Reviewerly/exHarmony huggingface dataset:
A Novel Teaching-Learning-Based Optimization with Laplace Distribution and Experience Exchange

Teaching-Learning-Based Optimization (TLBO) algorithm is an evolutionary powerful algorithm that has better global searching capability. However, in the later period of evolution of the TLBO algorithm, the diversity of learners will be degraded with the increasing iteration of evolution and the smaller scope of solutions, which lead to a trap in local optima and premature convergence. This paper presents an improved version of the TLBO algorithm based on Laplace distribution and Experience exchange strategy (LETLBO). It uses Laplace distribution to expand exploration space. A new experience exchange strategy is applied to make good use of experience information to identify more promising solutions to make the algorithm converge faster. The experimental performances verify that the LETLBO algorithm enhances the solution accuracy and quality compared to original TLBO and various versions of TLBO and is very competitive with respect to other very popular and powerful evolutionary algorithms. Finally, the LETLBO algorithm is also applied to parameter estimation of chaotic systems, and the promising results show the applicability of the LETLBO algorithm for problem-solving.
Byzantine-Resilient Decentralized Collaborative Learning

Decentralized learning techniques have been increasingly popular for model training on distributed worker nodes. Unfortunately, such learning systems are vulnerable to failures and attacks. In this paper, we consider the decentralized learning problem over communication networks, in which worker nodes collaboratively train a machine learning model by exchanging model parameters with neighbors, but a fraction of nodes are corrupted by a Byzantine attacker and could conduct malicious attacks. Our key idea for mitigating Byzantine attacks is to check the direction and magnitude of the cross-update vectors (the difference between each received model and local model from the previous round) at each consensus round. We propose a similarity-based reweighting scheme to obtain a robust local model update for each worker. Our proposed method does not need to know the exact number of Byzantine nodes and can be employed in both static and time-varying networks. We evaluate our method on the Fashion-MNIST dataset with different Byzantine attacks and system sizes. Numerical results demonstrate the robustness of our proposed method against Byzantine attacks and superior performance than existing methods.
Autoregressive Variational Autoencoder with a Hidden Semi-Markov Model-Based Structured Attention for Speech Synthesis

This paper proposes an autoregressive speech synthesis model based on the variational autoencoder incorporating latent sequence representation for acoustic and linguistic features and the structure of a hidden semi-Markov model (HSMM). Although autoregressive models can provide efficient and accurate modeling of acoustic features, they have exposure bias, i.e., the mismatch between training (teacher-forcing) and inference (free-running). To overcome this problem, we introduce an autoregressive latent variable sequence, rather than using autoregressive generation of observations. Latent representation of alignment using HSMM-based structured attention mechanism enables the use of a completely consistent training algorithm for acoustic modeling with explicit duration models. Experimental results indicate that the proposed model outperformed baselines in subjective naturalness.
Coneface: Approximate Pairwise Loss for Face Recognition

The discriminability of learned face features is the key to a successful face recognition algorithm under the open-set protocol. Recent research exploits well-designed loss functions to penalize the angles between the deep features and their class centers for reaching the purpose of minimizing the intra-class variance and achieves a significant increase in recognition accuracy. In this paper, we proposed an approximate pairwise loss (APL) to encourage inter-class separability as well as intra-class compactness. More specifically, we use cones to approximate the location of hard examples in the feature space which replaces the thorny hard-mining step, and the APL is obtained by calculating the angular distance between the features of training samples and the cone and we named our method ConeFace. Moreover, ConeFace can be easily used together with other SOTA methods to improve the performance with negligible computational overhead. Extensive experiments on Labeled Face in the Wild (LFW), Celebrities in Frontal-Profile in the Wild (CFP), AgeDB-30, and MegaFace datasets show the effectiveness of the proposed ConeFace.
The Construction of an Intelligent Evaluation and Analysis System for the Learning Environment and Teaching Status under the Background of Big Data

Based on the background of big data, the article analyzes the intelligent analysis system of the smart learning environment based on the characteristics of the current learning environment and teaching status, and designs the overall structure of the smart learning environment in line with the development of the times, and from the two aspects of learning environment and teaching Dimension designed a research framework and compiled a questionnaire, covering multiple aspects such as resource construction, media equipment and policy support, the use of information technology, and the use of teaching strategies. Implemented intelligent assessment of 334 middle school teachers. Based on the data results, the current situation and existing problems of the current smart learning environment and teaching are analyzed, and the preparation rate is as high as 97.2%.
Technology Utilization of Quipper School in Children's Literature Learning during COVID-19 Pandemic Era to Increase Reading Interest in Elementary School

One of the basic problems experienced by teachers, parents, and students due to the impact of COVID-19 is the adaptation of learning methods for students which requires the use of technology as a learning medium. This study aims to describe the use of Quipper School in learning children's literature in the era of the COVID-19 pandemic to increase the reading interest of elementary school students. The method used in this study is a qualitative method, this method examines a group of people, a set of conditions or a class of events which is then analyzed to interpret the data obtained by solving problems and a relevant final phenomenon. This research will provide an overview of the problems or phenomena that occur in the Laboratory Elementary School, State University of Malang. The informants in this study were 47 students. The data collection techniques were carried out by means of observation, interviews and documentation. The findings obtained in this study are empirical data regarding the use of Quipper School in learning children's literature in the era of the COVID-19 pandemic to increase interest in reading. The use of Quipper School has a positive impact on increasing students' interest in reading. The increase in students' reading interest is shown by understanding the content of the text in each material presented, better independent reading activities, critical thinking skills in responding to the content of the text.
Spammer Detection in Social Networks using ML and NLP

Spammers have turned some of the most popular places for people to communicate with each other into a distribution platform for shoddy and perhaps harmful information. Many individuals, all around the world, make use of online services for connecting with others in real time. To provide just one example, Facebook has grown to become one of the most widely used platforms ever, allowing an unacceptably large amount of spam to be transmitted from the site. There has been an increase in the potential for disseminating dangerous information to customers via the use of fictitious identities. As a result, the identification of spammers and fake Twitter clients has recently appeared as an unmistakable research problem in modern online interpersonal organizations (OSNs). On the web-based media platform Twitter, spammers may be identified using a variety of techniques. A scientific classification of Twitter spam location strategies is also presented, which divides the strategies into four categories based on their ability to distinguish I counterfeit material, (ii) spam dependent on URL, (iii) spam in hot subjects, and (iv) fake clients on the person-to-person communication site. Researchers seeking for the aspects of continuous advancements in Facebook spam detection may benefit from this investigation.
Comparative Analysis of Transformer Models on WikiHow Dataset

Because of the ever-increasing amount of information around us, it has become highly essential to consume it in the most efficient way as possible. Text Summarization is a way of generating a summary for a long piece of text while keeping the context and the meaning conveyed intact. Unlike Extractive Text Summarization, Abstractive Text Summarization is the process in which the summary is generated in a more humanlike way, without directly selecting a few sentences from the original piece of text itself. This research study has conducted a comparative analysis of T5 and PEGASUS, which are state-of-the-art abstractive text summarizers. The proposed study has used WikiHow Dataset for the comparison because its summaries are evenly extracted. The evaluation metrics used are BLEU and ROUGE. Unlike other researches doing comparisons, our novelty is in human evaluation for a subset of the summaries to see how reliable the evaluation metrics really are for abstractive summarization and in the use of the WikHow Dataset.
High-Performance Computational Recognition of Communication Signals Based on Bispectral Quadratic Feature Model

In response to the problems in the signal identification of radiation sources during the communication process, the bispectral quadratic feature model is applied to the identification algorithm for communication signals. According to the signal eigenvalues obtained from the bispectrum of the diagonal slices in the radiation source signals, the eigenvalues of the bispectrum diagonal slices can be extended from the frequency domain to the complex plane through the chirp-z operation in this paper, and the relevant data are obtained based on the bispectrum quadratic feature model of the signals by using the separation rules corresponding to the extended Babbitt distance. The bispectral quadratic feature model method is used to establish a sparse observation model, and the communication signal processing problem can be transformed into an estimation problem of signal motion parameters through the construction of a parametric database. At the same time, the high-resolution distance of communication signals is tested, and the communication signals are estimated by using the variational inference method. Finally, practical cases are analyzed, and the results indicate that the algorithm proposed in this paper can be used to identify different types of communication signals in accordance with simulated and measured data in the processing of communication signals in various environments, which has the certain anti-interference capacity to noise, can improve the identification rate of communication signals, and has verified the effectiveness and practicality of the algorithm proposed in this paper.
Comparison of Different Machine Learning Algorithms for Sentiment Analysis

There is an exponential growth in textual content generation every day in today's world. In-app messaging such as Telegram and WhatsApp, social media websites such as Instagram & Facebook, Google searches, news publishing websites, and a variety of additional sources are the possible suppliers. Every instant, all of these sources produce massive amounts of text data. Because of the large amounts of text data, Natural Language Processing (NLP) turns out to be an important tool for interpreting the content. NLP which is a popular subtask of sentiment analysis is the topic of this research. Sentiment Analysis (SA) is a type of textual data mining that discovers and extracts subjective information. It has proven to be an excellent asset for individuals to obtain important data and for organizations to analyze the social outlook of their product, brand or service by observing electronic discussions. The study carried out in this paper primarily focuses on the implementation and performance analysis of various machine learning classification models. The experiment results show that Support Vector Machine (SVM) classifier resulted in the maximum accuracy of 82% for the provided dataset.
Accurate Credit Card Fraudulent Dataset using Logistics Regression Compared with Random Forest

Using the Logistic Regression technique, it is possible to forecast the accuracy % of credit card fraudulent transactions. For forecasting the accuracy percentage of credit card fraudulent transactions, Logistic Regression with sample size=100 and Random forest(RF) with sample size=100 were iterated with 95 percent confidence interval and pretest power of 80 percent at various periods. Because the sigmoid function used in logistic regression maps the value between 0 and 1, it aids in the prediction of accuracy % by improving the accuracy percentage prediction. When compared to the accuracy of Random forest, the accuracy of Logistic regression is much higher (98.2 percent) (92.4). A statistically significant difference existed between Logistic regression and RF, with (p=0.000) (p0.005) indicating a statistically significant difference. The Logistic Regression method aids in the prediction of credit card fraudulent transactions with more accuracy than other algorithms.
Implementation of Personality Detection and Accuracy Prediction for identification of fake and true news using Decision Tree and Random Forest Algorithms

This project aims to innovatively classify and evaluate constructive proactive personality data in order to predict fake and true news on social media. There are 25000 entities and five attributes in the proactive personality knowledge dataset that was used for training and testing. By using the Decision Tree (DT) and Random forest (RF) algorithms, a methodology for predicting fake news and true news in the proactive personality was developed. The investigation of classification model comprises DT sieves fake and true news with an accuracy of 96% and the RF classifier predicted the fake news at an accuracy of 93%. The significance value is (P<0.05). The experimental results demonstrated the capability of DT and RF classifier models based on their prediction of relevant information of input data. It is found that the DT model performs better compared to the RF model in terms of accuracy and precision.
Breast Cancer Prediction Based on Mammographic data by Hybrid Resnet and Decision Tree

Recently, Breast cancer is easily occurred in all women due to poor eating habits. Further, technological improvement also leads to the causes of breast cancer. Image processing-based solution is an effective solution for identifying breast cancer with a higher rate of accuracy. Compared to other algorithms deep learning based detection methods gives a promising solution in the medical field. In this work, the Deep Residual learning model is joined with a Decision Tree Machine Learning strategy for an efficient breast cancer forecast. Therefore proposed work is named as Resnet-Decision Tree (RDT) model. This RDT model is utilized for anticipating the likelihood of cancer on the server side. The RDT model is created and verified in terms of Recall, accuracy, accuracy, and specificity separately. The outcome showed that the HDL model has higher expectation accuracy than the conventional strategies
Blockchain-based, Decentralized Evidence Archive System using IPFS

In spite of growth in technology, Indian Judiciary system somehow lacks digitalization. In the court trials cases, every argument by the lawyers, evidence presentation, witness/suspect cross examination everything will be noted down by the stenographer and everyday hearings details will be printed at the end of every court sessions. Therefore, the details about particular case will be in physical files as well as in digital format and can be accessed whenever it is needed like in the situation of case reopening. Data integrity is important in the judiciary system; when it comes to court cases, evidence integrity must be protected because even little changes in the evidence can lead to false judgments, and historical data is crucial. Where historical data archiving is necessary, Blockchain technology is suited. In the modern era, Blockchain technology is regarded as more reliable technology than any other. Blockchain technology can be used in the justice system to provide privacy and integrity, as well as efficient auditability and traceability, for storing case records and evidences. This research study has proposed a novel method using InterPlanetary File System distributed data storage to store case details and evidences on top of the Ethereum Blockchain. The case details can be stored using text and image files. The Ethereum smart contract is used for storing hash value of data in the Blockchain. The storage and access of the data in InterPlanetary File System is studied and explained using an experimental setting.
Adaptive energy filtering method based on time-domain image sequences for high-accuracy spot target localization

High-accuracy spot target localization is an essential optical measurement technique in fields such as astronomy and biophysics. Random noise generated during the imaging process limits further improvement of centroiding accuracy. Research for centroiding methods can no longer meet the demand for higher accuracy. This limitation is even more severe for low signal to noise ratio (SNR) imaging measurements. This paper proposes an energy filtering method based on time-domain extended image sequences, which is a typical application such as a star tracker. The energy variations of the spot in continuous sequences are analyzed, and the energy is filtered at pixel level. The filtered pixel response that is closer to real energy is involved in the calculation of the centroid. Adaptive variations of filter parameters for different energy distributions are also realized. Both simulations and laboratory experiments are designed to verify the effectiveness of the approach. The results show that this method can effectively and adaptively filter the spot energy at pixel level and further improve centroiding accuracy.
Password Guessing Based on GAN with Gumbel-Softmax

Password guessing is an important issue in user security and privacy protection. Using generative adversarial network (GAN) to guess passwords is a new strategy emerging in recent years, which exploits the discriminator’s evaluation of passwords to guide the update of the generator so that password guessing sets can be produced. However, the sampling process of discrete data from a categorical distribution is not differentiable so that backpropagation does not work well. In this paper, we propose a novel password guessing model named G-Pass, which consists of two main components. The first is a new network structure, which modifies the generator from the convolutional neural network (CNN) to long short-term memory- (LSTM-) based network and employs multiple convolutional layers in the discriminator to provide more informative signals for generator updating. The second is Gumbel-Softmax with temperature control for training GAN on passwords. Experimental results show the proposed G-Pass outperforms PassGAN in password quality and cracking rate. Moreover, by dynamically adjusting one parameter during the training process, a trade-off between sample diversity and quality can be achieved with our proposed model.
Hidden Interfaces for Ambient Computing: Enabling Interaction in Everyday Materials through High-brightness Visuals on Low-cost Matrix Displays

Consumer electronics are increasingly using everyday materials to blend into home environments, often using LEDs or symbol displays under textile meshes. Our surveys (n=1499 and n=1501) show interest in interactive graphical displays for hidden interfaces — however, covering such displays significantly limits brightness, material possibilities and legibility.
Stego Dog: Image Steganography Tool for Confidentiality and Integrity

With the fast-growing digital world in the pandemic scenario, there is a concern for data security when it crosses through various unbarred communication media. The data, the most useful way of communication despite being vulnerable to security breaches. Although there are a lot of other techniques suggested to keep this type of data in various ways, the potential for exploitation remains constant. Steganography, where the user embeds his secret message in a file so that ownership of intellectual property can be claimed to ensure the integrity of the content. The article discusses building a flutter application for cell phones that implement image steganography that follows an RGB algorithm. The algorithm highlights a new symmetric encryption methodology where in complex determinant calculations and random matrix development is used for key generation. The application codes the desired message using the RGB algorithm into the specified image from the gallery after clicking on the encode button displayed on the screen. After communication, the same application can be used to decode the message from the picture; if the picture contains a secret message that is coded using the same algorithm used for decoding methods, hence ensuring confidentiality and integrity.
Anomaly Detection via Context and Local Feature Matching

Unsupervised anomaly detection in medical imaging is an exciting prospect due to the option of training only on healthy data, without the need for expensive segmentation annotations of many possible variations of outliers. Most current methods rely on image reconstruction error to produce anomaly scores, which favors detection of intensity outliers. We instead propose a discriminative method based on a deep learning self-supervised pixel-level classification task. We model context and local image feature information separately and set up a pixel-level classification task to discriminate between positive (matching) and negative (mismatching) context and local feature pairs. Negative matches are created using data transformations and context/local shuffling. At test-time, the model then perceives local regions containing anomalies to be negative matches. We evaluate our method on a surrogate task of tumor segmentation in brain MRI data and show significant performance improvements over baselines.