Reviewerly/exHarmony huggingface dataset:
Predicting the Structure of a Lexical Environment from Properties of Verbal Working Memory

We analyzed a Japanese lexical database to investigate the structure of the lexical environment based on the hypothesis that the lexical environment is optimized for the functioning of verbal working memory. Our prediction was that, as a consequence of the cultural transmission of language, low-imageable meanings tend to be represented by frequent phonological patterns in the current vocabulary rather than infrequent phonological patterns. This prediction was based on two findings of previous laboratory studies on verbal working memory. (1) The quality of phonological (phonemic and accent) representations in verbal working memory depends on phonological regularity knowledge; therefore, short-term phonological representations are less robust for words with infrequent phonological patterns. (2) Phonological representations are underpinned by contributions from semantic knowledge; therefore, phonological representations of highly imageable words are more robust than those for low-imageable words. Our database analyses show that nouns with less imageable meanings tend to be associated with more frequent phonological patterns in Japanese vocabulary. This lexical structure can maintain the quality of phonological representations in verbal working memory through contributions of semantic and phonological regularity knowledge. Larger semantic contributions compensate for the less robust phonological representations of infrequent phonological forms. The quality of phonological representations is preserved by phonological regularity knowledge when larger semantic contributions are not expected.
A SYNOPSIS OF PURE AND APPLIED CRYPTOGRAPHY

The literature of cryptography has a curious story. Secracy, of course , played a central role ,but until the rst World war , important developments appeared in print in a more or less timely fashion. After the rst world war ,however things began to change.US. Army and Navy organizations working entirely in secret. In the “imitation Game “ we see that Allen turing build a machine called “The Enigma Machine “. And using statistical method he inspired to win The world war . This was an extraordinary example of the application of mathematics to see how math works in every section of life. I am going to write a series of paper of cryptology for everyone . It will be a good reading for anyone interest in cryptology and want to know more !!
Twitter Media Sentiment Analysis to Convert Non-Informative to Informative Using QER

The term sentiment analysis deals with sentiment classification based on the review made by the user in a social network.The sentiment classification accuracy is evaluated using various selection methods, especially those that deal with algorithm selection.In this work, every sentiment received through user expressions is ranked in order to categorise sentiments as informative and noninformative.In order to do so, the work focus on Query Expansion Ranking (QER) algorithm that takes user text as input and process for sentiment analysis and finally produces the results as informative or non-informative.The challenge is to convert non-informative into informative using the concepts of classifiers like Bayes multinomial, entropy modelling along with the traditional sentimental analysis algorithm like Support Vector Machine (SVM) and decision trees.The work also addresses simulated annealing along with QER to classify data based on sentiment analysis.As the input volume is very fast, the work also addresses the concept of big data for information retrieval and processing.The result comparison shows that the QER algorithm proved to be versatile when compared with the result of SVM.This work uses Twitter user comments for evaluating sentiment analysis.
An Optimal Algorithm for Secure Transactions in Bitcoin Based on Blockchain

Technological advancement has made a significant contribution to the change of the economy and the advancement of humanity.Because it is changing how economic transactions are carried out, the blockchain is one of the technical developments that has a lot of promise for this progress.The public record of the Bitcoin blockchain provides dispersed users with evidence of transaction ownership by publishing all transaction data from block reward transactions to unspent transaction outputs.Attacks on the public ledger, on the other hand, are a result of the fact that all transaction information are exposed.De-anonymization attacks allow users to link transaction entities and acquire user privacy through specified transaction amounts.As a result, in light of the Bitcoin blockchain system's privacy issues, this scheme combines the concept of coin mixing with encrypted transaction technology to create a truly anonymous blockchain system that preserves the payer identity and transaction amount privacy.The one-way aggregated signature technique of Boneh, Gentry, and Lynn systematically embeds the notion of mixing into the whole block.The homomorphic encryption approach of Boneh, Goh, and Nissim allows miners to check the legality of encrypted transactions.Miners will validate transactions, conceal transactions, and package transactions as entities in the scheme.Finally, this technique was chosen after a comparison of several privacy-preserving blockchain schemes.It not only ensures complete anonymity, but also keeps transaction storage overhead to a minimum.
Robust Symmetry Prediction with Multi-Modal Feature Fusion for Partial Shapes

In geometry processing, symmetry research benefits from global geometric features of complete shapes, but the shape of an object captured in realworld applications is often incomplete due to the limited sensor resolution, single viewpoint, and occlusion.Different from the existing works predicting symmetry from the complete shape, we propose a learning approach for symmetry prediction based on a single RGB-D image.Instead of directly predicting the symmetry from incomplete shapes, our method consists of two modules, i.e., the multi-modal feature fusion module and the detection-by-reconstruction module.Firstly, we build a channel-transformer network (CTN) to extract cross-fusion features from the RGB-D as the multi-modal feature fusion module, which helps us aggregate features from the color and the depth separately.Then, our self-reconstruction network based on a 3D variational auto-encoder (3D-VAE) takes the global geometric features as input, followed by a prediction symmetry network to detect the symmetry.Our experiments are conducted on three public datasets: ShapeNet, YCB, and ScanNet, we demonstrate that our method can produce reliable and accurate results.
Utilization of artificial intelligence for tuberculosis screening in Nepal

Tuberculosis (TB) is an infectious disease that needs to be diagnosed and enrolled for treatment. Artificial intelligence for TB (AI4TB) software screens TB suspected cases at the point of care and helps in quick diagnosis. This study aims to explore the significance and usefulness of AI4TB by comparing its performance with different diagnostic test results.A cross-sectional study was conducted among 197 participants who had symptoms suggestive to TB. The chest X-ray images were analyzed by AI4TB software and human expert readers. The bacteriological test results were obtained, and Kappa test was applied to calculate the inter-reader reliability. The sensitivity, specificity, positive predictive value and negative predictive value was calculated and ROC curve was generated.Among 85 sputum smear microscopy, about 21% of the had sputum positivity rate. At 0.4 threshold: 62.4%, at 0.5 threshold: 58.4% and at 0.6 threshold: 50.3% symptoms suggestive cases were identified having abnormal X-ray images. Reader-I identified 28.4% and Reader-II identified 37.1% of the symptoms suggestive cases of TB as positive cases. There was a significant substantial agreement between two human expert readers (k-0.783, p-value: <0.001). The ROC curve explored the fair sensitivity accuracy of the AI4TB test results at 0.5 threshold level (AUC = 0.72) and at 0.6 threshold level (AUC = 0.77).The sensitivity of the AI4TB was higher compared to different human readers. AI4TB can be the relevant screening tool for the TB symptoms suggestive cases prior to the laboratory test in the countries like Nepal with deficient health manpower.
Enhanced Cuckoo Search Optimization Technique for Skin Cancer Diagnosis Application

Skin cancer segmentation is a critical task in a clinical decision support system for skin cancer detection.The suggested enhanced cuckoo search based optimization model will be used to evaluate several metrics in the skin cancer picture segmentation process.Because time and resources are always limited, the proposed enhanced cuckoo search optimization algorithm is one of the most effective strategies for dealing with global optimization difficulties.One of the most significant requirements is to design optimal solutions to optimize their use.There is no particular technique that can answer all optimization issues.The proposed enhanced cuckoo search optimization method indicates a constructive precision for skin cancer over with all image segmentation in computerized diagnosis.The accuracy of the proposed enhanced cuckoo search based optimization for melanoma has increased with a 23% to 29% improvement than other optimization algorithm.The total sensitivity and specificity attained in the proposed system are 99.56% and 99.73% respectively.The proposed method outperforms by offering accuracy of 99.26% in comparisons to other conventional methods.The proposed enhanced optimization technique achieved 98.75%, 98.96% for Dice and Jaccard coefficient.The model trained using the suggested measure outperforms those trained using the conventional method in the segmentation of skin cancer picture data.
Efficient Expressive Attribute-Based Encryption with Keyword Search over Prime-Order Groups

Attribute-based encryption with keyword search (ABEKS) is a novel cryptographic paradigm that can be used to implement fine-grained access control and retrieve ciphertexts without disclosing the sensitive information.It is a perfect combination of attribute-based encryption (ABE) and public key encryption with keyword search (PEKS).Nevertheless, most of the existing ABEKS schemes have limited search capabilities and only support single or simple conjunctive keyword search.Due to the weak search capability and inaccurate search results, it is difficult to apply these schemes to practical applications.In this paper, an efficient expressive ABEKS (EABEKS) scheme supporting unbounded keyword universe over prime-order groups is designed, which supplies the expressive keyword search function supporting the logical connectives of "AND" and "OR".The proposed scheme not only leads to low computation and communication costs, but also supports unbounded keyword universe.In the standard model, the scheme is proven to be secure under the chosen keyword attack and the chosen plaintext attack.The comparison analysis and experimental results show that it has better performance than the existing EABEKS schemes in the storage, computation and communication costs.
Diagnostic accuracy and agreement between inter- and intra-observers in the absence of clinical history for liquid-based preparation of gynecology specimens

The clinical history in cytology is the best source of information to ensure the accuracy of diagnosis, facilitating a slide observer to interpret and relate their findings in screening gynecology slides.This study aims to evaluate the performance of slide observers to screen-blinded gynecology slides without providing any information on clinical history.A correlational study design was conducted at the cytology laboratory, Universiti Teknologi MARA Selangor, Puncak Alam Campus.Fity-seven liquid-based preparation slides from gynecology specimens were screened blindly by five slide observers among Medical Laboratory Technology students who completed the enrollment of the cytology course.The inter- and intra-observer reliability testing was measured using the kappa value of Fleiss' and Cohen's kappa value, respectively, while the diagnostic accuracy without a clinical history was determined by the receiver operating characteristic (ROC) curve.The value of Fleiss' kappa (κ) was 0.221-this represents a fair strength of agreement between inter-observers. An intra-observer reliability test for each slide observer was analyzed using Cohen's kappa statistic and revealed that the kappa value varied between 0.116 and 0.696, indicating slight-to-substantial agreement between intra-observers. Additionally, the sensitivity value of 94.28%, specificity value of 72.40%, a positive predictive value (PPV) of 37.28%, a negative predictive value (NPV) of 72.40%, a likelihood ratio of 14.43, and the diagnostic accuracy of 75.09% were recorded.In conclusion, the students (slide observers) from the Centre of Medical Laboratory Technology Studies who took part in this study were able to interpret, classify, and diagnose the LBP gynecologic cytopathological cases into several categories (NILM and ECA) based on the 2001 Bethesda System reporting guideline.
Face Mask and Social Distance Monitoring via Computer Vision and Deployable System Architecture

The coronavirus (COVID-19) is a lethal virus causing a rapidly infectious disease throughout the globe.Spreading awareness, taking preventive measures, imposing strict restrictions on public gatherings, wearing facial masks, and maintaining safe social distancing have become crucial factors in keeping the virus at bay.Even though the world has spent a whole year preventing and curing the disease caused by the COVID-19 virus, the statistics show that the virus can cause an outbreak at any time on a large scale if thorough preventive measures are not maintained accordingly.To fight the spread of this virus, technologically developed systems have become very useful.However, the implementation of an automatic, robust, continuous, and lightweight monitoring system that can be efficiently deployed on an embedded device still has not become prevalent in the mass community.This paper aims to develop an automatic system to simultaneously detect social distance and face mask violation in real-time that has been deployed in an embedded system.A modified version of a convolutional neural network, the ResNet50 model, has been utilized to identify masked faces in people.You Only Look Once (YOLOv3) approach is applied for object detection and the DeepSORT technique is used to measure the social distance.The efficiency of the proposed model is tested on real-time video sequences taken from a video streaming source from an embedded system, Jetson Nano edge computing device, and smartphones, Android and iOS applications.Empirical results show that the implemented model can efficiently detect facial masks and social distance violations with acceptable accuracy and precision scores.
Obstacle Avoidance Path Planning Algorithm of an Embedded Robot Based on Machine Vision

Due to the lack of accurate modeling information in environment modeling, the traditional path planning algorithm for robot obstacle avoidance is of low accuracy. Therefore, this paper designs an obstacle avoidance path planning algorithm for embedded robot based on machine vision. First, the method of target edge detection is optimized in this paper. The edge detection results are obtained by color space transformation, and the complete target is obtained by edge fusion combined with surrounding pixel attributes. Then, the distance of the obstacle is measured by binocular depth ranging, and the longitudinal positioning of the robot is obtained, and the position of the obstacle is further obtained. Finally, a fuzzy control method for obstacle avoidance path planning is designed to obtain a complete planning scheme. The performance test results of the obstacle avoidance path algorithm show that the obstacle avoidance path planning scheme obtained by the algorithm designed in this paper has better performance in different obstacle avoidance test environments and can successfully avoid obstacles when the robot runs at high speed.
Analisis Kelayakan Buku Teks Bahasa Indonesia untuk Siswa Kelas VIII Sekolah Menengah Pertama

The purpose of this study was to determine and describe the quality/feasibility of the textbook “Mahir Berbahasa Indonesia” published by Erlangga Publisher and “Bahasa Indonesia Kelas VIII SMP/MTs Edisi Kurikulum 2013 Revisi” published by Yudhistira Publisher. It is a content analysis research that describes objectively, systematically, and clearly communicates from the VIII Indonesian Language and Literature Book. Research technique to objectively, systematically and quantitatively describe clear communication content. Based on the analysis and findings of the feasibility of books using the BNSP standard, the book “Mahir Berbahasa Indonesia” for class VIII SMP/MTs published by Erlangga Publisher is considered feasible with a percentage of 75%. In comparison, the book “Bahasa Indonesia Kelas VIII SMP/MTs Edisi Kurikulum 2013 Revisi” issued by Yudhistira Publisher is considered feasible with a percentage of 71.60%.
Study of desertification in the East of Iraq in (2013-2020) by supervised maximum likelihood

This study is concerned with the recent changes that occurred in the years (2013-2020) in the eastern region of Traq as a result of changes in the global climate, and the study included all water bodies in the three governorates that are located in the regions of eastern Traq (Diyala, Wasit and Maysan). Landsat 8 satellite scenes are used to create a mosaic covering the three provinces in a timeframe with the slightest difference between the dates the scene was captured, not exceeding 8 days. The results of calculating the changes in water areas were obtained by using the vector machine supporting the classifier. High accuracy ratios were recorded in the classification of the study area for a period of eight years. The study showed that the year 2020 represents a turning point in water bodies that were suffering from a period of drought, as the area of the water structure increased than it was in 2013, as well as a decrease in green spaces in 2020 than in 2013, due to the drought that occurred in the years between 2013 and 2020.
Human face detection in a crowd image based on template matching technique

In numerous security applications, face identification and recognition are astounding and significant capacities that we use in our daily lives. The primary purpose behind the interest in building up the computer vision-based robotized advances of mechanized face acknowledgment emerges from the genuine worries for public security in the present organized world. Where personality check's for physical and coherent access in numerous offices are basic in daily life.The process of identifying a human face image through a crowd is an important and very useful task for security purposes. Usually, digital cameras are erected on the roofs of dwellings, buildings, and storefronts to monitor events and accidents on the opposite streets. Often, the captured photos involve a group of people who are at that location. In the event of a strange accident, security services are required to identify the perpetrator. If these security services have pictures of suspects, then we can compare and match their photos with the captured image to identify the real perpetrator. Traditionally, It includes three processing steps in a crowd analysis.That is, preprocessing, object detection, and event/behavior recognitionIn this paper, we will try to recognize a person from a crowd of people by using a single image of the person to be recognized, using the Two-Dimensional Normalized cross-correlation function.
Binary Symmetric Polynomial-Based Protected Fair Secret Sharing and Secure Communication over Satellite Networks

The rapid establishment of the low Earth orbit (LEO) satellite network in orbit has promoted the development of satellite communication technology. However, with the reduction of access conditions of satellite networks, the problems of data protection and secure communication have attracted extensive attention. A secret sharing scheme is a cryptographic technology that can disperse risks and tolerate intrusion by dividing and storing secrets. Using secret sharing technology in satellite communication can realize information security and data confidentiality. However, if there are cheaters among the participants, existing secret sharing schemes cannot prevent cheaters from sharing secrets exclusively, even if they can detect attacks. For this reason, this paper proposes a satellite based on binary symmetric polynomials protected fair secret sharing and secure communication scheme. In satellite secret refactoring, this scheme can produce a shared session key between two participants, no other key agreement processes, and reduce the scheme in the shared secret and the actual communication satellite application complexity. Users use the session key to encrypt communication to improve security and resist external attacks. The safety and fairness of the scheme are proved against the four attack models. Compared with the existing schemes, the scheme has a lower cost of deception identification on the premise of satisfying security and fairness. This scheme does not require any cryptographic assumptions and is unconditionally secure.
Dynamic Model of Language Propagation in English Translation Based on Differential Equations

English is the most widely used language in the world, and at the same time, English translation is becoming increasingly important. However, the traditional English translation model still has some problems, such as poor translation effect, repeated translation, translation solidification, and translation limitations caused by regional language differences in the application process of language communication. Differential equations refer to relational expressions containing unknown functions and their derivatives. Solving differential equations is to find unknown functions. Differential equations are a branch of mathematics developed along with calculus. In order to solve the problem, this paper puts forward the use of differential equation thinking research English translation language propagation dynamic model, the differential equation combines the neural network language model (NNLM) to form the language dynamic propagation using the differential equation and NNLM model algorithm of the dynamic model for the actual translation effect test (the database includes more than 3,000 indexed, abstract journals and newspapers, including nearly 3,000 full-text journals. The database covers topics, such as international business, economics, economic management, finance, accounting, labor and personnel, and banking. It is suitable for professionals in economics, business administration, financial banking, labor, and personnel management, etc.), and analyze the average code length and the first word hit rate of general language model (GLM), NNLM model, and domain model in the dynamic model in different fields. Then its statistics the overall recall rate and accuracy rate of the dynamic model. The results show that the average code length of the NNLM model is relatively shorter at 2.2 bits. The first word hit rate is higher than the other two models. The first word hit rate of the NNLM model is 85% under non-professional phrases, while 90% under professional phrases. The overall accuracy of the English translation of the dynamic model of language propagation in this paper is 86.37%, and the recall rate is 79.67. It shows that the dynamic model of language propagation combined with neural network and differential equation has a good translation effect and is feasible.