Reviewerly/exHarmony huggingface dataset:
Hadoop Job Scheduling Using Improvised Ant Colony Optimization

Hadoop Distributed File System is used for storage along with a programming framework MapReduce for processing large datasets allowing parallel processing. The process of handling such complex and vast data and maintaining the performance parameters up to certain level is a difficult task. Hence, an improvised mechanism is proposed here that will enhance the job scheduling capabilities of Hadoop and optimize allocation and utilization of resources. Significantly, an aggregator node is added to the default HDFS framework architecture to improve the performance of Hadoop Name node. In this paper, four entities viz., the name node, secondary name node, aggregator nodes, and data nodes have been modified. Here, the aggregator node assigns jobs to data node, while Name node tracks aggregator nodes. Also, based on the job size and expected execution time, an improvised ant colony optimization method is developed for scheduling jobs.In the end, the results demonstrate notable improvisation over native Hadoop and other approaches.
A prediction framework with time-frequency localization feature for detecting the onset of seismic events

Onset detection of P-wave in seismic signals is of vital importance to seismologists because it is not only crucial to the development of early warning systems but it also aids in estimating the seismic source parameters. All the existing P-wave onset detection methods are based on a combination of statistical signal processing and time-series modeling ideas. However, these methods do not adequately accommodate some advanced ideas that exist in fault detection literature, especially those based on predictive analytics. When combined with a time-frequency (t-f) / temporal-spectral localization method, the effectiveness of such methods is enhanced significantly. This work proposes a novel real-time automatic P-wave detector and picker in the prediction framework with a time-frequency localization feature. The proposed approach brings a diverse set of capabilities in accurately detecting the P-wave onset, especially in low signal-to-noise ratio (SNR) conditions that all the existing methods fail to attain. The core idea is to monitor the difference in squared magnitudes of one-step-ahead predictions and measurements in the time-frequency bands with a statistically determined threshold. The proposed framework essentially accommodates any suitable prediction methodology and time-frequency transformation. We demonstrate the proposed framework by deploying auto-regressive integrated moving average (ARIMA) models for predictions and the well-known maximal overlap discrete wavelet packet transform (MODWPT) for the t-f projection of measurements. The ability and efficacy of the proposed method, especially in detecting P-waves embedded in low SNR measurements, is illustrated on a synthetic data set and 200 real-time data sets spanning four different geographical regions. A comparison with three prominently used detectors, namely, STA/LTA, AIC, and DWT-AIC, shows improved detection rate for low SNR events, better accuracy of detection and picking, decreased false alarm rate, and robustness to outliers in data. Specifically, the proposed method yields a detection rate of 89% and a false alarm rate of 11.11%, which are significantly better than those of existing methods.
Energetic Data Security Management Scheme using Hybrid Encryption Algorithm over Cloud Environment

Now-a-days all documents are in a digital format as well as everyone need to maintain their data in electronic mode with the help of cloud servers. A cloud server provides lots of facilities to users such as remote data maintenance, huge data handling and so on. But in the case of security many cloud servers are providing probabilistic results alone. So, that a new cloud server data maintenance scheme is required to provide a high-level data security to the cloud system in an efficient manner. This paper introduces a new crypto-approach called Novel Hybrid Encryption Mode (NHEM), in which this algorithm integrates several latest approaches of crypto logics into it and provides a top-end security level to the cloud data. This NHEM is derived from the base factors of two cryptographic algorithms called Advanced Encryption Standard with 512-bit size of key frequency with Message Digest (MD5) algorithm. By using the integration of these two powerful approaches, a novel approach called NHEM is designed and it provides a huge support to preserve security on cloud medium. The cloud data needs to be protected from unwanted threats and intruders by means of raising an interruption attacks on server. The proposed approach of Novel Hybrid Encryption Mode concentrates more on access control logics and the crypto norms with respect to privacy measures. In this paper, the proposed scheme assures the data is too robust and no one can attack the data without proper credentials as well as the outcome proofs are clearly given on the resulting unit of this paper. The Advanced Encryption Standard algorithm is a well-known and powerful crypto scheme and the MD5 algorithm is also considered to be the unique unidirectional algorithm to provide security level in energetic manner. These two algorithms are combined together with the bit frequency of 512 to achieve the highest accuracy levels in data security over cloud environment as well as the resulting portion clearly illustrates that with proper graphical outcome.
Randomized Deep Structured Prediction for Discourse-Level Processing

Expressive text encoders such as RNNs and Transformer Networks have been at the center of NLP models in recent work.Most of the effort has focused on sentence-level tasks, capturing the dependencies between words in a single sentence, or pairs of sentences.However, certain tasks, such as argumentation mining, require accounting for longer texts and complicated structural dependencies between them.Deep structured prediction is a general framework to combine the complementary strengths of expressive neural encoders and structured inference for highly structured domains.Nevertheless, when the need arises to go beyond sentences, most work relies on combining the output scores of independently trained classifiers.One of the main reasons for this is that constrained inference comes at a high computational cost.In this paper, we explore the use of randomized inference to alleviate this concern and show that we can efficiently leverage deep structured prediction and expressive neural encoders for a set of tasks involving complicated argumentative structures.
Extraction of Key Motifs as a Preview from 2017 Nobel Prize Winning Novel, ‘Never Let Me Go’

Word clouds manifest interactive visuals along with their statistical data. Thus knowledge discovery and aesthetic data visualization interlink to produce interactive word cloud which is an interesting, textual, statistical and visual data. This study aims to generate interactive word cloud—Cirrus—on the basis of statistical data to preview text of the novel for readers. So cirrus tool is selected from Voyant open access tools to produce interactive statistical word cloud. Then the generated word cloud and statistical data are analyzed with mixed method and its analysis draws insight from Rakesh Aggrawal’s Knowledge Discovery Theory which seeks innovative and interesting knowledge patterns. This thematic word cloud verifies already known themes and discovers innovative interesting themes. Current study reveals that all mentioned key themes can be easily extracted from a voluminous novel with the help of Cirrus tool. Key motifs have been presented in the word cloud for the readers. On the other hand, unwritten themes can’t be extracted through machine learning tools, rather it is the task of human cognition. Primarily, this novel based study reveals names of chief characters, for instance “Tommy (496),” “Ruth (455)” and “I (Kathy) (355).” Furthermore, motifs of nostalgic memories with word “remember (143),” “thought (126)” about “Hailsham (203),” “carer (74),” “sex (80),” sex “lectures (8)” have been discovered as a preview. Previewing technique prepares reader’s mind and gives an epigrammatic digital view of the text. The visual themes as knowledgeable word cloud leave an indelible mark on the slate of memory.
CF-CNN: Coarse-to-Fine Convolutional Neural Network

In this paper, we present a coarse-to-fine convolutional neural network (CF-CNN) for learning multilabel classes. The basis of the proposed CF-CNN is a disjoint grouping method that first creates a class group with hierarchical association, and then assigns a new label to a class belonging to each group so that each class acquires multiple labels. CF-CNN consists of one main network and two subnetworks. Each subnetwork performs coarse prediction using the group labels created by the disjoint grouping method. The main network includes a refine convolution layer and performs fine prediction to fuse the feature maps acquired from the subnetwork. The generated class set in the upper level has the same classification boundary to that in the lower level. Since the classes belonging to the upper level label are classified with a higher priority, parameter optimization becomes easier. In experimental results, the proposed method is applied to various classification tasks to show a higher classification accuracy by up to 3% with a much smaller number of parameters without modification of the baseline model.
Efektivitas Penggunaan Aplikasi Wordwall dalam Pembelajaran Daring (Online) Matematika pada Materi Bilangan Cacah Kelas 1 di MIN 2 Kota Tangerang Selatan

The purpose of this study was to determine the effectiveness of the use of the Wordwall application in online mathematics learning on counting number 1 in MIN 2 South Tangerang and the effectiveness of using Wordwall media in the closing activity which was reviewed throgh test results (final test of all materials). The research used is a combination research by combining quantitative and qualitative techniques. With the subject of this research are 56 studens of class 1 MIN 2 South Tangerang. In this study, various instruments were used to collect data such as: (1) interviews to support the result of student response questionnaires (2) interviews to support the results of student response questionnaires (3) questions throgh the Wordwall media and test questions to see the effectiveness of using Wordwall media in online mathematict learning activities. The results of this study were (1) in the student response questionnaire using Wordwall media in the closing activities of online mathematics learning, counting numbers were very effective with an average questionnaire value of 32,3 (2) the results of the tests carried out by the students resulted in a percentage completeness of 88,04 and 75% of the number of students so that it has been declared effective.
Dimensioning UX Models for Design and Evaluation

This paper attemptsto find out the available dimensions of user experience models in extant literature. The study observed that there are numeroususer experience models with a number of dimensions. This notwithstanding, there appears to be no concordamong practitioners and researchers on the dimensioning of UX models. The reason for lack of consensus is due tounderlying nature of user experience which signifies that UX should be structured and dimensioned differently dependent on the context and domain of use. Seemingly every experience is particularly unique and is situated in time and context, thus making the dimensioning of UX to vary and differ. A close look at prior literature indicate that several user experience models exist but their dimensions are however not comprehensive enough for a holistic design and evaluation of digital applications experiences. To this end, this study proposed a set of dimensions for interactive systems that will aid a more comprehensive and holistic design and evaluation of the user experience of interactive applications.
On EST Sentence Translation Based on English and Chinese Structures: Natural Linear Expansion vs. Reversed Linear Expansion

With the development of science，new subjects with a new technical vocabulary spurt from technology and science, and it makes translation in EST more difficult. It turns out that similarities between English and Chinese can be handled easily in translation, while differences between the two make translation in EST a hard task. The intention of this study is to analyze the formulas of the patterns between English and Chinese in thinking and explore possible advantages in translation practice.
A verifiable quantum key agreement protocol based on six-qubit cluster states

Quantum key agreement requires all participants to recover the shared key together, so it is crucial to resist the participant attack. In this paper, we propose a verifiable multi-party quantum key agreement protocol based on the six-qubit cluster states. A verifiable distributor who preserves some subsequences of the six-qubit cluster states is introduced into this protocol, thus the participants can not obtain the shared key in advance. Besides, the correctness and simultaneity of the shared key are guaranteed by the trusted design combiner and homomorphic hash function. Furthermore, the security analysis shows that the new protocol can resist the external and internal attacks.
Review of Bounding Box Algorithm Based on 3D Point Cloud

Abstract Collision detection is mainly to judge whether there is intersection between virtual models, which means there is collision. Bounding box is one of the important methods of collision detection. It uses regular geometry with simple structure to replace the complex model to be detected. A simple point cloud model usually contains hundreds or thousands of polygons when meshed. When collision tests are performed directly on the geometry of two model objects, the calculation process is relatively complex. In order to reduce the computational cost, the bounding box test of the model was performed before the geometry intersection test. Only when the bounding box has a collision, can further accurate intersection detection be carried out. This paper mainly introduces the content and significance of the bounding box, and compares four kinds of common bounding boxes and their advantages and disadvantages as well as the application scenarios of the bounding box. Firstly, the content and significance of the bounding box are expounded. Secondly, it is analyzed according to different bounding boxes. Finally, the different bounding box algorithms are summarized, and the advantages and disadvantages of different bounding boxes and application scenarios are pointed out.
Investment Risk Analysis On Bitcoin With Applied of VaR-APARCH Model

Investment can be defined as an activity to postpone consumption at the present time with the aim to obtain maximum profits in the future. However, the greater the benefits, the greater the risk. For that we need a way to predict how much the risk will be borne. Modelling data that experiences heteroscedasticity and asymmetricity can use the Asymmetric Power Autoregressive Conditional Heteroscedasticity (APARCH) model. This research discusses the time series data risk analysis using the Value at Risk-Asymmetric Power Autoregressive Conditional Heteroscedasticity (VaR-APARCH) model using the daily closing price data of Bitcoin USD period January 1 2019 to 31 December 2019. The best APARCH model was chosen based on the value of Akaike's Information Criterion (AIC). From the analysis results obtained the best model, namely ARIMA (6,1,1) and APARCH (1,1) with the risk of loss in the initial investment of IDR 100,000,000 in the next day IDR 26,617,000. The results of this study can be used as additional information and apply knowledge about the risk of investing in Bitcoin with the VaR-APARCH model.
“Laughing at you or with you”: The Role of Sarcasm in Shaping the Disagreement Space

Detecting arguments in online interactions is useful to understand how conflicts arise and get resolved.Users often use figurative language, such as sarcasm, either as persuasive devices or to attack the opponent by an ad hominem argument.To further our understanding of the role of sarcasm in shaping the disagreement space, we present a thorough experimental setup using a corpus annotated with both argumentative moves (agree/disagree) and sarcasm.We exploit joint modeling in terms of (a) applying discrete features that are useful in detecting sarcasm to the task of argumentative relation classification (agree/disagree/none), and (b) multitask learning for argumentative relation classification and sarcasm detection using deep learning architectures (e.g., dual Long Short-Term Memory (LSTM) with hierarchical attention and Transformer-based architectures).We demonstrate that modeling sarcasm improves the argumentative relation classification task (agree/disagree/none) in all setups.* Equal Contribution. Arg. Rel. Turn PairsPrior Turn: Today, no informed creationist would deny natural selection
Comparative Study on Sentiment Analysis Approach for Online Shopping Review

The internet has revolutionized the way most people shop. Flexibility, convenience, products’ variations, better price, and more privacy contribute to the exponential growth of online shopping platforms. However, due to the nature of online shopping, customers are not able to physically test the product before purchasing. They rely on the information given by the seller and previous customers’ ratings to make their decision. Sometimes, the information that is given by sellers may be fraudulent, misleading, or over claim. Many researchers had found out that ratings and other customers’ reviews can be manipulated and did not reflect on the actual customers’ sentiment on the particular product. This research investigates how sentiment analysis can be used as an alternative solution to measure the positive, negative, and neutral feedback of the past reviews. It is to offer more comprehensive way to help the customers make an informed decision for the product that they wish to buy based on the totality of the reviews. This paper makes a comparative study on sentiment analysis methods on online shopping reviews. This can lead to the proposed theoretical framework of an alternative solution for better insight exploration. It is envisaged that this research would benefit the customer in making a better decision when doing online shopping and may act as a feedback mechanism for the seller to provide good products and services. A good product rating can influence many new buyers and increase business revenue and expansion
Finite-state script normalization and processing utilities: The Nisaba Brahmic library

This paper presents an opensource library for efficient lowlevel processing of ten ma jor South Asian Brahmic scripts.The library provides a flexible and extensible framework for supporting crucial operations on Brahmic scripts, such as NFC, visual normalization, reversible transliteration, and validity checks, implemented in Python within a finitestate transducer formalism.We survey some com mon Brahmic script issues that may adversely affect the performance of downstream NLP tasks, and provide the rationale for finitestate design and system implementation details.
Achieving Model Robustness through Discrete Adversarial Training

Discrete adversarial attacks are symbolic perturbations to a language input that preserve the output label but lead to a prediction error.While such attacks have been extensively explored for the purpose of evaluating model robustness, their utility for improving robustness has been limited to offline augmentation only.Concretely, given a trained model, attacks are used to generate perturbed (adversarial) examples, and the model is re-trained exactly once.In this work, we address this gap and leverage discrete attacks for online augmentation, where adversarial examples are generated at every training step, adapting to the changing nature of the model.We propose (i) a new discrete attack, based on best-first search, and (ii) random sampling attacks that unlike prior work are not based on expensive search-based procedures.Surprisingly, we find that random sampling leads to impressive gains in robustness, outperforming the commonly-used offline augmentation, while leading to a speedup at training time of ∼10x.Furthermore, online augmentation with search-based attacks justifies the higher training cost, significantly improving robustness on three datasets.Last, we show that our new attack substantially improves robustness compared to prior methods.
Teaching Veterinary Anatomy During Covid-19 Pan- demic Time, Challenges and Solutions

How we managed the official suspension of the study in the Universities in Egypt since 14 March 2020 during the first attack of Covid-19 and also during the second Attack, is the point onto which lights will be focused in this essay. Challenges facing the anatomy/histology education were: How to manage the lectures, how to prepare the practical lessons, and how to make the exams specially the practical ones. Two ways were adopted: presenting the power point program with audio comments to the groups of 1st and 2nd year students launched using WhatsApp. Some faculties managed a platform and the lecturers uploaded their power points on it, then the students download it. Instead of the written exams, an essay or small research article for each topic in anatomy/histology the students had studied was decided. The students have given 1.5 month as dead line to send their essays. The practical exams indissecting rooms/ histology labs were replaced by MCQ, True and false, diagrams and short answers questions sheets. The evaluation is either pass or failed. The first end semester exams of this year were postponed to the beginning of the second semester i.e. 20 February 2021. The beginning of teaching the second semester is not yet decided. The general education policy now after this pandemic is a transfer to the electronic exams and corrections and in turn encouraging the universities to establish question banks for each syllabus and to adopt the Blended learning (50% online & 50% face to face).
Scenery Image Extension via Inpainting With a Mirrored Input

Generative image extension has the advantage of extending the overall image size while preserving the target image because, unlike other image extensions using interpolation, it completes the surroundings of the target image. However, existing generative image extension methods tend to have poor quality in the generation of outer pixels. One method deals only with a limited number of scene classes because the extension repeats the same semantics. We propose a mirrored input, which sandwiches the extended region by mirroring a part of the target image. This replaces generative image extension with an image inpainting problem and thus helps to achieve higher quality pixel generation and can extend semantics with more complex shapes than horizontal repetition. Experimental results show that our proposed method achieves a scenery image extension that exceeds the state-of-the-art generative image extension methods in both visual quality and FID score for datasets containing diverse scenes.