Reviewerly/exHarmony huggingface dataset:
Just noticeable difference (JND) and satisfied user ratio (SUR) prediction for compressed video

This paper contains the research proposal of Jingwen ZHU that was presented at the MMSys 2022 doctoral symposium. Just noticeable difference (JND) is the minimum amount of distortion from which human eyes can perceive difference between the original stimuli and distorted stimuli. With the rapid raise of multimedia demand, it is crucial to apply JND into the visual communication systems to use the least resources (e.g., bandwidth and storage) but without damaging the Quality of Experience (QoE) of end-users. In this thesis, we focus on the JND prediction for compressed video to guide the choice of optimal encoding parameters for video streaming service. In this paper, we analyse the limitations of the current JND prediction models and present five main research questions to address these challenges.
Perception of video quality at a local spatio-temporal horizon

This paper contains the research proposal of Andréas Pastor that was presented at the MMSys 2022 doctoral symposium. Encoding video for streaming on Internet has become a major topic to reduce the consumption of bandwidth and latency. At the same time, the human perception of distortions has been explored in multiple research projects, especially for distortions generated by Coder-DECoder (CODEC) algorithms. These algorithms operate in a rate-distortion optimization paradigm to efficiently compress video content. This optimization can be driven by metrics that are most of the time not based on the human perception, and more importantly, not tuned to reflect the local perception of distortions by human eyes.
GENDER AND LANGUAGE VARIATION ON THE COMMENTS OF VIRAL YOUTUBE VIDEOS

This study aims at analyzing the language variations between female and male comments on YouTube viral videos as to abbreviations, emojis, laughter variants, and spelling variants of personal pronouns, utilizing a mixed-method design. This study revealed that males tend to use abbreviations in their comments in the leisure domain. Females used more abbreviations in the information and knowledge domains and emojis in the two domains. The female users used haha, hehe, and jaja more frequently than male users in the leisure domain. Male and female users used the laughter variant more often in the leisure domain than in the information and knowledge domain. Women preferred to write the standard spelling of the personal pronouns "I" and "you." Moreover, both men and women used abbreviations to express their views immediately to speed up the typing of messages. Women were more familiar with positive and negative emojis than men. Language varies according to YouTube users' preferences in using the language when posting comments online, and the core social attributes influencing language use are social class, social networks, sex and gender, ethnicity, and age. Thus, infographic material with meanings and examples can be distributed to students and teachers.
Students’ Perceptions Of Their Readiness, Self-Learning Motivation And Attitude Toward English Online Learning During Covid-19 Pandemic At High Schools In North Makale

The research aimed at describing how students studied during the Covid-19 pandemic, the process of shifting from a face-to-face learning to online learning. In order to achieve these aims, the researcher designed three main objectives: to find out students’ readiness in English online learning, students’ motivation in English online learning and student’ attitude towards English online learning. The researcher applied the quantitative method. The samples were chosen randomly from 65 students within 7 different high schools in North Makale. The data analyzed was in quantitative research involved summarizing data dependably and accurately. The result showed that the students are not fully ready for English online learning during the Covid-19 Pandemic. The lack of infrastructure such as internet access is the major obstacle to the implementation of distance learning in schools, especially in North Makale. Furthermore, the samples are not confident computer and internet users, and they also demonstrate low self-efficacy issue in doing online learning. The students’ motivation toward English online learning was inconsistent even thought it is categorized in a moderate degree of motivation. Some students prefer face-to-face learning process than online learning and think that the previous one will give them a better learning result. It becomes a challenge for teachers to prepare the teaching materials, teaching strategies and the methods well before the English online learning.
Stability of the Money Demand Function Revisited: Evidence from Sierra Leone

The study empirically examined the stability of the money demand function that justifies the adoption of the monetary aggregate targeting framework by the Bank of Sierra Leone using quarterly data spanning from 2002 to 2018.To account for structural breaks emanating from policy shocks and regime shifts in the data such as the civil unrest from 1991-2002, the Ebola Virus Disease (EVD) outbreak from 2014-2016, the mudslide in 2017 and other global shocks, the study conducted unit root tests with structural breaks and regime shift.The long-run estimates of the ARDL model confirm the stability of the money demand functions.In particular, the results show high income elasticity of money demand when narrow money was used as the dependent variable, implying a cash based economy that requires serious consideration in the conduct of monetary policy.Given that the economy is still rudimentary with high propensity of underground economic activities, it is palpable that monetary policy will continue to be challenged especially when the central bank continues to struggle in mopping up excess liquidity in the banking system as a result of the enormous shadow activities in the country.Hence, any policy that is geared towards targeting the narrower definition rather than broader definition of money could help mitigate the challenges associated with withdrawing excess liquidity from the banking system and thus enhances the conduct of an effective monetary policy.
The New Dataset MITWPU-1K for Object Recognition and Image Captioning Tasks

In the domain of image captioning, many pre-trained datasets are available. Using these datasets, models can be trained to automatically generate image descriptions regarding the contents of an image. Researchers usually do not spend much time in creating and training the new dataset before using it for a specific application, instead, they simply use existing pre-trained datasets. MS COCO, ImageNet, Flicker, and Pascal VOC, are well-known datasets that are widely used in the task of generating image captions. In most available image captioning datasets, image textual information, which can play a vital role in generating more precise image descriptions, is missing. This paper presents the process of creating a new dataset that consists of images along with text and captions. Images of the nearby vicinity of the campus of MIT World Peace University-MITWPU, India, were taken for the new dataset named MITWPU-1K. This dataset can be used in object detection and caption generation of images. The objective of this paper is to highlight the steps required for creating a new dataset. This necessitated a review of the existing dataset models prior to creating the new dataset. A sequential convolutional model for detecting objects on a new dataset is also presented. The process of creating a new image captioning dataset and the gained insights are described.
Securing Audio Chat With Cryptool-Based Twofish Algorithm

Technology and the internet are growing very rapidly in society making it easy for people to share information and communicate with each other. However, the security of such data or information is something that should be highlighted. The utilization of technology and the internet has many security gaps that can make data or information vulnerable to being stolen and even misused. Valuable data or information is very likely to be accessed by unauthorized people. On the other hand, data and information are prone to be illegally altered and even duplicated. In connection with various possible data or information security issues, it is necessary to do a data security. The purpose of this study is to secure audio chat using a Cryptool-based Twofish algorithm. Based on research conducted, the security process with encryption and decryption simulations was successfully carried out on audio chat. Audio chat sent via IP Address can be encrypted into ciphertext and can be decrypted back into audio at a speed of 15.78 kB/s and the resulting size is also still the same, which is 160 packets. This Twofish algorithm proved to be well usable because the size and quality of chat audio generated from decryption is still the same as audio chat before it is encrypted.
Speedup Multimedia Authoring Process: Incrementality of Multimedia Authoring Verification

Multimedia authoring tool performs to combine various kinds of media, both visual and non-visual media into a multimedia document.The process for converting media objects into multimedia documents in the form of multimedia programming uses a mandatory process called the kernel mechanism.The kernel mechanism consists of editing, verification, modeling and translation processes.In the kernel mechanism there is a time-consuming verification process.Verification used in the kernel mechanism is time computation verification and Spatio-temporal verification.With the increasing number of media objects, the verification process time will be longer.A novel method proposed in this study is incrementality of time computation and incrementality of spatio-temporal verification.The Incrementality process uses a specific algorithm to process verification on media objects that have been modified by the author.Experiments that have been carried out in this study show that this method is successful in speeding up the verification process significantly.
The use of virtual and augmented reality in science and math education in Arab countries: A survey of previous research studies

The aim of this study was to present a survey of research relating to the use of virtual and augmented reality in science and math education in Arab countries after 2012. The research studies were collected from several sources that include a set of common Arabic research database, a set of English research database, and popular search engines. Thirty three research studies were found related to the use of virtual and augmented reality in science and math education in Arab countries. These studies were examined based on four variables: focus, research method, data collection tools, and geographical distribution. The results showed that most of the research related to uses of virtual and augmented reality in science and math education in Arab countries focused on students and their teachers and most of the researches followed the two-group experimental design, and the quantitative descriptive research. Regarding the methods of data collection most of the studies used test and questionnaires. In addition, most of the research studies were conducted in Saudi Arabia and Egypt. Based on the findings, a set of recommendations were presented.
The Current Situation and Trend of Blockchain Technology in the Financial Field

In the Internet era, with the development and application of information technology, the financial environment is becoming more and more complex, and traditional financial business products and service models are gradually unable to meet people’s daily needs. In response to this problem, it is important to iteratively upgrade the traditional business products by rationally applying the existing information technology in the entire financial market and realize the adjustment of the organizational structure of financial institutions and the optimization of products and services. With the development of network technology, the research on the application of blockchain technology in the financial field is gradually carried out, and its advantages and characteristics are of great significance to the optimization and upgrading of the traditional financial system. The purpose of this paper is to analyze the research status and trends of blockchain technology in the financial field. Through CiteSpace to analyze all relevant researches on blockchain technology in the financial field, we can grasp the research status and trends of blockchain technology in the financial field as a whole, coping with financial issues in the new environment. This article explains the basic theory of blockchain technology and conducts an overall visual analysis of related research on blockchain technology in the financial field based on CiteSpace. The results showed that the research keywords of blockchain technology in the financial field at home and abroad are concentrated in “supply chain finance,” and China’s research on this keyword accounts for 59.5%. Foreign research on this keyword accounts for 26.5%. The difference is 33%, and the overall popularity is on the rise.
Modified Fitness Dependent Optimizer for Solving Numerical Optimization Functions

The Fitness Dependent Optimizer (FDO) is a recent metaheuristic algorithm that was developed in 2019.It is one of the metaheuristic algorithms that has been used by researchers to solve various applications especially for engineering design problem.In this paper, a comprehensive survey conducted about FDO and its applications.Consequently, despite of having competitive performance of FDO, it has two major problems including low exploitation and slow convergence.Therefore, a modification of FDO (MFDO) is proposed for solving FDO issues.MFDO used two methods to enhance the performance of FDO: firstly, optimizing the range of weight factor between 0 and 0.2 which is used for finding fitness weight.Secondly, using sine cardinal mathematical function to update fitness weight and pace which is referred to the speed of the bees.To evaluate the performance of MFDO, 19 classical benchmark functions and CEC2019 benchmark functions are used.MFDO compared against all the enhancement of FDO and also it is compared with Grey Wolf Optimization (GWO), Chimp Optimization Algorithm (ChOA), Genetic Algorithm (GA), and Butterfly Optimization Algorithm (BOA).Statistical results proved that MFDO achieved significant performance compared to other algorithms.Finally, MFDO is used to solve three applications: Welded Beam Design (WDB), Pressure Vessel Design (PVD), and Spring Design Problem.Results proved that MFDO outperformed well in solving these applications against FDO, Gravitational Search Algorithm (GSA), GA, and Grasshopper Optimization Algorithm (GOA). .
Multikey Verifiable Homomorphic Encryption

A homomorphic encryption (HE) scheme is an advanced encryption technology which allows any user receiving ciphertexts to perform computations over them in a public manner.An important application of an HE scheme is a private delegating computation where clients encrypt their secret data, send the ciphertexts to a (computationally powerful) server who perform computations over encrypted data.In this application, one of the crucial problems is that the delegated server might be not trusted one and in this case, we cannot believe that a server always returns correct computation results.To solve this problem, Lai, Deng, Pang, and Weng (ESORICS 2014) proposed a verifiable homomorphic encryption (VHE) as a core primitive realizing private and verifiable secure delegating computation.However, their VHE scheme only supports homomorphic evaluation over ciphertexts generated by a single user.In this paper, we propose a formalization and its construction of multi-key verifiable homomorphic encryption (MVHE), which is a new cryptographic primitive for realizing private and verifiable delegated computation in the multi-client setting.Our construction can be obtained by combining a multi-key homomorphic encryption scheme and a multi-key homomorphic encrypted authentication scheme, which is also a new primitive provided in this work.
Semantic Modeling and Analysis of Natural Language System Requirements

System requirements specify how a system meets stakeholder needs.They are a partial definition of the system under design in natural language that may be restricted in syntax terms.Any natural language specification inevitably lacks a unique interpretation and includes underspecified terms and inconsistencies.If the requirements are not validated early in the system development cycle and refined, as needed, specification flaws may cause costly cycles of corrections in design, implementation and testing.However, validation should be based on a consistent interpretation with respect to a rigorously defined semantic context of the domain of the system.We propose a specification approach that, while sufficiently expressive, it restricts the requirements definition to terms from an ontology with precisely defined concepts and semantic relationships in the domain of the system under design.This enables a series of semantic analyses, which guide the engineer towards improving the requirement specification as well as eliciting tacit knowledge.The problems addressed are prerequisites to enable the derivation of verifiable specifications.We present the results from a case study of modest size from the space system domain, as well as an evaluation of our approach from the user's point of view.The requirement types that have been covered demonstrate the applicability of the approach in an industrial context, although the effectiveness of the analysis depends on pre-existing domain ontologies.
RealROI: Discovering Real Regions of Interest From Geotagged Photos

Given a set of geotagged photos with a view direction, the discovering real regions of interest (DRRI) problem involves identifying real regions of Interest (RealROI) in the photos.The problem is important in many practical applications, such as tourist route recommendation and travel advertising.Much work has been conducted to utilize geotagged photos to discover regions of interest or routes and to provide recommendations according to similar preferences.However, these approaches are not ideal for identifying regions of interest (ROI) because the usage of geotagged photos is limited to the GPS coordinates of the photo.This paper proposes novel algorithms to discover real regions of interest in photos based on the direction in which the photos were taken.
Effectiveness of SETS-Based Electronic Student Worksheet (E-LKPD) to Improve Student Learning Outcomes

This research aimed to determine the effectiveness of electronic-based student worksheets (E-LKPD) on student learning outcomes. The method used was a quasi-experiment with one group pretest-posttest design. The sample taken in a cluster random sampling were 32 students of class VII SMPN 2 Ngemplak Sleman School Year 2021/2022. The instrument used was a problem sheet in the form of a description. The results of the study were analyzed using quantitative descriptive methods. The results of the analysis of the increase in the pretest and posttest obtained an average of N-Gain 0.60 in the medium category. The average pretest value was 6.94 while the average posttest value was 8.80. Test results of t sig value (2-tailed) &lt; 0.05 showed a significant improvement between pre-test and post-test. This indicated that E-LKPD-based SETS was effective for improving students' cognitive learning outcomes.
Statistical Analysis of Students’ Reading Habits and their Academic Performance in Tertiary Institution: A Case Study of the Departments in School of Applied Sciences Federal Polytechnic, Ekowe Bayelsa State

Reading is seen as a process of looking at a written or printed word (or symbol) and getting meaning from them. For a student to have good academic performance, it is perceived that the students should have good reading habits which correlate to the fact that there is a relation between reading habits and academic performance. The test statistic used in this research is the chi-square test of independence, the data used is a primary data gotten through the use of questionnaire administered to the students in the School of Applied Sciences, Federal Polytechnic Ekowe, Bayelsa State. The questionnaire is based on students’ reading habits and their academic performance, the result of the analysis was carried out at 5% level of significance which revealed that academic performance, grade point average, cumulative grade point average, and reading habits are independent of their departments.
Building a LAMP Stack: Apache and PHP

A LAMP stack is the combination of Linux with Apache, MariaDB or MySQL, and PHP, that provides a robust, versatile platform for creating websites and web applications. It’s a perfect addition to your Linux lab because it can become the foundation for a wide variety of freely available and custom software tools.
Gold Price Prediction using Machine Learning

We predict future gold rates supported twenty two market variables victimization machine learning technique.One machine learning algorithm, random forest regression were used in analyzing these knowledge[1]. Historically, gold was used for supporting trade transactions around the world besides alternative modes of payment. Various states maintained and increased their gold reserves and were recognized as rich and progressive states. In present times, precious metals like gold area unit control with central banks of all countries to make sure re-payment of foreign debts, and conjointly to control inflation. Moreover, it conjointly reflects the Imoney strength of the country[2]. Besides government agencies, varied transnational firms and people have conjointly invested with in gold reserves. In ancient events of Asian countries, gold is in addition presented as gifts/souvenirs and in marriages, gold ornaments are conferred as gift in Republic of India. KEYWORDS: Price prediction, Machine Learning, Supervised Learning, Linear Regression, Python , Power Bi , Tableau.
AN ANALYSIS OF STUDENT DIFFICULTIES IN MASTERING VOCABULARY IN EIGHT GRADE AT SMPN 06 KOTABUMI IN THE ACADEMIC YEAR OF 2021 / 2022

Abstract : Vocabulary is an important part of foreign language learning that must be mastered well by students because vocabulary is an important factor that supports students' ability to communicate. Students who experience little vocabulary mastery will have difficulty understanding English lessons. The objectives of the research is describe the difficulties faced by the eighth grade students of SMPN 6 Kotabumi in the 2021/2022 academic year in mastering vocabulary and describe the dominant difficulties in mastering the vocabulary of the eighth grade students of SMPN 6 Kotabumi in the 2021/2022 academic year. The approach used in this research is qualitative and the method used is case study design. The research was conducted in class VIII SMPN 6 Kotabumi. The data sources of this study were eighth grade students. The data collection techniques were interview, and observation. To analyze the data, the researcher used data reduction, data presentation, and drawing conclusions or verification. The results of this study are the difficulties that students face in mastering vocabulary such as: students are not able to pronounce vocabulary correctly and precisely, students have difficulty in distinguishing vocabulary that has the same spelling and pronunciation, students have difficulty and do not understand grammar and students are unable to understand the context of English. The difficulty most often experienced by students is the lack of understanding of the context in English sentences in all aspects, both in pronunciation, spelling, grammar and grammar..&#x0D;