Reviewerly/exHarmony huggingface dataset:
Video Captioning based on Augmented Semantic Alignment

Video captioning aims to describe the target objects, object attributes and object interactions contained in the video using the natural language. Existing methods can generate description that generally match the video content, but they are insufficient for generate finer interactions/action between objects, that is typically represented by the predicate in description sentences. In contrast to other parts of the sentence, the predicate is not only reliant on the dynamic action but also the static scene. Therefore, the relationship between its corresponding visual semantics and text semantics is difficult to align. In this research, we propose an Augmented Semantic Alignment model (ASA) for video captioning that explicitly learns finer actions by enhancing the semantic alignment relationship between video and text. Specifically, we first introduce a multimodal feature aggregation network to capture high-quality, more action-relevant video semantic features. And then we use an action-guided decoder to fuse video semantic features and predicate information representing actions, resulting in a finer action description. Validated on two public datasets, the proposed model can generate descriptions of finer actions, that exhibit better semantic alignment with dynamic content in videos.
Accelerating High-resolution Image Stitching for the Dual Camera System based on GPU

Image stitching plays an important role in the field of computer vision and image processing. Although some sophisticated stitching methods have been proposed, they are usually time-consuming. Because many application scenarios, such as video surveillance, need quick image stitching to provide a wide field of view, accelerating the image stitching is critical. In recent years, the neural network is used to stitching images, but only relatively small size images can be processed; meanwhile, time-consuming image stitching is also unavoidable. In this paper, to quickly stitch 1920×1080 images generated by the dual camera system, an image stitching algorithm is proposed. We divided the algorithm depending on its timeliness on different steps, and its time-consuming steps are parallelized on Graphics Processing Unit (GPU) platforms to be accelerated. We tested different hierarchies for organization of threads and access of memory on compute unified device architecture (CUDA) programming, and the final experiment results showed that our algorithm could reach a speedup ratio up to 119x on the Desktop with 3080ti.
A Weight Optimization Method of Deep Echo State Network Based on Improved Knowledge Evolution

As a deep network, deep echo state network (DESN) is equipped with the reservoir as the core and has great prediction performance with lower computational cost. It has been widely used in the field of time series prediction. However, during the construction process of DESN, the weights of reservoirs such as the input weights and the internal weights are randomly initialized and not adjusted after initialization, which limits the performance of DESN. The traditional weight optimization methods can improve the performance, but they require a lot of training samples and training time. Therefore, this paper firstly introduces the knowledge evolution into the weight optimization of DESN, and then proposes a weight optimization method of DESN based on improved knowledge evolution. First, the weights of DESN are split into two parts by the weight-level splitting, namely the fit-hypothesis and the reset-hypothesis. Second, the sailfish optimizer is used to train the fit-hypothesis. Finally, simulation results on three time series datasets indicate that DESN optimized by this method has better prediction performance and requires less training samples and training time, compared with the one optimized by other existing weight optimization methods.
Diagnosing Breast Cancer Using a Novel Dual-Layered Random Forest with Null Handling

The purpose of this project was to determine if I could develop an early and accurate model of breast cancer detection that can decrease the mortality rate of women by using a novel dual-layered Random Forest with Null Handling. Mammograms have an accuracy of about 86.9% and are susceptible to False negatives, and False positives. In order for my model to be trained and tested, the Wisconsin Data for Breast Cancer was accumulated and duplicated. In the duplicated data, random values were deleted. The first random forest is then trained on x% of the processed data. The next random forest is trained on the output of the previous random forest and the processed data. It acted to fine-tune results from the previous model. Lastly, the majority of the votes from the individual random forests led to the cancer prediction. I found out that dual-layered random forests with null values in their training data had an accuracy of 94.4%, which is 7% higher than human accuracy. This model also overcame overfitting. All our dual-layered models or models trained with appended null data worked better than human detection and could be built and tested in under 7 seconds with an easy-to-use interface, allowing for results in the same visit to the hospital. The best model had the first layer of 200 trees, the second layer of 800 trees, and accuracy of over 94% compared to humans with 86.9%. This model is fast, accurate, and can save people’s lives.
Tracking Genre Variation with Formative Variables: A PLS-SEM Study in Mandarin Chinese

Multivariate methods have manifested potential for innovating stylometric research since Biber (1988) first adopted multi-dimensional analysis (MDA) for register variation studies. Structural Equation Modeling (SEM) is one of the mainstream techniques for conducting multivariate statistics. This paper specifically adopted the PLS-SEM (Partial Least Square) method, rarely employed for language studies, to investigate genre diversity in Mandarin Chinese. This study included a dataset (sorted by 20 genre types for 14,556 texts in 28 million tokens) and calculated the 77 feature-tag frequencies. The analysis treated the tag frequencies in each text as the formative observed variables for the seven grouped sets of features (part-of-speech categories), which are also the formative variables for higher-order latent variables, formulating two PLS-SEM structures. Firstly, the observed variables were stratified into a model containing three factors. Secondly, the seven POS categories formed another model concerning one higher-order language-use variable. This study adopted the SMARTPLS software package to analyze the 3-factor and the 7-POS models. The investigation compared the path coefficients between variables using the tag frequencies in the 20-genre dataset. The difference exhibited a mechanism to distinguish textual variants and identify language styles. Evidently, the envisioned framework could serve as an applicable instrument for future stylistic research.
Image Watermarking Framework Using Histogram Equalization and Visual Saliency

A digital image watermarking strategy using histogram equalization and visual saliency followed by LSB (Least Significant Bit) replacement is proposed for better imperceptibility with hiding capacity. With this technique, a saliency map determines lesser observable parts of the original image and are gradually implanted with increasing amounts of information based on histogram equalization information. The output from saliency are the perceptible areas within an image which is the most notable position from the perspective of vision, as a result, any changes made other than those areas will be less noticeable to viewers. The implementation of histogram method helps to identify the areas where we can hide our secrete information within that image. Using LSB replacement technique we are inserting our secrete information into the original image adaptively. Here, imperceptibility, robustness and hiding capacity are used to evaluate the technique.
Feature Selection in MicroArray Datasets using Manta Ray Foraging Optimization Technique

In this document, we apply the Manta Ray Foraging Optimization technique for Feature Selection in the genetic dataset to identify the relevant features from a high dimensional feature space.MRFO is one of the recent meta-heuristic optimization techniques which has been used for Feature Selection.presence of cancerous tumors in the patient.[10].This document analyzes the pre existing algorithms developed in the space of Feature Selection using the MRFO algorithm in the GEM dataset.The MRFO method uses three unique search techniques in three different ways.The local search ability of the algorithm is highly aided by the chain foraging behavior; the global search ability is determined by the manta ray cyclone foraging behavior; and the local search ability and convergence rate are greatly improved by the somersault foraging behavior.This document, two separate learning classifiers were used: K-NearestNeighbors (KNN) classifier and the Random Forest Classifier were used for calculating the fitness
Preface: Education of Science, Technology, Engineering, and Mathematics International Conference (ESTEMIC) 2021

First Page
SISTEM INFORMASI BERBASIS WEB UNTUK PENGELOLAAN ARENA OLAHRAGA PADA APRIDA SPORT CENTER JAKARTA

The competition between sports arena businesses today is very tight and few can survive. So it takes good management to survive. Apart from having complete availability and services, Aprida Sports Center does not yet have a good system in processing transaction data for its operational activities. So that there are often errors in booking data, payment transactions, and difficulties in making reports. This reason makes the writer interested in this research to evaluate the implementation of transaction data processing on the running system. The purpose of this research is to solve the problems that occur in the exiting conditions. The design of the sports arena management system uses PHP programming and MySql database to make it easier to apply. With the information system for managing the sports arena, customers, prospective customers, and owners can easily access the information about the field. The sports arena management information system is run as expected
RANCANG BANGUN SISTEM INFORMASI PENDATAAN LAPORAN SKRIPSI DENGAN ANALISA METODE SWOT

The students to graduate and get a degree are required to make a Job Training and Final Reportt. So that accumulated many reports that must be recorded every year. All these reports should be recorded and saved to the storage media. The recording process currently carried out uses a recording system in a book. This recording system becomes a problem when making periodic reports for the data collection on Job Training Reports and Final Projects. Besides the problems in reporting, there are also difficulties when wanting to find out which students have submitted reports or not, because there is no system that can display the latest data. The results of research to make to solve the problems that occur require an information system. For this reason, a system of application for the data collection on Job Training Reports and Final Projects is designed. The results of the system design have met technological, operational, and legal feasibility so that they are suitable for use
Implementation and applications of triaxial oscilloscope in sound visualization

This work describes the process of developing a real-time sound visualization system, presenting various parameters in both intuitive and useful way, with the added benefit of being visually appealing.Initial idea was to extend a traditional oscilloscope display into a third spatial dimension and by that improve time-scale intelligibility or introduce visualization of additional sound parameters, while preserving cohesive and intuitive form of an oscilloscope image.The visualization system has been implemented in graphical programming language Max, particularly video and graphics framework Jitter and OpenGL API.It has been tested in various scenarios, including Lissajous shapes based visualization of triads and three-dimensional goniometer for surround mixing tasks.
A CRITICAL DISCOURSE ANALYSIS OF MEDICAL ASSOCIATIONS AND RESEARCH JOURNALS ON OBESITY USING VAN DIJK’S MODEL

This present study aims at exploring the CDA approach to determine the differences in the discourse of research articles in medical journals and associations. The study investigates how the associations delete, select, construct or generalize information for their benefits. The information was gathered from medical research articles and associations about obesity. World Health Organization (WHO), Obesity Medicine Association (OMA), National Institutes of Health (NIH), National Health Services (NHS), Healthline, Mayo Clinic are among the organizations that have been chosen for this study. The macro structure of Van Dijk model is used to address the research questions and to determine whether medical journals and associations are on the same page or not. As a result, associations have more authority than journals. The results of this descriptive self-study are summarized in graph charts and tables.
Mobile learning and socially constructed blended learning through the lens of Activity Theory

&#x0D; &#x0D; &#x0D; The pandemic has drastically changed the education landscape. The pedagogical practices, policies and procedures ingrained and refined over many years were suddenly rendered less effective. Overnight, new practices, policies and procedures had to be drafted to support teaching and learning. More than a year on, educators have found a new home, new pedagogies and practices have been refined and continue to be, policies and procedures are agile to support a volatile environment academia dwells in during the pandemic era. Building upon the work the Mobile Learning Special Interest Group (MLSIG) presented last year at the conference, we investigate the role mobile learning is and could play in emerging pandemic pedagogies. Eight vignettes are presented from universities around the world that are analysed using Activity Theory to understand the role of mobile devices and social tools for developing blended synchronous learning (BSL) and HyFlex learning.&#x0D; &#x0D; &#x0D;
Student Teachers’ Experiences of Open Distance e-Learning Support in a Posthuman Era: A Learner Engagement Perspective

Online learning uses information and communication technologies which rely on reliable connectivity. While this is a giant step to widen access in South African education as shown by a number of studies conducted already regarding online learning, less focus has been paid on rural students which are under-resourced. They are presumed to have access and support to online learning and assistive ICTs that make online learning possible. Therefore, the focus for this study was on KwaZulu-Natal rural student teachers’ experiences of open distance e-learning in a posthuman era. The study focused on students’ experiences regarding online support tools like discussion forum and others as tools for student engagement and support on myUnisa platform. The problem was investigated using a descriptive qualitative case study, which used individual interviews. The study involved fifteen UNISA student teachers from KZN and the findings revealed that, notwithstanding the countless challenges, students were very passionate about the use of online learning in open distance e-learning and they showed a desire to engage more using different types of devices and platforms as they learn through social media and also showed that learning resides in technological appliances they use(posthumanism), hence the study’s conclusion and implications stress that the distance between the student and the institution, student and lecturer and student and other students can be mediated and reduced through proper student support services such as provision of gadgets for internet connection, proper telecommunications infrastructure, ICT workshops and training for all students and in posthumanism terms, formal integration of social networks in learning in light of the finding that learning is non-linear and resides in technological devices used to connect students.
Big Data Security: Detect and Prevent the Data from Attacks with Digital Forensic Tools

The main objective of this research is to defend big data against various cyberattacks. Today, it is commonplace to use big data security to safeguard data and analytical processes against malicious activity. Cybercriminals are increasingly using fraudulent calls and texts and other techniques to steal users' private information. Nowadays, these attacks are increasingly frequently online. This study has found various security technologies and strategies that are being utilized to make big data more secure from our prior research references. This study has also learnt about digital forensics tools that may be used to identify who, where, and how an attack was conducted. The fields of data science and cybersecurity are combined in this study. Detecting attacks on big data and ensuring and mitigating data security using cybersecurity and digital forensics techniques are the key goals of this study.
An Improved Scheduling Algorithm for Grey Wolf Fitness Task Enrichment with Cloud

In various industries, there has been an increase in need for massive processing power and more space, and to meet this demand, another breakthrough known as distributed computing has been introduced. Distributed computing innovation has grown in popularity as a result of its ability to provide these forms of support in a financially viable manner. With the introduction of virtualization, IT administrators have started to shift to distribute computing. Virtualization had made it possible to have unrestricted access to assets. Because, Cloud Computing is currently an infancy, additional research is needed to estimate its maximum capacity. More research is needed on how assets and assignments are assigned in a cloud environment. As a result the Quality of Services of the administrations offered by cloud specialist co-ops is represented. This research paper proposes to use the CloudSim toolkit to replicate the Performance by Improved Scheduling Algorithm by Gray Wolf Fitness Function (ISAGWFF) based on achieving improvements in the time spent fraction of assets and undertakings in distributed computing. The primary objective for its existence is to reduce both handling time and cost in accordance with genuine competence. The popularity of the suggested methodology can be seen in the reenactment results, which indicate a significant reduction in task completion time and cost. Using this approach, a greater number of errands can be completed proficiently within the cutoff time. As a result, the results reveal that the ISAGWFF technique outperforms conventional calculations in terms of comprehension to execution.
Sentiment Analysis Classification for Text in Social Media: Application to Tunisian Dialect

Social networks are the most used means to express oneself freely and give one's opinion about a subject, an event, or an object. These networks present rich content that could be subject today to sentiment analysis interest in many fields such as politics, social sciences, marketing, and economics. However, social networkusers express themselves using their dialect. Thus, to help decision-makers in the analysis of users' opinions, it is necessary to proceed to the sentimental analysis of this dialect. The paper subject deals with a hybrid model combining a lexicon-based approach with a modified and adapted version of a sentiment rule-based engine named VADER. The hybrid model is tested and evaluated using the Tunisian Arabic Dialect, it showed good performance reaching 85% classification.
Abbreviation Dictionary for Twitter Hate Speech

Informal methods of communication, like tweets, rely heavily on initialization abbreviations to reduce message size and time, making them difficult to mine and normalize using existing methods. Therefore, this present study compiled a lexicon repository to normalize the initialism abbreviations used in tweets in the English language. Several components were taken into consideration while compiling the repository. This included the Tweepy Python library, keyword list, small developed rules, and online dictionaries. A lexicon repository of 300 abbreviations and their complete forms was compiled. This will be used in an ongoing study to normalize Twitter hate speech data and to detect it.
The Relativity of Energy and the Reversal of Time is a Shift in Perspective

The Relativity of electrical engineering explains the reversal of time as a reversal of current resulting from electrical or magnetic reactance. This affects the frame of reference of electricity, but this is not obvious to the observer. Polarity of the sign of a simulator's time interval is derived from its power and energy assessment. Current reversal is equivalent to time reversal. Reversal of current increases voltage differences in deference to common sense. Coils of wire become generators without any significant prime mover. This requires a severe reduction of input power so as to avoid repressing these effects. The foreseeable consequence is a severe reduction of the size of energy farms putting electrical energy into the hands of third-world countries and pragmatic industry. The cost of energy can be integrated into the cost to manufacture an appliance possessing its own, built-in power supply.
Analysis of The Implementation of SCRUM in The Development of A BBLobster Content Management System

PT Rapier Technology International applies scrum in developing the BBLobster Content Management System (CMS). Scrum is a framework for developing an iterative, flexible, and effective BBLobster CMS designed to deliver significant value quickly. This study aims to identify the problems encountered in scrum implementation and validate the factors that influence the interaction with Scrum adoption during the BBLobster CMS development process using variable measurements. The measurement variables are the team factor variable, individual factor variable, technology factor variable, organizational factor variable, and scrum adoption variable. They analyzed the measurement of the data model (outer model) and structural model (inner model) using SmartPLS 3.0 after data collection using a questionnaire via google forms. The results of this study show that the analysis of the structural model test conducted by knowing the value of R-Square (R2) is a team factor variable, individual factor, technological factor, and organizational factor that has an influence of 67.4% on scrum adoption.
The aesthetics of the self: The meaning-making of Internet aesthetics

In this study we explore the so-called Internet aesthetics, labels applied on heterogenous collections of materials and activities by Internet users, which are discussed and constructed primarily on the Internet. We conducted qualitative content analyses on entries of AestheticsWiki, as well as interviews with 11 Internet aesthetics enthusiasts. In contrast to established notions such as genre, style or subculture, Internet aesthetics are characterized by few conventions, but seem fundamentally open for individual interpretations. We therefore theorize that they primarily serve as toolkits, through which individuals give sense and coherence to personal experience, in line with a situation-specific self-image. In doing so, they contribute to a reflexive myth of the self.