Reviewerly/exHarmony huggingface dataset:
A Topic Recognition Method of News Text Based on Word Embedding Enhancement

Topic recognition technology has been commonly applied to identify different categories of news topics from the vast amount of web information, which has a wide application prospect in the field of online public opinion monitoring, news recommendation, and so on. However, it is very challenging to effectively utilize key feature information such as syntax and semantics in the text to improve topic recognition accuracy. Some researchers proposed to combine the topic model with the word embedding model, whose results had shown that this approach could enrich text representation and benefit natural language processing downstream tasks. However, for the topic recognition problem of news texts, there is currently no standard way of combining topic model and word embedding model. Besides, some existing similar approaches were more complex and did not consider the fusion between topic distribution of different granularity and word embedding information. Therefore, this paper proposes a novel text representation method based on word embedding enhancement and further forms a full-process topic recognition framework for news text. In contrast to traditional topic recognition methods, this framework is designed to use the probabilistic topic model LDA, the word embedding models Word2vec and Glove to fully extract and integrate the topic distribution, semantic knowledge, and syntactic relationship of the text, and then use popular classifiers to automatically recognize the topic categories of news based on the obtained text representation vectors. As a result, the proposed framework can take advantage of the relationship between document and topic and the context information, which improves the expressive ability and reduces the dimensionality. Based on the two benchmark datasets of 20NewsGroup and BBC News, the experimental results verify the effectiveness and superiority of the proposed method based on word embedding enhancement for the news topic recognition problem.
Analysis of Spam Detection Using Integration of Logistic Regression and PSO Algorithm

Content-based text classification system can automatically categories the text document into predefined limited classes. But the e-mail document classification is a challenging process in the modern internet environment. The e-mail documents are lightly signified in a great dimensional features space, creating a learning process, and the generalization (abstraction) process is problematic. Spam is unsolicited mail, the bulk of spam mails sent by the spammers who use vast e-mail programs to cover their characteristics and send the spam mails every day with no money. The spam mail directs various effects, including exposing unwanted images, decreasing company productivity, blocking Internet Service Providers&#x0027; (ISP) networks, etc. Additionally, the spam mail contains an infection, which is gotten ready for some fake movement. Newline PSO is mighty in taking care of multivariable issues, where factors take on genuine qualities that are taken as new lines an independent method to arrange the datasets and contemplate the PSO-based Classifier for Multiclass Data Sets. Spam has become the foundation of decisions utilized by digital crooks to spread vindictive payloads, for example, infections and Trojans. Community-oriented spam recognition methods can manage the enormous scope of electronic communication information subsidized through numerous causes, and special issues needing exposure to electronic communication information. Distance-protecting hash standard arrangements utilized to save the security of electronic communication information while empowering mails orders to detect spam recognition. PSO, a Big Data security safeguarding synergistic spam identification stage based on top of a standard Map Reduce office. Without PSO feature selection, the training accuracy would be less. With the help of the best dataset fit, the classification result will be high.
Use of Bayesian networks in forensic soil casework

Forensic soil comparisons can be of high evidential value in a forensic case, but become complex when multiple methods and factors are considered. Bayesian networks are well suited to support forensic practitioners in complex casework. This study discusses the structure of a Bayesian network, elaborates on the in- and output data and evaluates two examples, one using source level propositions and one using activity level propositions. These examples can be applied as a template to construct a case specific network and can be used to assess sensitivity of the target output to different factors and identify avenues for research.
Computer-free computational imaging: optical computing for seeing through random media

Diffractive Deep Neural Network enables computer-free, all-optical "computational imaging" for seeing through unknown random diffusers at the speed of light.
Interval Prediction Method for Solar Radiation Based on Kernel Density Estimation and Machine Learning

Precise global solar radiation (GSR) data are indispensable to the design, planning, operation, and management of solar radiation utilization equipment. Some examples prove that the uncertainty of the prediction of solar radiation provides more value than deterministic ones in the management of power systems. This study appraises the potential of random forest (RF), V-support vector regression (V-SVR), and a resilient backpropagation artiﬁcial neural network (Rprop-ANN) for daily global solar radiation (DGSR) point prediction from average relative humidity (RHU), daily average temperature (AT), and daily sunshine duration (SD). To acquire more accurate predictions of DGSR and examine the inﬂuence of historical DGSR on the performance of point prediction models, two diﬀerent model inputs are considered: (1) three meteorological variables and (2) the lags of DGSR and three meteorological variables. Then, two interval prediction methods are developed by introducing the KDE to out-of-bag (OOB), introducing kernel density estimation (KDE) to split conformal (SC) based on the three machine learning models. The two methods for interval prediction are denoted as OOB-KDE and SC-KDE. The mean absolute error (MAE), mean relative error (MRE), and Kendall rank correlation (Kendall) are used to assess the point prediction models. The performance of interval prediction methods is evaluated by the prediction interval coverage probability (PICP), prediction interval normalized average width (PINAW), and coverage width criteria (CWC). The following conclusions are drawn from this study. First, the V-SVR model performs best with the lowest mean absolute error (MAE) of 0.016 and mean relative error (MRE) of 0.001. Second, the lags of DGSR improve the prediction accuracy by about 30%. Third, the OOB-KDE and SC-KDE methods improved the quality of the prediction interval (PI). OOB-KDE improved CWC by 81%, and SC-KDE improved CWC by 99.99%. Fourth, the best interval prediction result is obtained using the SC-KDE method using the V-SVR model. The average diﬀerence between its PICP and prediction interval nominal coverage (PINC) is only 3% of the PINC, and its PINAW is less than 0.007.
Investigating Cyber Security Awareness Among Preservice Teachers During the COVID-19 Pandemic

South African institutions of higher education suffered serious disruptions during the COVID-19 pandemic which, resulted in migrating most teaching and learning activities to various online platforms, of which many depended on the open web. This has the potential to expose lecturers and students to cyber security threats and risks. As such cyber security awareness (CSA) becomes important. This study investigated the CSA among preservice teachers pursuing a Bachelor of Education studies in Further Education and Training (FET) at a university in Cape Town, South Africa. The purpose of the study was to gain an insight into CSA among preservice teachers who had been using digital technologies to support learning during the COVID-19 pandemic. An electronic questionnaire was administered to a random sample of 300 preservice teachers. The findings show that preservice teachers were limited in their awareness of cyber security threats and risks likely to affect their use of various digital technologies for remote learning. Furthermore, preservice teachers implemented basic strategies to mitigate basic cyber security threats and attacks. These basic strategies were found not to be sufficient for advanced attacks. The study concluded that lack of proper CSA and knowledge among preservice teachers presented them with challenges in solving threat attacks associated with denial-of-service (DoS), data theft and phishing when using personal digital devices.
Algorithmic Fairness in Face Morphing Attack Detection

Face morphing attacks can compromise Face Recognition System (FRS) by exploiting their vulnerability. Face Morphing Attack Detection (MAD) techniques have been developed in recent past to deter such attacks and mitigate risks from morphing attacks. MAD algorithms, as any other algorithms should treat the images of subjects from different ethnic origins in an equal manner and provide non-discriminatory results. While the promising MAD algorithms are tested for robustness, there is no study comprehensively bench-marking their behaviour against various ethnicities. In this paper, we study and present a comprehensive analysis of algorithmic fairness of the existing Single image-based Morph Attack Detection (S-MAD) algorithms. We attempt to better understand the influence of ethnic bias on MAD algorithms and to this extent, we study the performance of MAD algorithms on a newly created dataset consisting of four different ethnic groups. With Extensive experiments using six different S-MAD techniques, we first present benchmark of detection performance and then measure the quantitative value of the algorithmic fairness for each of them using Fairness Discrepancy Rate (FDR). The results indicate the lack of fairness on all six different SMAD methods when trained and tested on different ethnic groups suggesting the need for reliable MAD approaches to mitigate the algorithmic bias.
Low Resolution Face Recognition System Based on ESRGAN

Low-resolution face recognition is one of the research hotspots of face recognition today. It can be widely used in face recognition in various scenarios, such as identity verification at stations and classroom check-in. The prior art has achieved better performance in ideal scenarios, but detecting low-resolution images will make it difficult to recognize low-resolution human faces, and the accuracy will eventually decrease. This is why improving the accuracy of low-resolution face recognition (LRFR) is still challenging. We have finished the reasearch aim to solve the problem about LRFR. The super-resolution GAN (SRGAN) and enhanced super-resolution GAN (ESRGAN) used in this search. Comparing these methods, we finally obtain a model that can solve the low-precision problem of LRFR. Our system uses super-resolution reconstruction as the preprocessing step of the LRFR problem, and then uses Facenet to recognize the image. These data sets are Wild Face Tag (LFW), YouTube Face Database (YTF), and Wider Face Dataset. The experimental results show that the accuracy of the ESRGAN based on Facenet of the proposed system in the unconstrained natural environment is as high as 98.78%. At the same time, increase the number and speed of face detection, effectively realize the function of multiple face recognition, has practical application value and system robustness.
ChatBots and Business Strategy

New technologies emerge every day, and existing ones are improving. Due to this improvement, chatbots with artificial intelligence appeared. A chatbot is a robot (bot), built through rules defined by a conversational flow and/or Artificial Intelligence, which meets a certain demand of the person who is chatting with it and delivers pre-defined responses. Chatting with a chatbot allows you to solve most customer requests without the intervention of a human, and applications can rely on a powerful ally, artificial intelligence. There are chatbots with and without artificial intelligence; the adoption of one or the other depends on the business of each company. The greater the degree of difficulty in identifying its presence, the greater its efficiency. With artificial intelligence, chatbots can develop in a surprising way, going far beyond a customer service solution, and even becoming indispensable in business strategy. In this article, we will carry out a qualitative bibliometric analysis of works on this topic, with the objective of evaluating the impact of bots on business strategy.
Blockchain Based Smart and Secure Agricultural Monitoring System

Nowadays, smart applications are increasing day by day to improve the standard of agriculture in villages. A modern day agriculture is characterised by the presence of numerous smart Information and Communication Technology (ICT)-enabled services such as automated agriculture monitoring, data privacy, among others. Such agriculture monitoring employ multitudes of Internet of Things (IoT) devices to collect and share data between trusted users by means of a centralized intermediary for monitoring and control of the myriad automatic activities. However, a centralized intermediary is plagued by issues such as single point of failure, risk of data loss, man-in-the-middle attack and so forth. Blockchain based smart contracts for automated control in smart agriculture provides a decentralized and secure alternative. With the increasing population across the world, the demand for high-quality products which are fresh as well as healthy is increasing significantly and therefore in a very limited time, the farming and food production have to increase yield significantly. In the coming future, we need to produce double of what we are producing now. There is much research done in the field of smart farming but they don't tackle issues such as storing of data on a central server, complete details of the products from beginning to end which could satisfy a customer as well as stakeholders. In this paper, we presented a novel Blockchain based solutions for secure and smart monitoring of agriculture lands.
Blockchain Technology in Smart Agriculture Environment: A PLS-SEM

The agriculture industry is being transformed by disruptive technologies, which is resulting in a rise in the Smart agriculture environment. In developing economies, blockchain is required to progress the traditional agriculture system, just as it is in developed countries. The purpose of this study is to look at the major constructs that influences an individual decision to adopt blockchain for agriculture firms. The proposed model uses structural equation modelling and is based on Unified theory of acceptance and use of technology (UTAUT). All factors were shown to have significant impact during the blockchain adoption expect (social influence on behavioral intention), according to the findings from a study of 917 specialists working in the Pakistan agriculture firms. The study findings are used to examine the managerial consequences, albeit there are some limitations.
Different Types of Attacks on Block Ciphers

Cryptanalysis is a very important challenge that faces cryptographers. It has several types that should be well studied by cryptographers to be able to design cryptosystem more secure and able to resist any type of attacks. This paper introduces six types of attacks: Linear, Differential, Linear-Differential, Truncated differential Impossible differential attack and Algebraic attacks. In this paper, algebraic attack is used to formulate the substitution box(S-box) of a block cipher to system of nonlinear equations and solve this system by using a classical method called Grobner Bases. By Solving these equations, we made algebraic attack on S-box.
Developing Automated Optical Character Recognition System Using Machine Learning Algorithm to Solve Payment Verification Issues

One primary aspect in customer services is to provide immediate solution towards payment verification issues, such as a time delay of payment confirmation by the payment service provider or supplier. This paper presents a development of an accurate optical character recognition (OCR) system using convolutional neural network with deep learning algorithm, which can skip some steps in the workflow of manual payment approval to fasten the process of payment verification and confirmation. By using some machine learning frameworks of pyTorch utilizing Tensors and CUDA-GPU parallel computing, the machine learning based OCR system was developed and tested with the actual data. The real data sets used here cover the non-uniformity of the receipt bill&#x0027;s papers with various conditions (crumple, water drops, and folds) with some nature of the customer&#x0027;s overall camera noise, angle, and lighting. Several experiments associated with data preparation, deep learning parameter settings, and model performance comparison, were properly conducted to obtain a high quality of OCR system to detect trace number, approval codes, and nominals on the widely-used payment receipts. The resulting OCR system performed very satisfactory with 100&#x0025; accuracy on testing data set. This promising results permit for the integration between this accurate and automated OCR system and chat environment with chatbot technology in order to provide better user experience and immediate and reliable solution toward payment verification issues.
Emoji Prediction using LSTM and Naive Bayes

The emojis or emoticons are tiny graphics, generally used during texting through social media platforms. These express a combination of visuals and words simultaneously and provide a different method of interactions. Even after being used quite a significant number of times on social media, their meanings and importance haven&#x0027;t received enough focus and attention via the perspective of Natural Language Processing. In the described project, we perform the analysis of the relationship between emojis and English words and predict the respective emojis for some given text or tweet messages. We have performed multiple methods for word embedding, and our model trained on the Multinomial Naive Bayes and LSTMs. As per the conclusion of our project, the model has shown significant appropriate predictions of emojis on the tweets.
Detecting cyber security related Twitter accounts and different sub-groups

Many cyber security experts, organizations, and cyber criminals are active users on online social networks (OSNs). Therefore, detecting cyber security related accounts on OSNs and monitoring their activities can be very useful for different purposes such as cyber threat intelligence, detecting and preventing cyber attacks and online harms on OSNs, and evaluating the effectiveness of cyber security awareness activities on OSNs. In this paper, we report our work on developing several machine learning based classifiers for detecting cyber security related accounts on Twitter, including a base-line classifier for detecting cyber security related accounts in general, and three sub-classifiers for detecting three subsets of cyber security related accounts (individuals, hackers, and academia). To train and test the classifiers, we followed a more systemic approach (based on a cyber security taxonomy, real-time sampling of tweets, and crowdsourcing) to construct a dataset of cyber security related accounts with multiple tags assigned to each account. For each classifier, we considered a richer set of features than those used in past studies. Among five machine learning models tested, the Random Forest model achieved the best performance: 93% for the baseline classifier, 88-91% for the three sub-classifiers. We also studied feature reduction of the base-line classifier and showed that using just six features we can already achieve the same performance.
Developments in Open Data Norms

Open data has been transformative for the scientific and public understanding of the recent COVID-19 pandemic, bringing into sharp focus the clear benefits of increasing transparency and accountability within psychological research. Despite the knowledge that individual gatekeeping of data is antithetical to the goals of the scientific community, research norms across Psychology are changing too slowly. This editorial reviews the recent developments made in open data practices and norms, the upcoming demands of openness, and marks the changes made to the Journal of Open Psychology Data to further support progress towards a culture of transparency and collaboration.
Image Steganography using Least Significant Bit (LSB) - A Systematic Literature Review

Digital image steganography is used to hide confidential data within a cover image. In this context, least significant bit (LSB) is a well-known steganography approach. As LSB is frequently applied for image steganography, there is a strong need to explore and summarize the state-of-the-art LSB approaches for image steganography. Therefore, this article performs a Systematic Literature Review to identify 20 research studies available during 2016&#x2013;2020. This leads to the identification of 17 image steganography approaches / algorithms and 20 datasets for the evaluation of image steganography techniques. Furthermore, 3 leading frameworks / tools are presented. In addition to that, to evaluate the quality of the image steganography, 4 of the prime parameters which are frequently used have also been identified. It can be safely claimed that the image steganography quality is significantly improved through LSB via enhanced PSNR (Peak Signal-to-Noise Ratio) and MSE (Mean Square Error). However, the size and secrecy of secret data is the biggest challenge while applying LSB techniques.
Face Expression Classification using Squeeze-Excitation based VGG16 Network

Recognizing facial expression is always an imperative topic in machine learning. A common structure to implement the recognition is called VGG, which is composed of a certain number of CNN layers combined with max-pooling layers. An experiment was carried out to realize an improved image classification neural network based on VGG16. The input features were firstly squeezed to c dimension, then be weighted and excited by a softmax layer, finally be scaled to the original dimension. Compared to the ordinary VGG network, it could capture the features in the channel dimension, therefore improving the accuracy and the convergence speed. It would be constructive to use other image datasets with photos having different illumination conditions and clarity. Experiments on FER2013 dataset validate the effectiveness of our method.
Classification of Testable and Valuable User Stories by using Supervised Machine Learning Classifiers

Agile is one of the most widely used software development methodologies that include user stories, the smallest units semi-structured specifications to capture the requirements from a user's point of view. Despite being popular, only a little research has been done to automate the quality checking/analysis of a user story before assigning it to a sprint. In this study, we have chosen two metrics, i.e., Testable and Valuable criteria from INVEST checklist, and have applied supervised machine learning classifiers to automatically classify them. Since the industrial data collected for the research was unbalanced, we also applied data balancing techniques such as SMOTE, RUS, ROS, and Back translation (BT) to verify if they improved any classification metrics. Although we did not see any significant improvements in accuracy and precision for the classifiers after applying data balancing techniques, we noticed a significant improvement in recall values across all the classifiers. Our research provides some promising insights into how this research could be used in the software industry to automate the analysis of user stories and improve the quality of software produced.