Reviewerly/exHarmony huggingface dataset:
TegTok: Augmenting Text Generation via Task-specific and Open-world Knowledge

Generating natural and informative texts has been a long-standing problem in NLP.Much effort has been dedicated into incorporating pretrained language models (PLMs) with various open-world knowledge, such as knowledge graphs or wiki pages.However, their ability to access and manipulate the task-specific knowledge is still limited on downstream tasks, as this type of knowledge is usually not well covered in PLMs and is hard to acquire.To address the problem, we propose augmenting TExt Generation via Task-specific and Open-world Knowledge (TEGTOK) in a unified framework.Our model selects knowledge entries from two types of knowledge sources through dense retrieval and then injects them into the input encoding and output decoding stages respectively on the basis of PLMs.With the help of these two types of knowledge, our model can learn what and how to generate.Experiments on two text generation tasks of dialogue generation and question generation, and on two datasets show that our method achieves better performance than various baseline models.
Phonetic Features of the Use of the Letters ‘g’ and ‘j’ in Spanish

The confusion between the letters G, J is caused by the fact that they sometimes represent the same sound. When g or j are before the vowels A, O and U, then there is no problem because they represent different sounds. Both, foreigners and Spanish speakers are in this situation when writing, as the sounds are very similar, so it is recommended to use the rules and modern technologies to avoid spelling mistakes.
Formal Verification of a Keystore

This paper is an experience report concerning the verification of a component of our operating system using Isabelle. The component allows for the secure storage of cryptographic key material. We will discuss the method used, describe the connection we created between the component and a standard library, identify lessons learned (both for the verification itself as to the process followed to write and adapt the software to be verified), and discuss possible avenues for further research.
Pre-loaded Deep-Q Learning

This paper explores the potentiality of pre-loading deep-Q learning agents’ replay memory buffers with experiences generated by preceding agents, so as to bolster their initial performance. The research illustrates that this pre-loading of previously generated experience replays does indeed improve the initial performance of new agents, provided that an appropriate degree of ostensibly undesirable activity was expressed in the preceding agent’s behaviour.
Blockchain-Based Secure File Storage with Hybrid Cryptography and Machine Learning for Malware Detection

Storing your data on hosted servers is made possible through cloud storage. With each organization using the cloud to save their data, there is a significant risk of data misuse. For added security, there is an urgent need to safeguard user data. We are here to store information on the cloud, and our main goal is to make sure data confidentiality, integrity and availability are maintained. Instead of keeping data on our local server, we have developed a model that saves data on cloud-based servers. Encrypted data will be protected. Additionally, it will check for malware attack policy while exchanging data. Finally, it will feature two layers of protection. The first is an implementation of hybrid cryptography, and the second is to avoid malware-based attacks. Incorporating machine learning will find all of the various possible malware attack methods and look for ways to prevent similar attacks in the future.
Multimodal Information Fusion and Feedback-Enhanced Model Construction

Multimodal fusion technology aims to give full play to the advantages of different modes of learning data, and summarize more accurate information so as to realize the complementary advantages of data. Multimodal fusion technology is a major technological breakthrough in the development of learning feedback areas, which can combine data obtained from multiple examinations to provide accurate feedback for students. Based on this, this study puts forward relevant suggestions on how to improve students’ feedback literacy with the help of multimodal information fusion by the way of multimodal interactive teaching style. This study selected 95 participants and then developed a students’ feedback literacy modal, applying questionnaire and multivariate regression analysis in order to understand what factors influenced student feedback literacy and tends to explore effective deep learning mechanism. Results revealed that multimodal interactive teaching style has potential influence on students’ feedback literacy and positive feedback has tendency to improve students’ learning achievement.
Information Security Policies in Nigerian Institutions

Information systems have become part and parcel of today’s business operations, with competition on its rise. The security challenges related to information systems could have a severe impact on the overall business objectives of an institution if not handled at the right time. A well-meaning institution must take a full-blown step towards information security management to achieve information security. Information security management has been seen as any management system to address the security issues affecting an institution and align security needs to the overall business objectives. Conversely, Information security policy is the foundation upon which institutions base their entire information security management. This study looks at the uptake of information security policy among Nigerian institutions. The survey result created a clear picture of the uptake of information security policy among Nigerian institutions.
An Improved Method to Solve Data Sparsity Dilemma in Knowledge Recommendation System

As an effective way of knowledge sharing, knowledge recommendation can alleviate the knowledge overload problem and promote the value-added of knowledge. To solve the difficulty in determining the number of users' nearest-neighbors and the deviation of rating prediction, which caused by data sparsity in the collaborative filtering algorithm of knowledge recommendation systems, an improved method based on Random Forest - Spectral Clustering and SVD++-UCF is proposed. Firstly, the SVD++ algorithm is applied to fill the sparse matrix, which determines the range of clusters for the spectral clustering based on the predicted scores using the random forest algorithm; Secondly, based on the RF-SC method, an improved UCF algorithm is designed to predict the scores of the nearest-neighbors of knowledge subjects; Thirdly, the SVD++ algorithm and the improved UCF model are linearly fused into the SUCF method. Finally, based on the Movielens dataset, the experimental analysis shows that the new method can effectively improve the accuracy and interpretability of knowledge recommendation compared with other existing typical algorithms.
Augmented Reality Street Art (ARSA) Model to Revitalize Deserted Alley in Kota Bharu, Kelantan Towards Shared Prosperity Vision 2030: A Systematic Literature Review

Previous research has mentioned that lost places in urban areas, such as back alleys, are often hotbeds of crime and immoral acts. Most countries, including Malaysia, have chosen to use creative methods, such as placemaking, to regenerate deserted alleys and urban spaces to improve such locations. However, integrating interactive technology, such as Augmented Reality (AR), has not had enough study as a creative approach in placemaking projects to revitalize abandoned alleys or empty urban places. Therefore, a systematic literature review was conducted to evaluate the relationship among various street arts, using AR technology, in creative placemaking projects, to revitalize places for society’s well-being and to fulfill the government’s Shared Prosperity Vision 2030. Inclusion criteria added studies on street art, street art with augmented reality technology, and their relationship to placemaking. The excluded studies were unrelated methodology, samples, contexts, and findings. Eleven studies met the inclusion criteria using a systematic search strategy. Based on the review, a research model for revitalizing areas using street art projects integrated with AR technology was developed for society’s wellbeing and to fulfill two of the Key Economic Growth Activities (KEGA); namely, (KEGA 4) Content Industries and (KEGA 15) Tourism Malaysia, in the government’s Shared Prosperity Vision 2030 blueprint.
A Simulation Computing System Model of Natural Language Communication Based on Genetic Algorithm

With the widespread popularity of smart devices in people’s daily lives, people hope to communicate with devices through a more humane interactive way. The natural language communication simulation computing system can provide technical support for the interactivity of smart devices. Genetic algorithm (GA) is a novel intelligent algorithm that has been studied hot in recent years. Therefore, this article will start the design research of the natural language communication simulation calculation (NLCSC) system model based on GA. This paper combines the advantages of BP neural network and GA, and proposes an improved adaptive genetic algorithm. This article designs the NLCSC system model in detail, including the architecture of the input layer, convolutional layer, and pooling layer. This article compares with the calculation model of BP neural network by experiment, it is found that the average calculation error of BP algorithm is 1.46%, while the average calculation error of GA-BP calculation model is 0.243%. This data verifies that the GA-based NLCSC system model has a high calculation accuracy.
Indonesian Automatic Speech Recognition with Xlsr-53

This study focuses on the development of Indonesian Automatic Speech Recognition (ASR) using XLSR-53 pre-trained model. The use of this XLSR-53 pre-trained model is to significantly reduce the amount of training data in non-English languages required to achieve competitive WER. The total amount of data used in this study is 26 hours: (1) TITML-IDN 14,5 hours; (2) Magic Data 3,5 hours; and (3) Common Voice 8 hours. With a WER of 22 %, the model built in this study can compete with similar models using the Common Voice dataset split test. WER can be decreased by around 10% using a language model, resulting in a WER of 12 %. Thus, the results of this study have succeeded in perfecting previous research in contributing to the creation of a better Indonesian ASR with a smaller amount of data.
Complete Proofs for All the 7 Millennium Problems Using Physics

The Clay Mathematics Institute chose seven well-known mathematics problems for the Millennium Prize Problems in 2000. For the proper solution to any of the questions, the Clay Mathematics Institute has guaranteed a $1 million prize. In this paper, I will use several significant physical principles and theories to prove all seven Millennium Problems, as well as a decent amount of math. These proofs can be characterized as both non-mathematical and mathematical in nature since they employ theorems and ideas from fields other than mathematics as well as a significant amount of mathematics.
Proposing a New Fast and Robust Framework for Anonymizing IoT Data Streams Using Apache Flink

We present a novel framework that considers the expiration period of stream Internet of Things (IoT) data to anonymize it. Anonymity is one of the safeguards in place to protect data privacy. Traditional approaches cannot be used to anonymize IoT data because, unlike database data, it is not static and is very vast. The anonymization framework proposed in this paper performs its operation using a new clustering method and Apache Flink flow data processing engine. After primary clustering, if size of clusters is not proper, our review will continue to suppressing and deleting or anonymizing and publishing the data. The final remaining data will be merged and anonymized at the end. The proposed approach reduces data delay significantly. Also, it handles both numerical and categorical data. Furthermore, at the end, combining the leftover clusters avoids information loss. In comparison with similar methods, the evaluation results show better performance in terms of data delay and information loss.
Imbalanced Data Classification of Pathological Speech Using PCA, SMOTE, and Expectation Maximization

Imbalance can affect the classification. In this study, a new algorithm to classify the pathology by considering the imbalance between classes has been built. This algorithm used principal component analysis (PCA), synthetic minority oversampling technique (SMOTE), and cluster membership degrees. Both PCA and Expectation-Maximization algorithms are used to give new features combined with the existing features. This proposed method is associated with Support Vector Machine (SVM) and Naive Bayes (NB) for severity classification of UA speech and TORGO speech databases. Another point in this work revealed the importance of articulatory of the TORGO database. The evaluation of this method on the two databases showed the significant results where the increase for TORGO database articulatory features, auditory features, and their combination was respectively 8.03%, 16.68%, and 17.79% compared to SVM performance and 3.04%, 23.67%, and 13.5% compared to the NB performance. The increase for UA speech was 5.13% and 24.63% compared to SVM and NB performance respectively. Additionally, the proposed method has outperformed four well-known imbalanced classification algorithms.
A Hybrid Feature Ranking Algorithm for Assisted Reproductive Technology Outcome Prediction

In recent years, the emerging technology of machine learning has made vast strides in medicine.Machine learning-based clinical decision support systems assist doctors make efficient diagnoses and offer better prescriptions.Today, one of the greatest challenges for doctors worldwide is the treatment of infertility, with even the most sophisticated technology offering limited success.Currently, the Assisted Reproductive Technology (ART) in use is highly sophisticated technology that offers a success rate of 20%, depending on a slew of factors with complex relationships.With their capacity to analyze large and complex datasets, the application of machine learning techniques to predictions can maximize the ART success rate.This research work attempts a dynamic model for ART outcome prediction using incremental classifiernamed Ensemble of Heterogeneous Incremental Classifier (EHIC) in Machine Learning.In this paper,a new feature ranking algorithm named Voted Information Gain Attribute Rank Estimation Algorithm (VIGAREA) is proposed to enhance the performance of EHIC.The proposed VIGAREA is a combination of a number of feature selection methods and information gain ratio of each variable.It has the capability to rank the features based on its significance.The methodology and the way how the proposed VIGAREA is developed is presented.Experimental results proved that the EHIC with the proposed VIGAREA achieves the highest prediction with the ROC area of 95.5% for the ART dataset used for the research.The effectiveness of the proposed VIGAREA is checked with a range of miscellaneous feature selection methods and found that the proposed HIGHLIGHTS• Proposed a hybrid feature ranking algorithm named VIGAREA• The proposed feature ranking algorithm gives importance to the individual feature as well as interaction between the features.• The proposed VIGAREA can combine any number and type of feature selection methods along with information gain ratio Brazilian Archives of Biology and Technology.Vol.65: e22210605, 2022www.scielo.br/babtfeature ranking method VIGAREA performs optimally.Further, the performance of the proposed model is compared to that of existing models, and the findings show that the former outperforms the latter.
Vision-and-Language Pretraining for VQA

Multimodal (e.g., vision and language) pretraining has emerged as a popular topic, and many representation learning models have been proposed in recent years. In this chapter, we focus on the vision-and-language pretraining model, which can be adapted in the VQA task. To this end, we first introduce three general pretraining models—ELMo, GPT and BERTBERT—for which only the representation of natural language is considered in the original research. Subsequently, we describe the vision-and-language pretraining models, which can be regarded as extensions of the language-aware pretraining models. Specifically, we categorize these models into two types: single stream and two stream. Finally, we describe the method to finetune these models for each specific downstream task, e.g., VQA, visual common-sense reasoning (VCR) and referring expression comprehension (REC).
Editable Person Image Synthesis

We present a framework for synthesizing a person image in different target poses and changing the garment styles such as shape, color, and texture. This work presents a part-aware style generator for spatially correlated style propagation between the source and target images through part-aware style encoding and mapping modules. The first module extracts the style features using the source semantic map, and the second module constructs the style guides using the predicted target semantic map. Finally, the style guides are fed into the person image generator through the part-aware positional style transformation scheme. Our method can also take user-edited semantic maps to change the shape of garments, \eg from short to long pants. The experimental results demonstrate the characteristics of the proposed method and its superiority over the state-of-the-art methods.
Destructive Outcomes of Digitalization (Credit Card), a Machine Learning Analysis

As the phase of digitization reaches our day-to-day lives, things are easily available and accessible by the computer, which is quite easier and faster method for transactions. The pandemic also played a huge role in growth in credit card fraud activities. And, that has led to massive increase in credit card fraud dramatically. As a result, fraud detection should include surveillance of the person’s/spending customer’s attitude in order to determine, prevent, and detect unwanted behavior. For both online and in-person buying, credit cards are the most convenient way of payment. Fraud detection is agitated not only with capturing fraudulent activities, but also with discovering them as early as possible, since this kind of fraud costs millions of dollars of people. Machine learning algorithms have proven to be extremely useful in detecting fraud of smart cards. Because of the uneven nature of regular classification algorithms, they are ineffective in detecting credit card fraud. Isolation forest algorithm is been used in the proposed scheme, and the local outlier (Tripathi et al. in J Pure Appl Math 118:229–234, [1]) factor is used to recognize fraudulent transactions and their accuracy.
A Triplet-Loss Dilated Residual Network for High-Resolution Representation Learning in Image Retrieval

AbstractContent-based image retrieval is the process of retrieving a subset of images from an extensive image gallery based on visual contents, such as color, shape or spatial relations, and texture. In some applications, such as localization, image retrieval is employed as the initial step. In such cases, the accuracy of the top-retrieved images significantly affects the overall system accuracy. The current paper introduces a simple yet efficient image retrieval system with a fewer trainable parameters, which offers acceptable accuracy in top-retrieved images. The proposed method benefits from a dilated residual convolutional neural network with triplet loss. Experimental evaluations show that this model can extract richer information (i.e., high-resolution representations) by enlarging the receptive field, thus improving image retrieval accuracy without increasing the depth or complexity of the model. To enhance the extracted representations’ robustness, the current research obtains candidate regions of interest from each feature map and applies Generalized-Mean pooling to the regions. As the choice of triplets in a triplet-based network affects the model training, we employ a triplet online mining method. We test the performance of the proposed method under various configurations on two of the challenging image-retrieval datasets, namely Revisited Paris6k (RPar) and UKBench. The experimental results show an accuracy of 94.54 and 80.23 (mean precision at rank 10) in the RPar medium and hard modes and 3.86 (recall at rank 4) in the UKBench dataset, respectively.
Resume Classification Using Bidirectional LSTM and Attention Mechanism

Document classification using Deep Learning encompasses handling documents and sorting them on the basis of the subjects that they belong to. Due to the increasing growth in online recruitment, job portals receive a mammoth amount of resumes from candidates with different fields of expertise. This has rendered the traditional hiring methods inefficient and given rise to the necessity of an automatic resume classification system that efficiently directs applications to their corresponding occupational categories. For building the training and evaluation dataset, we have scraped data from websites to obtain resumes in readable text format to form a real-world dataset. Here, we implement a Word2Vec model to form the word embeddings and a bidirectional LSTM paired with a custom attention layer as our proposed model to carry out the resume classification task. We also compare our proposed model against other algorithms such as SVM, KNN, etc., to demonstrate its effectiveness.
Human Object Recognition Technology Based on Internet of Things in Pedestrian Wandering Detection of Traffic Hub

In this paper, human target recognition technology is used to detect pedestrian wandering state in traffic hub. In this paper, the current mainstream target recognition algorithm YOLOv4 is selected to lighten the model. By integrating multiple SSP modules to optimize the performance of the model, the lightweight Ghost convolution module combined with channel pruning limit compression method is used to compress and accelerate the model efficiently, so as to obtain a target recognition model with high precision, small volume and high efficiency, so it is suitable to be deployed on the embedded platform with limited resources, and the local features are collected by Cauchy model. The accuracy of this model is 92.5% and the time complexity is T(n). The local detection ability of the optimized Cauchy model is about 30% higher than that of the simple YOLO model. The frame rate of YOLOv4 used in this paper is 20–25 FPS, which can achieve smooth multi-target recognition in ordinary pedestrian detection.