Reviewerly/exHarmony huggingface dataset:
DRAG: Director-Generator Language Modelling Framework for Non-Parallel Author Stylized Rewriting

Author stylized rewriting is the task of rewriting an input text in a particular author's style.Recent works in this area have leveraged Transformer-based language models in a denoising autoencoder setup to generate author stylized text without relying on a parallel corpus of data.However, these approaches are limited by the lack of explicit control of target attributes and being entirely data-driven.In this paper, we propose a Director-Generator framework to rewrite content in the target author's style, specifically focusing on certain target attributes.We show that our proposed framework works well even with a limitedsized target author corpus.Our experiments on corpora consisting of relatively small-sized text authored by three distinct authors show significant improvements upon existing works to rewrite input texts in target author's style.Our quantitative and qualitative analyses further show that our model has better meaning retention and results in more fluent generations.
Evaluating the Probability of Successful Knapsack Ciphersystem Analysis with Genetic Algorithms

The genetic algorithms are a well-known family of high-performance probabilistic algorithms. In this paper, we explore the possibility of using the genetic algorithm for the Knapsack problem to compromise the security of a Knapsack cipher. Despite being much faster than the exact algorithms, the genetic algorithm for the Knapsack problem may fail to find a solution. We explore the connection between the success rate of the genetic algorithm and the Knapsack problem parameters: the Knapsack Density, the items count in the solution and whether the Knapsack is modular or multiplicative. As a result, we determine whether the genetic algorithm is viable as an analysis tool for the Knapsack ciphers with specific parameters.
Lifelong Knowledge-Enriched Social Event Representation Learning

The ability of humans to symbolically represent social events and situations is crucial for various interactions in everyday life.Several studies in cognitive psychology have established the role of mental state attributions in effectively representing variable aspects of these social events.In the past, NLP research on learning event representations often focuses on construing syntactic and semantic information from language.However, they fail to consider the importance of pragmatic aspects and the need to consistently update new social situational information without forgetting the accumulated experiences.In this work, we propose a representation learning framework to directly address these shortcomings by integrating social commonsense knowledge with recent advancements in the space of lifelong language learning.First, we investigate methods to incorporate pragmatic aspects into our social event embeddings by leveraging social commonsense knowledge.Next, we introduce continual learning strategies that allow for incremental consolidation of new knowledge while retaining and promoting efficient usage of prior knowledge.Experimental results on event similarity, reasoning, and paraphrase detection tasks prove the efficacy of our social event embeddings.
Age Estimation in Short Speech Utterances Based on Bidirectional Gated-Recurrent Neural Networks

Analysis (LDA) , Speaker age estimation, Statistical functional, VoxCeleb1 dataset
Extended Kalman Filter Using Orthogonal Polynomials

This paper reports a new extended Kalman filter where the underlying nonlinear functions are linearized using a Gaussian orthogonal basis of a weighted <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {L}_{2}$ </tex-math></inline-formula> space. As we are interested in computing the states’ mean and covariance with respect to Gaussian measure, it would be better to use a linearization, that is optimal with respect to the same measure. The resulting first-order polynomial coefficients are approximately calculated by evaluating the integrals using (i) third-order Taylor series expansion (ii) cubature rule of integration. Compared to direct integration-based filters, the proposed filter is far less susceptible to the accumulation of round-off errors leading to loss of positive definiteness. The proposed algorithms are applied to four nonlinear state estimation problems. We show that our proposed filter consistently outperforms the traditional extended Kalman filter and achieves a competitive accuracy to an integration-based square root filter, at a significantly reduced computing cost.
On Two-Stage Guessing

Stationary memoryless sources produce two correlated random sequences Xn and Yn. A guesser seeks to recover Xn in two stages, by first guessing Yn and then Xn. The contributions of this work are twofold: (1) We characterize the least achievable exponential growth rate (in n) of any positive ρ-th moment of the total number of guesses when Yn is obtained by applying a deterministic function f component-wise to Xn. We prove that, depending on f, the least exponential growth rate in the two-stage setup is lower than when guessing Xn directly. We further propose a simple Huffman code-based construction of a function f that is a viable candidate for the minimization of the least exponential growth rate in the two-stage guessing setup. (2) We characterize the least achievable exponential growth rate of the ρ-th moment of the total number of guesses required to recover Xn when Stage 1 need not end with a correct guess of Yn and without assumptions on the stationary memoryless sources producing Xn and Yn.
Myanmar Continuous Speech Recognition System Using Convolutional Neural Network

Translating the human speech signal into the text words is also known as Automatic Speech Recognition System (ASR) that is still many challenges in the processes of continuous speech recognition.Recognition System for Continuous speech develops with the four processes: segmentation, extraction the feature, classification and then recognition.Nowadays, because of the various changes of weather condition, the weather news becomes very important part for everybody.Mostly, the deaf people can't hear weather news when the weather news is broadcast by using radio and television channel but the deaf people also need to know about that news report.This system designed to classify and recognize the weather news words as the Myanmar texts on the sounds of Myanmar weather news reporting.In this system, two types of input features are used based on Mel Frequency Cepstral Coefficient (MFCC) feature extraction method such MFCC features and MFCC features images.Then these two types of features are trained to build the acoustic model and are classified these features using the Convolutional Neural Network (CNN) classifiers.As the experimental result, The Word Error Rate (WER) of this entire system is 18.75% on the MFCC features and 11.2% on the MFCC features images.
An Optimizing Multilevel Thresholding for Image Segmentation Based on Hybrid Swarm Computation Optimization

This paper suggests a solution for the image segmentation (IS) problem with the multilevel thresholding based on one of the latest hybrid swarm computation optimization algorithms, particle swarms, and gravitational search (PSGA). The experimental results are comparable with other state-of-the-art algorithms that show that the PSGA on selected images is better than the competitors.
Virtual Museum ‘Takeouts’ and DIY Exhibitions–Augmented Reality Apps for Scholarship, Citizen Science and Public Engagement

This paper presents an Augmented Reality (AR) project for the curation of virtual museum ‘takeouts’ and DIY exhibitions. The project’s outputs include novel AR app technology demonstrators to support co-design with museum users and stakeholders - the goal being to create useful and easy-to-use AR apps for scholars, citizen scientists and the interested public.The apps were designed for users to create, display, animate and interact with exhibitions of selected 3D artefacts that could, for example, reflect academic specialisms for sharing with fellow researchers, support curators in exhibition planning or enable friends and students to share eclectic favourites from museum visits. The overarching project ambition was to create AR apps to support research, engagement and education, and to enable interactive and personalized visualizations of individual artefacts as well as reconstructed forms. As presented in the paper, these forms are exemplified in the AR apps with 3D models of a cuneiform envelope and its tablet contents, viewable either as i) separate artefacts or ii) in their reconstructed enveloped form, with the AR apps enabling animated opening and ‘X-ray views’ of the contents within. In this way, the apps can enable users to visualize individual objects and reconstructions that could, for example, incorporate artefacts held in different museums.
Structural Encoding and Pre-training Matter: Adapting BERT for Table-Based Fact Verification

Growing concern with online misinformation has encouraged NLP research on fact verification.Since writers often base their assertions on structured data, we focus here on verifying textual statements given evidence in tables.Starting from the Table Parsing (TAPAS) model developed for question answering (Herzig et al., 2020), we find that modeling table structure improves a language model pre-trained on unstructured text.Pre-training language models on English Wikipedia table data further improves performance.Pre-training on a question answering task with column-level cell rank information achieves the best performance.With improved pre-training and cell embeddings, this approach outperforms the state-of-the-art Numerically-aware Graph Neural Network table fact verification model (GNN-TabFact), increasing statement classification accuracy from 72.2% to 73.9% even without modeling numerical information.Incorporating numerical information with cell rankings and pre-training on a question-answering task increases accuracy to 76%.We further analyze accuracy on statements implicating single rows or multiple rows and columns of tables, on different numerical reasoning subtasks, and on generalizing to detecting errors in statements derived from the ToTTo table-totext generation dataset.
Statistical and Machine Learning Models for Classification of Human Wear and Delivery Days in Accelerometry Data

Accelerometers are increasingly being used in biomedical research, but the analysis of accelerometry data is often complicated by both the massive size of the datasets and the collection of unwanted data from the process of delivery to study participants. Current methods for removing delivery data involve arduous manual review of dense datasets. We aimed to develop models for the classification of days in accelerometry data as activity from human wear or the delivery process. These models can be used to automate the cleaning of accelerometry datasets that are adulterated with activity from delivery. We developed statistical and machine learning models for the classification of accelerometry data in a supervised learning context using a large human activity and delivery labeled accelerometry dataset. Model performances were assessed and compared using Monte Carlo cross-validation. We found that a hybrid convolutional recurrent neural network performed best in the classification task with an F1 score of 0.960 but simpler models such as logistic regression and random forest also had excellent performance with F1 scores of 0.951 and 0.957, respectively. The best performing models and related data processing techniques are made publicly available in the R package, Physical Activity.
A Systematic Review of Cognitive Diagnostic Assessment and Modeling through Concept Mapping

Traditional assessments are typically constructed on logical taxonomies and content specifications but lack explicit cognitive models of the processes and problem-solving strategies that underlie student performance. Cognitive Diagnostic Assessment (CDA) fills this gap by combining cognitive science and psychometrics. CDA is in its infancy, but over 1,000 relevant studies have been conducted in this area during the last 20 years. Facing these complicated studies, many beginners struggle to understand the whole picture of CDA. This paper systematically reviews the literature on CDA and relevant cognitive diagnosis models (CDMs) with the application of a concept mapping technology. Concept mapping is graphical representation of concepts and their relationships. Its use in this study allows researchers and students to gain in-depth knowledge about CDA and CDM and identify areas of future research.
A Novel Secure Data Hiding Technique into Video Sequences Using RVIHS

Most of the present hiding techniques on video are considered over plaintext domain and plain video sequences are used to embed information bits. The work presented here reveals the novelty for information embedding in a video sequence over the ciphered domain. The carrier video signal is encrypted using chaos technique which uses multiple chaotic maps for encryption. The proposed reversible video information hiding scheme (RVIHS) exhibits an innovative property that, at the decoding side we can perfectly extract the information along with carrier video without any distortion. The public key modulation is a mechanism used to achieve data embedding, where as in secret key encryption is not required. The proposed approach is used to differentiate encoded and non-encoded picture patches at decoder end by implementing 2 class Support Vector Machine grouping. This helps for us to retrieve the original visual sequence with embedded message and to scale up embedding capacity. The experiment is conducted using real time videos for embedding the information. The outcome of proposed work bring about best embedding capacity, compared to existing techniques.
A Reconfigurable Memory based Fast VLSI Architecture for Histogram Computation

Histogram computation is the crucial task used in processing so many image guided applications like pattern recognition, image segmentation etc. Image registration is one of the fundamental techniques for pre-processing of the images. Registration is the process of overlaying multiple images to geometrically align them. In medical Image processing, the improper registration can have negative impact on the analysis of the image which influences the final diagnosis. The accurate result of image registration is obtained by matching of multimodal images. Mutual Information is one of the commonly used techniques to find the similarity measurement between multi-modal images. Measurement of similarity requires a computation of histogram of individual images and joint histogram between the images. The hardware implementation of histogram computation has advantages in terms of flexible design, low power consumption, high speed, less execution time than the software implementation. This paper proposed a parallel algorithm for histogram computation. A memory based pipeline architecture is designed for implementing the proposed algorithm. The hardware mapping of the algorithm on FPGA is proposed and simulating them using Xilinx software tools.
Fire incidents visualization and pattern recognition using machine learning algorithms

A fire incident is a devastating event that can be avoided with enough knowledge on how and when it may occur. For the past years, fire incidents have become a big problem for the Philippines, since it affects the socio-economic growth of the country. Machine learning algorithm is a well-known technique to predict and analyze data. It can also be used to recognize pattern and develop models for artificial intelligence. Pattern recognition through machine learning algorithm is already established and have proven itself accurate in different fields such as education, crime, health and many others including fire incidents. This paper aims to develop a model for recognizing patterns of fire incidents in the province of Laguna, Philippines implementing a machine learning algorithm. With the foregoing project, it is found out that a recurrent neural network shows an astonishing result in terms of pattern recognition. Further, it is also found that Calamba City is the most vulnerable area in case of fire occurrence in the Province of Laguna.
Comparison of U-spatial statistics method with Classical Statistics results in the determination of geochemical anomalies of Epithermal Gold in Khoshnameh area, Hashtjin, Iran

In this study, methods based on the distribution model (with and without personal opinion) were used for the separation of anomalous zones, which include two different methods of U-spatial statistics and mean plus values of standard deviation (). The primary purpose is to compare the results of these methods with each other. To increase the accuracy of comparison, regional geochemical data were used where occurrences and mineralization zones of epithermal gold have been introduced. The study area is part of the Hashtjin geological map, which is structurally part of the folded and thrust belt and part of the Alborz Tertiary magmatic complex. Samples were taken from secondary lithogeochemical environments. Au element data concerning epithermal gold reserves were used to investigate the efficacy of these two methods. In the U- spatial statistics method, and criteria were used to determine the threshold, and in the method, the element enrichment index of the region rock units was obtained with grouping these units. The anomalous areas were identified by, and criteria. Comparison of methods was made considering the position of discovered occurrences and the occurrences obtained from these methods, the flexibility of the methods in separating the anomalous zones, and the two-dimensional spatial correlation of the three elements As, Pb, and Ag with Au element. The ability of two methods to identify potential areas is acceptable. Among these methods, it seems the method with criteria has a high degree of flexibility in separating anomalous regions in the case of epithermal type gold deposits.
Neural Linguistic Steganalysis via Multi-Head Self-Attention

Linguistic steganalysis can indicate the existence of steganographic content in suspicious text carriers. Precise linguistic steganalysis on suspicious carrier is critical for multimedia security. In this paper, we introduced a neural linguistic steganalysis approach based on multi-head self-attention. In the proposed steganalysis approach, words in text are firstly mapped into semantic space with a hidden representation for better modeling the semantic features. Then, we utilize multi-head self-attention to model the interactions between words in carrier. Finally, a softmax layer is utilized to categorize the input text as cover or stego. Extensive experiments validate the effectiveness of our approach.
BIOLOGICAL GENE SEQUENCE STUCTURE ANALYSIS USING HIDDEN MARKOV MODEL

Identification or prediction of coding sequences from within genomic DNA has been a major part of the search of the gene. In this work real hidden Markov models (HMMs) to denote the consensus and deliver a beneficial tool in determining the splicing junction sites Markov models which has a recurring nature in computational biology leads to statistical models, in every sequential analysis it plays a role of putting up a right label on each residue. In sequential alignment and as well as in gene identification namely exons, introns or intergenic sequences which make in a sequence with homologous residue with the target database. Under the gene identification methodology Condon bias, exons, introns have length preference which leads to a combination of splice site consensus. Parameters are fixed on the onset while weight of the different information are polled together leading to the interception of result probability, which could lead to identifying the best score based on score mean and how confident are the best scoring answers are perfect. This leads to the concept of extendibility, to perfect and ad hoc gene finder, which is a modeled transitional methodology leading to the consensus, alternate splicing and offers polyadenylation signal. This leads to piling of authenticity against a delicate ad hoc program which could make to breakdown under its individual weightiness.&#x0D;
SELF-REGULATED LEARNING IN ENGINEERING STUDENTS: STRATEGIES FOR PEDAGOGICAL RESOURCE

This paper presents some findings of the investigation "Self-regulated learning in engineering students of the Army Engineering Faculty (FIE)". The central theoretical contributions on the “self-regulation of learning” comes from theconceptual approaches of Zimmerman and Pintrich since they allow the understanding of the processes that takes placein the control of: thoughts, feelings, motivations and actions for the achievement of the learning objectives.It was found that to achieve this self-regulation in their learning, the students in this sample developed strategies throughwhich they manage and internalize resources from the academic environment. They are grouped into two large catego-ries: group study and the use of elements of the environment. The dynamics of conformation and functioning of work incollaborative study teams are similar among students. In addition, they describe the progressive use of elements of theinstitutional environment: study spaces, materials, technologies, institutional devices and also codes and dynamics ofsocial interaction typical of this faculty. The achievement of self-regulation implies the progressive incorporation of theresources of the academic context and this is a process that demands time and effort from the student. The acquisitionof self-regulation is evidence of the solid construction of the student ́s craft and a successful educational affiliation tothe university context.
Integrated Specification of Quality Requirements in Software Product Line Artifacts

There are many calls from software engineering scholars to incorporate non-functional requirements as first-class citizens in the software development process. In Software Product Line Engineering emphasis is on explicit definition of functional requirements using feature models while non-functional requirements are considered implicit. In this paper we present an integrated requirements specification template for common quality attributes alongside functional requirements at software product line variation points. This approach implemented at analytical description phase increases the visibility of quality requirements obliging developers to consider them in subsequent phases. The approach achieves weaving of quality requirements into associated functional requirements through higher level feature abstraction method. This work therefore promotes achievement of system quality by elevating nonfunctional requirement specification. The approach is illustrated with an exemplar mobile phone family data storage requirements case study.
An Efficient Class-Based Data Clustering Through K-Means And Knn Approach

In this paper an efficient class-based data clustering through k-means and KNN approach were applied. It has been applied for proper data grouping and efficient classification. In this approach three dataset have been considered for the experimentation. Data preprocessing has been performed for the removal of unmatched or empty entry. Then weight assignment and normalization has been performed. K-means and k-nearest neighbor (KNN) have been applied on the dataset for the data grouping and classification purpose. Different splitting variations have been considered with higher variance. Data selection is completely random. The results obtained shows the strength of our approach though classification and class-based clustering.