation among various courses that are offered at colleges or universities is reflected through many different aspects such as factors or metrics, which are affecting course interest, course content, course arrangement, etc. In this article, we have thoroughly analyzed various factors that are affecting students’ interest in literature courses with the help of association rules of data mining technology. Through the collected original data, this article uses Apriori algorithm to screen the association rules affecting students’ interest in literature courses and combines them with the current teaching situation to complete the rule analysis. The results of rule analysis show that the most relevant factors affecting students’ interest in literature curriculum mainly include the space-time dimension of textbook selection and compilation, the processing method of selected reading, and the evaluation method. The reading content is effectively processed by using the counterpoint reading method, and the literature curriculum textbooks are compiled from the perspective of cross-cultural communication, to enhance students’ interest in the literature curriculum.
Construction of an Artificial Intelligence Writing Model for English Based on Fusion Neural Network Model

This paper presents an in-depth study and analysis of the model of English writing using artificial intelligence algorithms of neural networks. Based on word vectors, the unsupervised disambiguation, and clustering of multimedia contexts extracted from massive online videos, the disambiguation accuracy reaches over 0.7, and the resulting small-scale multimedia context set can cover up to 90% of vocabulary learning tasks; user experiments show that the multimedia context learning system based on this method can improve the effectiveness and experience of ESL vocabulary learning, as well as the long-term word sense memory of learners. The results are 30% better. Based on the dependency grammatical relations and semantic metrics of collocations on a large-scale professional corpus, we established a collocation intention description and retrieval method in line with users’ linguistic cognition and doubled the usage rate of collocation retrieval on the actual deployment system after half a year, becoming a user “sticky” ESL writing aid, and further defined style. Dictionaries only provide basic lexical definitions, and, even if supported by example sentences, they still cannot meet the needs of ESL authors in terms of expressive accuracy and richness. However, the current machine translation is based on the black box deep neural network construction, and its translation process is not understandable and interactive. Among the three algorithmic models constructed in this paper, the multitask learning model outperforms the conditional random field model and the LSTM-CRF model because the multitask learning model with auxiliary tasks solves the problem of sparse data to a certain extent, allowing the model to be trained more adequately in the case of uneven label distribution, and thus performs better than other models in the task of grammatical error detection.
Automated Crossword Solving

We present the Berkeley Crossword Solver, a state-of-the-art approach for automatically solving crossword puzzles.Our system works by generating answer candidates for each crossword clue using neural question answering models and then combines loopy belief propagation with local search to find full puzzle solutions.Compared to existing approaches, our system improves exact puzzle accuracy from 71% to 82% on crosswords from The New York Times and obtains 99.9% letter accuracy on themeless puzzles.Additionally, in 2021, a hybrid of our system and the existing Dr.Fill system outperformed all human competitors for the first time at the American Crossword Puzzle Tournament.To facilitate research on question answering and crossword solving, we analyze our system's remaining errors and release a dataset of over six million question-answer pairs.
Decentralized and Collaborative Tracing for Group Signatures

We propose a decentralized but collaborative attribute-based tracing mechanism (a signer-identifying mechanism) for group signatures. Instead of a central tracing party in our scheme, a set of tracers satisfying the attribute set used for generating the group signature can identify the signer. Thus our proposal limits the parties who can identify the signer. On the other hand, it decentralized the tracing authority.
GasSaver

Currently, the number of smart contracts using Solidity language in Ethereum increases at a breakneck speed. In this context, optimizing gas fees in smart contracts becomes necessary to save costs for both developers and users who interact directly with these contracts. To optimize gas, developers typically need to have deep knowledge of blockchain and programming languages, making it a significant challenge for new programmers. In this paper, we introduce GasSaver - an open-source tool that analyzes Solidity-based contracts and suggests modifications to reduce transaction fees. Our tool, which relies on a set of seven rules for identifying non-optimized codes, shows that 6,333 in a total of 10,245 top most interactive contracts on Ethereum contain at least one optimization problem and the total problems are more than 30,000. Hence, the use of this tool can reduce a considerable cost for the deployment and interaction of smart contracts.
DPaSE

Cloud storage is becoming increasingly popular among end users that outsource their personal data to services such as Dropbox or Google Drive. For security, uploaded data should ideally be encrypted under a key that is controlled and only known by the user. Current solutions that support user-centric encryption either require the user to manage strong cryptographic keys, or derive keys from weak passwords. While the former has massive usability issues and requires secure storage by the user, the latter approach is more convenient but offers only little security since encrypted data is susceptible to offline attacks. The recent concept of password-authenticated secret-sharing (PASS) enables users to securely derive strong keys from weak passwords by leveraging a distributed server setup, and has been considered a promising step towards secure and usable encryption. However, using PASS for encryption is not as suitable as originally thought: it only considers the (re)construction of a single, static key -- whereas practical encryption will require the management of many, object-specific keys. Using a dedicated PASS instance for every key makes the solution vulnerable against online attacks, inherently leaks access patterns to the servers and poses the risk of permanent data loss when an incorrect password is used at encryption. We therefore propose a new protocol that directly targets the problem of boostrapping encryption from a single password: distributed password-authenticated symmetric encryption DPaSE. DPaSE offers strong security and usability, such as protecting the user's password against online and offline attacks, and ensuring message privacy and ciphertext integrity as long as at least one server is honest. We formally define the desired security properties in the UC framework and propose a provably secure instantiation. The core of our protocol is a new type of Oblivious Pseudorandom Function (OPRF) that allows to extend a previous partially-blind query with a follow-up request and will be used to blindly carry over passwords across evaluations and avoid online attacks. Our (proof-of-concept) implementation of DPaSE uses 10 exponentiations at the user, 4 exponentiations and 2 pairings at each server, and has a server throughput of 76 account creations and 37 (user authentication followed by) encryptions per second, when run between a user and 2-10 servers.
Video Tactical Intelligence Analysis Method of Karate Competition Based on Convolutional Neural Network

The performance of image classification technology based on deep network has been greatly improved, making computer vision enter the stage of industrialization and be gradually applied to many aspects of human work and life. As a typical classification task in computer vision, human behavior recognition has immeasurable potential value in medical, family, transportation, and other scenarios. At the same time, in the field of competitive sports, the integration of artificial intelligence technology and sports technical and tactical analysis is undoubtedly an important way to innovate and improve the technical and tactical level. Taking karate as an example, the study of athletes’ training and competition videos is an important means and method for technical and tactical analysis in competitive sports. Traditional tactical intelligence analysis methods have many shortcomings, such as high labor cost, serious data loss, long delay, and low accuracy. Therefore, based on the convolutional neural network, this paper establishes a new graph convolution model for automatic intelligent analysis of karate athletes’ technical action recognition, action frequency statistics, and trajectory tracking. The technology effectively makes up for the disadvantages of traditional tactical intelligence analysis methods. The research results show that the new topology map construction method has a significant effect on improving the accuracy of behavior recognition and also lays a foundation for technical and tactical analysis.
Breast Cancer Detection from Histopathological Images using Deep Learning and Transfer Learning

Microscopic analysis of breast tissues is one of the most precise technique used for the diagnosis of breast cancer. However, this technique is time consuming as it requires scanning of various breast tissue images with different magnification levels. Computer assisted diagnosis techniques that make use of machine learning methods can automate some of the tasks in the breast cancer diagnostic process. This paper conducts a comparative study of the use of CNN and transfer learning based DenseNet121 deep learning models for automatic classification of breast histopathological images into malignant or benign. This study uses 7909 histopathological images of benign and malignant types. The impact of image magnification, scaling and rotation on the model's accuracy was investigated. Results show DenseNet121 outperformed (86.6% accuracy) for the 100X magnification level with 128*128 image size scaling. Transfer learning significantly increased the training accuracy by 16.4% for 100X magnification level.