Reviewerly/exHarmony huggingface dataset:
Image Enhancement using Recursive Separate Standard Intensity Deviation Based Clipped Sub Image Histogram Equalization

To improve image contrast, this paper introduces a recursive separate standard intensity deviation based clipped sub image histogram equalization method. This is an extension of standard intensity deviation value based sub image histogram equalization algorithm, in terms of histogram separation and equalization. In existing equalization methods do not effectively utilizes the information from different region in equalization process. In this scheme, the image histogram is bisected based on standard intensity deviation value. The further separation is carried out based on the specific region threshold value and the resulting four sub histograms are equalized individually. This is an effective method for enhancing, low exposure, medical and mammogram images and for addressing the over-enhancement problem. The performance evaluation of the proposed method is presented with the help of average information and visual quality assessment and the proposed algorithm outperforms existing recursive algorithms based on histogram equalization.
Indexing of American Football Video Using MPEG-7 Descriptors And MFCC Features

In this work, an application system is proposed to classify American Football Video shots. The application uses MPEG-7 motion and audio descriptors along with MEL Frequency Cepstrum coefficient features to classify the video shots into 4 categories, namely: Pass plays, Run plays, Field Goal/Extra Point plays and Kickoff/Punt plays. Fisher's Linear Discriminant Analysis is used to classify the 4 events, using a leave-one-out classification technique in order to minimize the sample set bias. For a database of 200 video shots taken from four different games, an overall system performance of 92.5% was recorded. In comparison to other American Football indexing systems, the proposed system performs 8% to 12% better. We have also proposed an algorithm that uses MPEG-7 motion activity descriptors and mean of the motion vector magnitudes, in a collaborative manner to detect the starting point of play events within video shots. The algorithm can detect starting points of the play with 83% accuracy.
Machine Learning Empowered Urdu Characters Recognition Mechanism

The study was conducted on digitally written Urdu characters.After a little preprocessing (resizing the images and separating different characters based on the classes), we applied different features extraction techniques and artificial intelligence algorithms on the same dataset and compared the results, to successfully predict digitally written Urdu characters.We managed to classify the data with an accuracy of 98%.But of course, these are results from the best combination of algorithms, feature extraction, and hyperparameter combination.There is still a huge issue of recognizing handwritten characters.Much work has been in previous years to successfully recognize characters but most of the research was done in English characters or digits and very less work is done in recognizing Urdu characters.One of the main reason for this is the same characters has different shapes depending on their position in the word.Moreover, some properties of Urdu like calligraphic nature also cause a lot of problems.To cover all these problems, we studied several feature extraction techniques and algorithms to drive the best possible results.
PENGGUNAAN VIRLENDA DALAM PEMBELAJARAN MENULIS KARYA ILMIAH DI MASA PANDEMI COVID-19 PADA MAHASISWA PGSD

The purpose of the study was to describe the implementation, results, constraints, and responses to the use of VIRLENDA in learning to write scientific papers during the Covid-19 pandemic for PGSD students. The research method uses a descriptive quantitative approach. Data analysis techniques in the study used descriptive analysis and content analysis. The results showed that the implementation of the use of VIRLENDA in learning to write scientific papers during the Covid-19 pandemic for PGSD students took place in a very orderly manner. The results of using VIRLENDA in learning to write scientific papers during the Covid-19 pandemic, PGSD students obtained varied scores. The obstacles found during the use of VIRLENDA in learning to write scientific papers during the Covid-19 pandemic for PGSD students were due to the internet network, the emergence of boredom, difficulty in concentrating, and lack of time management. PGSD students gave a good response to the use of VIRLENDA in learning to write scientific papers during the Covid-19 pandemic.
Ethical Objection on COVID-19 Informed Consent in Bangladesh

The recent unethical COVID-19 informed consent in Bangladesh focuses on the weakness of the unethical healthcare system in this country.After 50 years when Bangladesh celebrated its Golden Jubilee of independence, the Corona crisis opened our eyes once again -how weak the countries' policymaking system is!We try to figure out the ethical gap of the corona vaccines' informed consent which is used by the government of Bangladesh.
Novel Cryptography Technique via Chaos Synchronization of Fractional-Order Derivative Systems

Synchronization of fractional-order-derivative systems for cryptography purpose is still exploratory and despite an increase in cryptography research, several challenges remain in designing a powerful cryptosystem. This chapter addresses the problem of synchronization of fractional-order-derivative chaotic systems using random numbers generator for a novel technique to key distribution in cryptography. However, there is evidence that researchers have approached the problem using integer order derivative chaotic systems. Consequently, the aim of the chapter lies in coding and decoding a text via chaos synchronization of fractional-order derivative, the performance analysis and the key establishment scheme following an application on a text encryption using the chaotic Mathieu-Van Der Pol fractional system. In order to improve the level of the key security, the Fibonacci Q-matrix is used in the key generation process and the initial condition; the order of the derivative of the responder system secretly shared between the responder and the receiver are also involved. It followed from this study that compared to the existing cryptography techniques, this proposed method is found to be very efficient due to the fact that, it improves the key security.
Nonstarters

Most coined words die with their coiner. Failure to attract users is an occupational hazard of neologizing. In the world of neology, nonstarters are the norm. Even coined words that enjoy a brief run seldom last. Thinks of this as <italic>Sniglet syndrome</italic> (for the many cleverly coined “sniglets” that didn’t catch on). When one searches the word “coined” online, it’s striking how many neologisms that at one time looked promising have disappeared. After burning brightly for a time, they fizzled out. Like seasonal kiosks, such terms pop up, do some trade, then vanish. They prove to be <italic>pop-ups.</italic> New words rooted in current events are particularly likely to become pop-ups (e.g. the onetime “word of the year,” <italic>Y2K</italic>). Terms that are time and context-specific seldom have lasting utility. Even success coining one word doesn’t guarantee success coining another. Mencken did far better with <italic>Bible belt</italic> than with <italic>ecdysiast</italic> (stripteaser).
Tree Algorithms

The special properties of trees allow us to create algorithms that are specialized for trees and work more efficiently than general graph algorithms. This chapter presents a selection of such algorithms.
The Impact of Training on Job Performance at General Directorate of Residency and Foreigners Affairs in the U.A.E

Today, organizations are facing challenges related to the job performance. Training is being considered an important factor through which job performance and be increased. This study determines the relationship between training and job performance. For this the survey is conducted at the GDRFA (General Directorate of Residency and Foreigners Affairs) in the UAE. The number of respondents for this study was 387. A quantitative methodology was used to analyze the collected data from the respondents using standard statistical methods with the help of SPSS. The data analysis methods include descriptive statistics, reliability test, Pearson non-directional correlations and Simple Linear regression. The result of this study indicates that training is correlated with employees’ productivity and job performance in a positive, moderate, and causal relationship. The weak job performance is result of unsuitable training programs that leads to poor outcomes from employees at the end of the training. This outcome shows the importance of training on developing the overall performance of employees and improving their productivity in work. Thus, training is an important factor that could enhance the job performance.
Pengukuran Metrik Kompleksitas Model Proses Bisnis Pondok Pesantren

Today's world &nbsp;business organizations make use of digital information systems to provide an understanding of &nbsp;the management of business processes that they run, including boarding schools. To find out the quality of &nbsp;the boarding school business process, a modeling of the business process is carried out which will be used as &nbsp;a project to measure a business process in the boarding school. Therefore, the calculation process is carried &nbsp;out through metric indices in the boarding school business process model. This is a basic c onsideration that &nbsp;is used as a reference for calculating the metrics that can be obtained from modeling existing business &nbsp;processes. In this paper, a calculation of the business process model of the boarding school is carried out &nbsp;through BPMN diagram types.
Image Reconstruction and Per-pixel Classification

We describe face classification algorithm which can be used for object recognition, pose estimation, tracking and gesture recognition which are useful for human-computer interaction. We make use of depth camera (Creative Interactive Gesture Camera – Kinect®) to acquire the images which gives several advantages when compared over a normal RGB optical camera. In this paper we demonstrate a intermediate parsing scheme, so that an accurate per-pixel classification is used to localize the joints. We make use of an efficient random decision forest to classify the image which in turn helps to estimate the pose. As we employ depth camera to acquire depth image it may contain holes on or around depth map, so we first fill those holes and the classify the image. Simulation results was observed by varying several training parameters of the decision forest. We generally learned an efficient method which stems the basics in the development of pose estimation and tracking. Also we gained an intensive knowledge on Decision forests.
Towards Silver Standard Dependency Treebank of Urdu Tweets

Manually annotated corpus is a perquisite for several natural language processing applications including parsing.Nevertheless, annotated corpus is not always available for resource-poor languages, especially when domain under consideration is noisy user-generated data found on social media platforms such as Twitter.To overcome this deficiency of hand-annotated corpus, researchers have focused their attention on semi-automatic corpus annotation methods.This paper describes the experiments carried out using semi-automatic methods like self-training and co-training in an attempt for creating silver-standard dependency treebank of Urdu tweets.Six iterations of each approach were performed using same experimental conditions using MaltParser and Parsito parser, both statistical data driven parsers.For self-training experiments, the best performing MaltParser model was trained on 1250 Urdu tweets, with an accuracy of 70.2% LA, 74.4% UAS, 63% LAS.Whereas the best performing Parsito model was also trained on 1250 Urdu tweets with an accuracy of 70.8% LA, 74.8% UAS, 63.4% LAS.For co-training experiments, best performing MaltParser model was trained on 1500 Urdu tweets, with an accuracy of 70.5% LA, 74.4% UAS, 63.2% LAS.The best performing Parsito model was also trained on 1500 Urdu tweets with an accuracy of 70.5% LA, 74.3% UAS, 63% LAS.Although, there was not much difference between the results of both approaches, co-training results were slightly better for both parsers and is used for generating a silver-standard dependency treebank of 4500 Urdu tweets.
High Efficient Sequences Generate from Developed MCG Generator

It has been before using the mathematical system – the Multipicative Cyclic Group (MCG) - to construct the MCG unit, which is a base unit of MCG systems. In this paper we develop the MCG unit to gain random digital sequences with high efficiency to be used in Stream Cipher Systems. The basic criteria of efficiency are applied to test the digital sequence results from the system which is combined from the proposed unit.
Efficient Search Mechanism from Large Scale Corpora for Domain-Specific Language Modeling in Speech Recognition

With the Internet and the World Wide Web revolution, large corpora in variety of forms are germinating ceaselessly that can be manifested as big data. One obligatory area for the usage of such large corpora is language modeling for large vocabulary continuous speech recognition. Language modeling is an indispensable module in speech recognition architecture, which plays a vital role in reducing the search space during the recognition process. Additionally, the language model that is contiguous to the domain of the speech can dwindle the search space and escalate the recognition accuracy. In this paper, an efficient searching mechanism for domain-specific document retrieval from the large corpora has been elucidated using Elasticsearch which is a distributed and an efficient search engine for big data. This assisted us in tuning the language model in accordance with the domain and also by reducing the search time by more than 90% in comparison to conventional search and retrieval mechanism used in our earlier work. A word level and a phrase level retrieval process for creating domain-specific language model has been implemented. The evaluation of the system is performed on the basis of word error rate (WER) and perplexity (PPL) of the speech recognition system. The results shows nearly 10% decrease on WER and a major reduction in the PPL that helped in boosting the performance of the speech recognition process. From the results, it can be consummated that Elasticsearch is an efficient mechanism for domain specific document retrieval from large corpora rather than using topic modeling toolkits
Particle swarm optimisation representations for simultaneous clustering and feature selection

© 2016 IEEE. Clustering, the process of grouping unlabelled data, is an important task in data analysis. It is regarded as one of the most difficult tasks due to the large search space that must be explored. Feature selection is commonly used to reduce the size of a search space, and evolutionary computation (EC) is a group of techniques which are known to give good solutions to difficult problems such as clustering or feature selection. However, there has been relatively little work done on simultaneous clustering and feature selection using EC methods. In this paper we compare medoid and centroid representations that allow particle swarm optimisation (PSO) to perform simultaneous clustering and feature selection. We propose several new techniques which improve clustering performance and ensure valid solutions are generated. Experiments are conducted on a variety of real-world and synthetic datasets in order to analyse the effectiveness of the PSO representations across several different criteria. We show that a medoid representation can achieve superior results compared to the widely used centroid representation.
Vectors, Tensors and Forms

InVector thisTensor chapterForms we develop the main mathematical concepts used in this book. First vectors, not only as quantities with length and direction, but as differential operators. Then tensors of arbitrary rank are introduced. As a preparation for using Cartan’s formalism we introduce forms, i.e. antisymmetric covariant tensors. This antisymmetric tensor formalism is most effective when we introduce an orthonormal basis field. Using an orthonormal basis co-moving with an observer, i.e. where the time-like vector is equal to the 4-velocity of the observer, simplifies the physical interpretation of the calculations.
Genetic programming for feature construction and selection in classification on high-dimensional data

Classification on high-dimensional data with thousands to tens of thousands of dimensions is a challenging task due to the high dimensionality and the quality of the feature set. The problem can be addressed by using feature selection to choose only informative features or feature construction to create new high-level features. Genetic programming (GP) using a tree-based representation can be used for both feature construction and implicit feature selection. This work presents a comprehensive study to investigate the use of GP for feature construction and selection on high-dimensional classification problems. Different combinations of the constructed and/or selected features are tested and compared on seven high-dimensional gene expression problems, and different classification algorithms are used to evaluate their performance. The results show that the constructed and/or selected feature sets can significantly reduce the dimensionality and maintain or even increase the classification accuracy in most cases. The cases with overfitting occurred are analysed via the distribution of features. Further analysis is also performed to show why the constructed feature can achieve promising classification performance. This is a post-peer-review, pre-copyedit version of an article published in 'Memetic Computing'. The final authenticated version is available online at: https://doi.org/10.1007/s12293-015-0173-y. The following terms of use apply: https://www.springer.com/gp/open-access/publication-policies/aam-terms-of-use.
New Approach for Efficiently Computing Factors of the RSA Modulus

This chapter consists of a compilation of new attacks on the modulus of the RSA cryptosystem. The first attack works when the public exponent e satisfies the modified RSA key equation eX – uY = Z – ϕ b where X, Y, Z are integers and u = ϕ a + ϕ b for ϕ a and ϕ b are the upper and lower bounds of ϕ(N). We show that the modulus N can be factored if X, Y, Z satisfy our given condition. The second attack is an extension of first attack, as we alter the equation into e i X – u i Y i = Z i –ϕ b . We show that the modulus N i = p i q i can be factored simultaneously whenever our given condition is satisfied. The third attack is conducted upon the modulus N = pq with the public key e &lt; ϕ(N) satisfies the key equation ed – kϕ(N) = 1. Applying the continued fraction and continuous midpoint subdivision analysis upon an interval containing (p – 1) (q – 1), we show that N can be factored in polynomial time. The last attack is applicable upon the modulus N = p 2 q where the primes share a known amount of Least Significant Bits (LSBs). Utilizing the given information to build a lemma, we substitute it into the equation ed – kϕ(N) = 1 where ϕ(N) = p(p – 1) (q – 1), then we build an integer polynomial. We manage to find the roots of the polynomial by using the Jochemsz-May strategy and thus factor the modulus N.
Bounds on Complexity when Sorting Reals

We derive the upper bounds on the complexity of the counting sort algorithm applied to reals. We show that the algorithm has a time complexity O(n) for n data items distributed uniformly or exponentially. The proof is based on the fact that the use of comparison-type sorting for small portion of a given data set is bounded by a linear function of n. Some numerical demonstrations are discussed.
Statistical Analyses in Effective Usage of English Grammar of Engineering Students in Communication

Computer Science Engineering students require good command over English language to get the right placement after the course. Many fail to get the employment as they are not good at communication. English Grammar plays a vital role in making one to confident in their communication. The present paper focuses on statistical analyses of deviations in English grammar of computer science engineering students in coastal Andhra region. The analyses will help the students to concentrate on their mistakes well as the faculty members to minimize the deviations.
Sentiment Analysis

We've already demonstrated how to train a character-level RNN to create original text. Now, we create a word-level RNN to analyze sentiment.
Using V2X communications and data fusion to achieve lane-level positioning for road vehicles

Precise positioning is a key issue for road vehicles in navigation, safety, and autonomous driving applications. While global positioning system (GPS) is widely accepted, it is still a challenge to achieve lane-level positioning. In this work, we consider the fusion of multi-sensory data using particle filter (PF), which is flexible in integrating different information in complex outdoor environments. We focus on three types of popular sensors: controller area network (CAN bus), GPS, and roadside camera. We propose a PF model that can adopt these types of sensory inputs for vehicle positioning. We show that in scenarios where vision sensory inputs are available, lane-level precision can be achieved. When there is no vision coverage, seamless localisation with reasonable precision can still be supported by GPS. Field trial results are presented to validate our model.
Trace Elements and Nd Isotope of Apatites in the Early Mesozoic Granitoids in the North China Craton: A Window to Constrain Petrogenesis

Apatite, as a accessory mineral in igneous rocks, has recently been used in constraining petrogenesis and thermaltectonic process within the Earth sciences.This paper reports the trace elements and Nd isotopic data of apatites from the early Mesozoic granitoids in the northeast North China Craton (NCC) to constrain their petrogenesis.The comparisons between the whole-rock and apatite chemical compositions indicate that linear relationship can be observed between their Sr/Y ratios and Nd isotopic compositions etc., i.e., geochemical data of these apatites in the granitoids can be used to constrain petrogenesis.The apatites from the Late Triassic granitoids are characterized by enrichment in light rare earth elements (LREEs) and depletion in heavy rare earth elements (HREEs), and have (La/Yb) N ratios of 8.3-35.7 and Sr/Y values of 0.33-2.83,indicating that the Late Triassic granitoids have an affinity to adakitic rocks.Combined with εNd(t) values (-14.4 to -9.2) and T DM2 ages (2168 to 1739 Ma) of these apatites, we conclude that the Late Triassic granitoids in the northeastern NCC were derived from magma generated by partial melting of a thickened ancient lower continental crust.Compared with the apatites from the Late Triassic granitoids, those from the Early Jurassic granitoids have relatively high HREE abundances and low LREE/HREE ratios (1.2 to 4.2), low Sr/Y ratios (0.02-0.05), as well as relatively high εNd(t) values (-9.3 to -6.7) and low T DM2 ages (1699 to 1502 Ma), indicating that the Early Jurassic granitoids were derived from partial melting of the Paleoproterozoic-Mesoproterozoic lower continental crust with normal thickness.
The Implementation of Web-Based Financial Statements Application in Islamic Education Foundations

Islamic Education Foundation is a non-profit entity consisting of MA, MTs, Vocational Schools, Foundations, and Islamic education foundations. This study aims for a web application that can be used in the financial data processing of the Al-Mujaddadiyyah Islamic Education Foundation replacing manua