Reviewerly/exHarmony huggingface dataset:
Improving the Reliability of Web Based Result Query Systems during High Traffic Periods

Displaying of examination results by a single central entity, for lakhs of students becomes a tedious task, and sometimes may also result in server crashing. These servers typically rely on heavy and often unrestricted threads spawned to handle each incoming request which is the reason why the server resources are used up quickly. We propose a solution that is three fold: First, multiple Volunteer entities are brought in to hold the data and donate a portion of their computing power to offload the enormous work placed on the central entity. Second, the central entity is changed to play the role of dispatcher that generates monitors and assigns extremely lightweight, independent processes (called agents) to each user request without requiring any additional hardware upgrade. Each agent will be responsible to satisfy their assigned user requests. Third, we introduce a load balancing technique derived from the ideas of autonomous agents load balancing techniques in cloud to provide load balancing among the Volunteer entities and the central entity such that the Volunteer entities can continue with its own tasks and not be overwhelmed by its Volunteer work while ensuring fast response time and better reliability and response to the user.
Bandoneon!

<italic>Bandoneon! (a combine)</italic>, performed in October 1966 as part of the <italic>9 Evenings: Theatre and Engineering</italic>, has been regarded as Tudor’s first work as a composer. However, the conception of this piece was not only directly influenced by two other amplified bandoneon pieces he realized in the same year, Gordon Mumma’s <italic>Mesa</italic> and Lowell Cross’s <italic>Musica Instrumentalis</italic>, but had also started off as a realization of a “Mobius-Strip” composition by Mauricio Kagel. Moreover, most of the modular electronics in his setup were also used in other realizations around the same period. The true difference lies in how these common materials were used and to what ends. What the self-proclaimed effort to make a “giant white noise generator” from scratch brings to the fore is a synecdochical relationship between the modular instruments used and the larger instrumental complex they compose. The investigation of this coordination between the parts and the whole reveals a strange disappearance of an entire group of instruments considered central to the performance, a mystery that highlights the peculiar nature of Tudor’s “composition.”
The Effect of XR in Audio Visual Medium: Consumer Revolution of Virtual Platforms

This paper Investigates usage cases and consumer studies of AR/VR technologies that corroborate the positive impacts of Virtual applications in learning. Taking a look at the history of audiovisual media and the impact of screen technology in modern life, helped justify the creation of the DirectAR app which enables young Cinematography enthusiasts to learn basic concepts used in video creation.
Free Eye Examination Policy for Primary School Students North Larangan, Banten

Background: Playing videogames or looking at smartphone for long period of time may cause visual dysfunction such as myopia and amblyopia.This visual dysfunction also impairs learning activity among primary students.Vision screening for schoolaged children becomes important to detect the eye problem.This study aimed to investigate the free eye examination policy for primary school students North Larangan, Banten.Subjects and Method: This was a descriptive qualitative study conducted at North Larangan, Tangerang, Banten, in July 2019.A sample of 6 informants and 28 primary students were selected for this study.The informants were including chief of the citizen association, chief of the neighborhood association, optic owner, refractionist, parents, and primary student.The theme of this study was analysis of the free eye examination policy for primary school students.The data were collected by in-depth interview and direct observation.The data were analyzed by content analysis. Results:The citizen association 005 collaborated with Remaja optic for free routine eye examination program for primary school students and health education.The results of the eye examination were 20 students had ≥-1.00, 4 students had ≥-2.00, 3 students had ≥-3.00 and 1 student had ≥-4.00.This program had just been implemented, and its effect on the reduction of visual dysfunction had not been identified.Eye health education was carried out by refractionist for students and parents.The citizen association 005 made a regulation imposing for 4 hour compulsory study at home in order to reduce time for playing online game.Conclusion: Free routine eye examination for primary school students in citizen association 005 has been started, but its effect on the reduction of visual dysfunction has yet to be identified.The compulsory study hour has also been implemented although its enforcement is weak.
Robust Classification using Twosome Classifier

The proficient knowledge revolution with the concept class detection is a significant challenge for the incremental learning classification, here the batch of data is flowing unremittingly. The algorithm recommended in this research learns continual raw data, transform preceding knowledge to the present data without stating to the longstanding data and able to proficiently allow new concept class noticed by the classification. A major aim was knowledge transformation and the accumulation of the same with concept class detection in the incremental data flow. After the analysis of non-incremental ML approaches for classification and incremental learning algorithms in the literature, this research introduces new incremental learning algorithm, which uses twosome classifiers, in which knowledge transformation and new class detection have been done efficiently. The performance of the system is validated using simulation results on available datasets. The class wise and batch wise accuracy is calculated. The projected technique is also used to detect concept class detection.
USING АPRIORI ALGORITHM TO SEARCH FOR ASSOCIATIVE RULES

In this article is investigated Apriori algorithm and are showed some simple examples of it using on R-language to see how it's helpful.
Organic chemistry synthesis problem as artificial intelligence planning

This thesis formulates organic chemistry synthesis problems as Artificial Intelligence planning problems and uses a combination of techniques developed in the field of planning to solve organic synthesis problems. To this end, a methodology for axiomatizing organic chemistry is developed, which includes axiomatizing molecules and functional groups, as well as two approaches for representing chemical reactions in a logical language amenable to reasoning. A novel algorithm for planning specific to organic chemistry is further developed, based on which a planner capable of identifying 75 functional groups and chemical classes is implemented with a knowledge base of 55 generic chemical reactions. The performance of the planner is empirically evaluated on two sets of benchmark problems and analytically compared with a number of competing algorithms. v
A Post-Processing Algorithm for Spatial Domain Detection &amp; Removal of Alocking Artifacts

Block based Discrete Cosine Transform (BDCT) is commonly used to detect and remove blocking artifacts in the compressed images. We proposed spatial domain post processing algorithm with four fold model. In the initial stage, pixel vector (PV) is calculated for horizontal as well as vertical block boundaries, after defining PV calculation of different threshold values is made for extracting blocking artifacts. These thresholds are basically adaptive to the image quality due to strong correlation with the PV. To avoid ringing artifacts across block edges directional filter is proposed. Our research further worked on region classification based upon activity of PV within the blocks. Based upon different PV activity regions separate filters are used to achieve best filtering and finally Symmetrical Pixel Normalization filter (SPN Filter) is used to normalize the values of symmetrical pixel value for better visual performance . Proposed technique various indices like PSNR, MSSIM, GBIM are calculated and compare with different post processing techniques used in literature
Checking In With Google Books, HathiTrust, and the DPLA

Google Books and HathiTrust have been making headlines in the library world and beyond for years now, while a new player, the Digital Public Library of America (DPLA), has only recently entered the scene. This article will provide a “state of the environment” update for these digital library projects including project history and background. It will also examine some challenges common to all three projects including copyright, orphan works, metadata, and quality issues
Exploration and Performance Assessment of Efficient Histopathology Image Pre-Processing and Segmentation Techniques for Breast Cancer Prediction

Evaluation of features after segmentation is a significant process in image processing, especially in the medical field. Medical imaging technologies are widely utilized in clinical diagnosis to guide therapeutic and surgical decisions and to monitor disease advancement, understanding occurrence and re-occurrence of infection and view treatment response. Among all cancer kinds, Breast Cancer (BC) now-a-days has turn out to be a common form of cancer amongst ladies around the world and is the second common cause of most cancers deaths. At present, there aren't any powerful methods to save you and remedy breast cancer, because its cause is not absolutely known. Early detection is the most effective way to enhance breast cancer survivals and might deliver a better hazard of full healing. The main motive of this paper is to implement various efficient image segmentation techniques after preprocessing histopathology BC images of different magnification level and exhaustively compare them to achieve best results. We analyze the outcomes of widely used edge detection based image segmentation methods such as Adaptive K-Means, Multi Class Fuzzy C-Means, Canny, Gabor filter and Homogeneity based PSO and evaluate them via metrics which includes accuracy, precision, recall and F-degree. Various preprocessing strategies like histogram equalization (HE), Adaptive equalization (AE) and Contrast Stretching (CS) are compared and used to enhance the performance of above segmentation methods. Contrast Improvement Index (CII) is chosen as a selection criteria for preprocessed image. Finally one vs. all multiclass SVM classifier is used for BC image classification. Our implementation uses breast cancer dataset having two classes as benign and malignant each in turn having four sub-classes. Images of different magnification levels such as 40x, 100x, 200x and 400x, are considered for BC detection.
A Content Level Based Deduplication on Streaming Data using Poisson Process Filter Technique (PPFT)

This paper proposed on Poisson process-based algorithm is to carry out content-level deduplication for the streaming data. Since Poisson processes are meant to do the counting of different events happening over a period of time and space, it becomes appropriate to use it for identifying duplications of data as it gets streamed based on time and space, which can allow the deduplication process to be carried out in tandem. Some of the research on deduplication has been focusing on File-level and Block-level deduplication while the focus can be brought to content-level, as data get streamed lively and becomes more dynamic. With this approach, the content-level deduplication will allow the data to be scanned intelligently and at the same time, it can save the deduplication operation time. Also, streaming data has its randomness which is innately there and by having Poisson process based deduplication it will address the random behaviour of the data transfer and can work efficiently in the dynamically connected environment. The proposed method identifies the unique data to store in the Database. Based on the experimental result, the Poisson Process-based algorithm produce 0.912 Area Under Curve (AUC) accuracy on real-world streaming data, which means that if AUC is greater than 0.8 then the performance of algorithm is pretty good. So, the machine intelligence-based deduplication model produced reliable and robust deduplication on streaming data compared to existing approaches.
Juxtaposing Python with BASIC in the Context of Introductory Programming

Computer programming and programming language is relatively machinedependent, as it spring up control element called software through computational algorithms.Introductory programming is quite fundamental to study of computing and computational theories, and as such it is offered /taught at elementary or intermediate level for computer science majors across tertiary institutions.Though, different programming languages had came into being with focus and diversity for problem solving in different domains.Exploratory context of lexical and semantic similarities between BASIC and Python were provided in this paper.
The impact of Human Capital on the Public Services Performance

The goal of the study is to check the impact of human capital on the performance of public services in Bahrain. Through the literature reviewing showed that human capital is considered to be one main element which helps to the enhancing and improvement organizational performance especially public service performance. The lack of enough previous studies in the area of human capital and performance of public services in the public sector was the motivated to conduct this study. The number of questionnaires that distributed to citizens of Bahrain was 640 via systematic random sampling in the four governorates of Bahrain: Capital, Muharraq, Northern and Southern and 384 questionnaires were returned, with 368 responses found usable. The results of the study revealed that human capital has a significant and positive relation on the performance of public services.
Beyond Paper: PDart - Participatory Design Augmented Reality tool

This paper presents the idea of PDart, a tool that would allow end-users to actively participate in the (re-)design of extended reality (XR) application interfaces and content.The idea is based on our existing tool PDot, which supports Participatory Design (PD) by enabling end-users to shape the content and 2D interfaces of software, websites, or apps.As PDot allows the inclusion of any digital content available online as artefact to collect feedback on, it can also be used for images of AR prototypes.However, these are static and presented in a browser rather than an AR environment, which limits the experience of the participants as well as the scope of the evaluations possible.We therefore describe our plans of implementing the features of PDot in PDart, together with several challenges we might encounter along the way.Participatory Design.
خصائص مضمون صحافة المواطن في سلطنة عمان خلال عام 2021

This study aims to reveal the characteristics of semi-institutional citizen journalism in the Sultanate of Oman by analyzing (10) social media accounts on "Twitter" of citizen journalists, which is one of the most important and widely used forms by citizen journalists. The study relies on the public sphere theory using media survey methodology via content analysis tool during the period of 1 to 31st of January 2021 and analyzed 1440 tweets posted during that period. The study revealed that citizen journalists are interested in publishing local news of a political nature by using the news template. The news function performed by tweets was the most common among the study sample. The study pointed out that citizen journalists use the "Arabic" language more than any other language. Moreover, the study sample tended to use media patterns and images with text mode. The study indicated the lack of use the feature of reference, links, and hashtags, and regarding interaction features such as: reply, re-tweet, and like, the citizen journalists tended to use the "like" feature more. The study revealed that the practice of citizen journalism on social media platforms in Oman is not dedicated to specialists in the media field but is available to everyone who has the tools and time to practice it. The study revealed the agreement of the characteristics of citizen journalism in the Sultanate of Oman with the characteristics of the public sphere identified by Habermas. These accounts allow all audiences to access, participate, and express their opinions, freely. On the other hand, those responsible for these accounts are completely reluctant to express their opinion on the events they cover, which limits the ability of citizen journalism in the Sultanate of Oman to become a real public sphere in which society’s events and issues are freely discussed. Keywords: Citizen Journalism; Oman; Social Media; Public Sphere Theory.
Identifying The Relationship Between Motivation, Anxiety and Intended Effort of Learning English: An SEM Approach

South Bengkulu Dept. of Education in 2017 initiated English Community Learning Program (PPKBI), addressed to all high school students in South Bengkulu Regency.As the program progresses, students' learning motivation tends to dissipate, seen from the decreasing number of participants.This study aims to identify the relationship between motivation, anxiety and intended effort in learning English.This study uses data from 453 high school students in South Bengkulu Regency, participants of PPKBI 2017 (average 16.15 years, 179 female).The questionnaires consisted of 40 items (LLOS / Language Learning Orientation Scale (24 items), Foreign Language Class Anxiety Scale (6 items) and Intended Effort (10 items).Analysis done by identifying common factors using Exploratory Factor Analysis, followed with Confirmatory Factor Analysis (CFA).The hypothesized model fulfills the requirement of goodness of fit index and questionnaires have good validity and reliability indexes.The model explains that most students who follow through with PPKBI are more intrinsically motivated.
Hierarchially Distributed Data Matrix Scheme for Big Data Processing

MapReduce is a programming paradigm and an affiliated Design for processing and making substantial data sets. It operates on a large cluster of specialty machines and is extremely scalable Across the past years, MapReduce and Spark have been offered to facilitate the job of generating big data programs and utilization. However, the tasks in these structures are roughly described and packaged as executable jars externally any functionality being presented or represented. This means that extended roles are not natively composable and reusable for consequent improvement. Moreover, it also impedes the capacity for employing optimizations on the data stream of job orders and pipelines. In this article, we offer the Hierarchically Distributed Data Matrix (HDM), which is a practical, strongly-typed data description for writing composable big data appeals. Along with HDM, a runtime composition is presented to verify the performance of HDM applications on dispersed infrastructures. Based on the practical data dependency graph of HDM, various optimizations are employed to develop the appearance of performing HDM jobs. The empirical outcomes show that our optimizations can deliver increases of between 10% to 60% of the Job-Completion-Time for various types of applications when associated with the current state of the art, Apache Spark.
Graph Theory

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Improved Cuckoo Optimization Algorithm for Association Rule Hiding

Association Rule Mining (ARM) is a standard data mining practice used to determine interactions hidden in huge sets. Association Rule Hiding (ARH) methods are used to preserve the privacy of data in ARM. ARH process modifies the original database without changing any non-sensitive rules and data. In order to hide the sensitive rules, cuckoo search optimization algorithm that was developed for hiding the sensitive association rules (COA4ARH) was proposed for sensitive rule hiding. In COA4ARH, number of transactions that should be modified to hide the sensitive rules is not considered which may leads to more number of iteration. In this paper, two properties are introduced to select less number of transactions to be modified. It makes the COA4ARH algorithm faster, decreases the number of lost rules and is suitable for variety of datasets. In order to increase the rule hiding capability of COA4ARH, new fitness functions are introduced. The new fitness functions reduce the amount of lost rules and avoid generation of ghost rules which are formed as objectives of COA4ARH algorithm. The multiple objectives in COA4ARH are conflicting with each other. This is known as multi-objective optimization problem. The multi-objective optimization deals with set of non-dominated solutions (Pareto front) for the problem having more than one objective. It is solved by using Crowding Distance (CD) which selects the optimal set of solution for association rule hiding. Thus, the proposed Improved COA4ARH- CD (ICOA4ARH-CD) can be suitable for variety of datasets and effectively hides the sensitive rules with fewer side effects.
Supplemental Material for Speakers and Listeners Exploit Word Order for Communicative Efficiency: A Cross-Linguistic Investigation

Supplemental materials 1. Post-hoc power analysesWe determined the power of our studies by bootstrapping our experimental data for each key prediction.All code for these post-hoc power analyses is available at https://osf.io/9hw68/.All our key predictions (see below) relied on differences between conditions.We analyzed these differences by computing bootstrapped 95% confidence intervals over the difference in effect sizes and seeing if the interval crossed 0 (suggesting there may be no difference in effect sizes) or not.The bootstrap process followed two stages.First, we bootstrapped the data to obtain a replicate of the two target experimental conditions (depending on prediction; see below).Next, for each experimental replicate, we conducted the analysis from the manuscript: bootstrapping the results to obtain a confidence interval over the difference in effect sizes.We ran 5000 experimental replicates and within each replicate we bootstrapped the difference in effect sizes with 1000 replicates.Experiment 1 Prediction 1. Percentage of redundant color words should be different in the four-item displays across languages.In 94.9% of the bootstrapped replicates, the corresponding confidence interval did not cross 0, suggesting that the two effects were reliably different and that our experiment's power is 0.949.Prediction 2. Percentage of redundant color words in English should be higher in the 16item displays relative to the four-item displays.In 92.0% of the bootstrapped replicates, the corresponding confidence interval did not cross 0, suggesting that the two effects were reliably different and that our experiment's power is 0.92.Prediction 3. Percentage of redundant color words in Spanish should be higher in the 16item displays relative to the four-item displays.In 100% of the bootstrapped replicates, the corresponding confidence interval did not cross 0, suggesting that the two effects were reliably different.Naturally, this does not imply that our power is 1, as it is likely that at least one replicate would eventually not produce a difference.Note, however, that the difference across conditions was striking in Spanish (see Figure 1S below), with almost no Spanish speaker producing color words in the four-item display, and the majority of them producing color words more than half of the time in the 16-item display.Given that we found no replicates that went against our predictions in the 5000 bootstrap samples, this suggests that the probability of a replicate not showing our predicted effect is lower than 1/500 = 1e-04.