Reviewerly/exHarmony huggingface dataset:
Sample Bias Effect on Meta-Learning

Sample bias is a common issue on traditional machine learning studies but rarely considered when discussing meta-learning. It happens when the training data sample lacks or overemphasizes one or more characteristics, compared to others. Herewith, models trained on such data may become inaccurate for some instances. This work aims to analyze this issue in the meta-learning context. Indeed, in most of the meta-learning literature, a random sample of datasets is taken for building meta-models. Nonetheless, there is no discussion over a possible side-effect bias not controlled in such random sampling. This work aims to analyze these effects, in order not only to discuss their consequences, but also to start a debate over the need of their consideration in meta-learning research.
Strengths and Challenges of Distance Learning Modalities in the New Normal: Basis for Intervention Program

The study was conducted to identify the strengths and challenges of distance learning modalities as a basis for intervention programs from a memorial high school in a schools’ division in Bulacan, Philippines. The researcher used a descriptive survey type of study. For the sample of the study, it employed eight (8) teachers and 39 learners from a junior high school chosen randomly. A self-made checklist survey questionnaire was created by the researcher which was validated by a panel of experts. Descriptive statistics were used to analyze the collected data with the help of a Microsoft Excel sheet. Results revealed that teachers and learners have a variety of strengths in distance learning modalities. In addition, teachers and learners also stressed the different challenges they encountered for distance learning modalities. Based on these findings of the study, the researcher used them for a possible formulation of an intervention program that aimed to providing solutions for the challenges of distance learning and also enhancing the strengths for both the teachers and learners.
Analogy

The paper is devoted to the concept of analogy. We consider the peculiarities of its use in the history of philosophy, starting from Antiquity, from the school of Pythagoras, which is associated with the origin of this term. The use of analogy by Plato, Aristotle, Renaissance and Modern philosophers is discussed. The definition of analogical inference as a special type of plausible inference is given. The types of analogical inference and the corresponding examples are listed. We also consider the use of reasoning by analogy in the field of mathematics, law, and in scientific research in general. Modeling as one of the most effective research methods is directly related to analogy. Analogy is based on the concept of similarity, and it has its own specifics in various scientific fields. In mathematics and logic, similarity is expressed in concepts such as isomorphism and homomorphism. The analysis of these concepts is provided in the corresponding section. Bionics is based on the analogy between living organisms and technological devices. Some examples of inventions, which were motivated by bionics ideas, are given. The paper also raises the question about the relationship of such concepts as analogy and metaphor, and indicates their essential differences.
Proposed Self – Regulated Gray Wolf Optimizer based Extreme Learning Machine Neural Network Classifier for Lung Cancer Classification

The major issue in the development of pattern recognition towards lung cancer classification is the formation of feature extraction process and the proposed classifier model. In the proposed approach, a self-regulated gray wolf optimizer based extreme learning machine classifier is proposed to carry out lung cancer classification along with the statistical feature extraction methods. Simulation shows that the proposed approach works well and produces higher classification accuracy than the conventional classifier methods. The modeled SelfRegulated Gray Wolf Optimizer (SRGWO) and Extreme Learning Machine (ELM) along with feature and segmentation process shows highest improvement in comparison with the other existing literature studies in neural networks. In particular, the significant finding of this work employing ELM, SRGWO and feature analysis validates the correlation of Computed Tomography (CT) measures as well as classification pathological parameters. Thus, the proposed SRGWO and ELM classifier is developed in the present approach for lung cancer classification of CT images reducing the computational cost and time of all the earlier classifiers and as well increasing the classification accuracy. On performing trial runs for the proposed SRGWO – ELM to compute the classification results for the considered real time and Lung Image Database Consortium (LIDC) lung images, it has been noted that at certain trials, the extreme learning machine neuronal classifier is noted to get stuck up with the local minima problem and it is necessary to restart the generation process to achieve classification solutions.
ISO 9126 UNTUK PENGUJIAN GAME EDUKASI MEMBUANG SAMPAH BERBASIS ANDROID

Games are applications that are familiar to people from all walks of life. Efforts to increase the efficiency of providing applications that contain educational elements require a variety of alternatives and new innovations in programming to be applied as a tool to simplify the learning process. This study aims to create a game design called the game "Throw your trash" by inserting educational elements about how to dispose of garbage in its place and introducing the types of waste. And the sample used is elementary school students, because of their interest in the theoretical learning process, considering that children prefer to play games. The methodology used is starting from system analysis, design and implementation stages, and testing using ISO 9126.The results of this educational game to throw garbage show that the game application based on the aspect obtained a percentage of 95% of the Functionality test and the results of the analysis of usability testing obtained a percentage of 81.89% Kata Kunci: education, games, testing, android, ISO 9126
Identifying Gender From Images of Faces

The objective of this project is to identify the gender of a person by looking at his/her photograph. This is a case of supervised learning where the algorithm is first trained on a set of female and male faces, and then used to classify new data. We have not taken genders other than Male and Female into account. A preliminary algorithm is run to make sure that an image is that of a human before classification begins.
Corrigendum: An Evaluation of Strong-Motion Parameters at the S-Net Ocean-Bottom Seismograph Sites Near the Kanto Basin for Earthquake Early Warning

Warning. Front. Earth Sci. 9:699439. doi: 10.3389/feart.2021.699439 In the original article, there was a mistake in the legend for S-net buried and S-net non-buried stations in Figures 4, 5, and 6. The symbols for the S-net buried and S-net non-buried stations were mistaken for each other. The correct legend appears below. The authors apologize for this error and state that this does not change the scientific conclusions of the article in any way. The original article has been updated.△ S-net non-buried ▽ S-net buried
Perceptual Image Quality Assessment Technique for Color Images based on HSI Colour Space

A new full-reference Perceptual Image Quality Assessment (PIQA) technique based on Hue-Saturation-Intensity (HSI) transformation method for colour images is offered in this work. Fundamentally, it is combined with three approaches (colour transformation, histogram processing and Human Visual System (HVS) based weighting) and it uses the Discrete Cosine Transform (DCT) domain. The PIQA is composed of four phases. At the first phase, a colour image is transformed using the HSI because an important characteristic of eyes reaction to intensity of light and colour knowledge is used for quality assessment processes. All channels' DCT coefficients have been calculated at the second phase, because many specifications cannot be assessed in the spatial domain. At the third phase, histogram based quality assessment results are obtained by using histogram of each channel (Hue, Saturation and Intensity). These results are weighted for obtaining perceptual quality result taking into account the HVS specifications at the last phase because the human eye has different sensitivities to the intensity and the others (i.e., hue and saturation). Experimental outcomes about feasibility of the PIQA on test images under different deteriorations, both sensible by the HVS and with the same Peak Signal to Noise Ratio (PSNR) result are offered. The PIQA shows better performance in comparison to state-of-the-art techniques
Research of Novel Web Page Classifiers and Feature Selection Methods

The World revolves around the web technology at present. Every year, the Web information are exponentially growing and this information are huge and complex. The web users are difficult to classify and extract useful information from the web, because the Webinformation are noisy, redundant and irrelevant and also misclassified.Many researchers don’t have strongknowledge about the process of web page classification, techniques and methods previously used. The objective of this survey is to convey an outline of the modern techniques of Web page classification. In this survey, the recent papers in this area are selected and explored.Thus this study will help the researchers to obtain the required knowledge about the current trends in web page classification
Using images of noise to estimate image processing behavior for image quality evaluation

Noise is an extremely important image quality factor. Camera manufacturers go to great lengths to source sensors and develop algorithms to minimize it. Illustrations of its effects are familiar, but it is not well known that noise itself, which is not constant over an image, can be represented as an image. Noise varies over images for two reasons. (1) Noise voltage in raw images is predicted to be proportional to a constant plus the square root of the number of photons reaching each pixel. (2) The most commonly applied image processing in consumer cameras, bilateral filtering [1], sharpens regions of the image near contrasty features such as edges and smooths (applies lowpass filtering to reduce noise) the image elsewhere. Noise is normally measured in flat, uniformly-illuminated patches, where bilateral filter smoothing has its maximum effect, often at the expense of fine detail. Significant insight into the behavior of image processing can be gained by measuring the noise throughout the image, not just in flat patches. We describe a method for obtaining noise images, then illustrate an important application&#x2014; observing texture loss&#x2014; and compare noise images for JPEG and raw-converted images. The method, derived from the EMVA 1288 analysis of flat-field images, requires the acquisition of a large number of identical images. It is somewhat cumbersome when individual image files need to be saved, but it&#x2019;s fast and convenient when direct image acquisition is available.
Delivering

Automated materials handling and RFID technology are becoming more common in libraries. Libraries are using supply chain technology to enhance both patron and staff user experiences. This article discusses the strategies and experiences we encountered bringing different departments together, revising workflows and establishing user-focused processes as we transitioned from a manual, barcode centered system to an autmated, RFID focus.
Chaotic Binary Sequence Generator based on Logistic Map

Pseudorandom binary sequences find various applications in different areas such as security, communication, steganography and cryptography. The properties like sensitivity to initial condition, ergodicity, mixing property and dynamic behavior are used in the designing of random number generators known as chaotic systems. In this study, an efficient chaotic binary sequence generator using logistic map is proposed, implemented and analyzed. The proposed binary sequence generator generates 50 chaotic sequences by varying initial condition with fixed system parameter. Subsequently, the generated sequences are transformed to binary sequences using thresholding method. The output of binary sequences is statistically tested with FIPS 140-2 test suite in order to identify the specific properties expected for truly random binary sequences. The experimental results prove that the generated binary sequences possess identical characteristics of true random numbers and can pass all tests of FIPS 140-2 test suite.
Detecting and Resolving Conflicts in Adaptation Rules for the Context Aware System

Context aware systems are embedded in the environment and provide services anytime and everywhere. Context aware system is built upon context information gathered from the environment and the adaptation rule which describes what need to be performed for the context information gathered. Consistent behavior of the context aware system will be ensured by the complete and correct context information and adaptation rules. There may have a situation where conflicts may occur at context level and adaptation rule level. Many researchers presented different methods to detect and resolve conflict in context information level. In this work we proposed a procedure to detect and resolve conflicts in the adaptation. Two different types of conflicts are addressed in this work. We have discussed about the procedure to resolve these two types of conflicts in the adaptation rule.
Dynamic Bit Coin Value Prediction

Bitcoin is online money that is utilized worldwide to make online installments. It has thusly become a venture vehicle in itself and is exchanged a route like other open monetary forms. The capacity to foresee the value change of Bitcoin would in this way encourage future venture and installment choices. The objective of this paper is to learn with what exactness the bearing of Bitcoin cost in USD can be anticipated. The value information is sourced from the Bitcoin Price Index. The errand is accomplished with changing degrees of achievement through the usage of a Bayesian streamlined intermittent neural system (RNN) furthermore, a Long Short Term Memory (LSTM) arranges. The LSTM accomplishes the most noteworthy order precision of 59%.
Encryption and Decryption using Hybrid Cryptography Techniques and Multi-level Steganography

Data Encryption, in the present time, is used to deter malicious parties from accessing sensitive data, allowing the access to only authorized parties as it uses the key to safeguard the sensitive information to be transferred. Various cryptography techniques are used to maintain confidentiality and integrity of the data, and this paper proposes the hybrid scheme of using four layers of encryption including steganography to safeguard the authenticity of the information to be transferred. In this paper, three conventional key cryptography algorithms, which are Fibonacci series, XOR cipher and PN sequence encryption, are used along with RSA cryptography which is a public key cryptography algorithm. In addition to it, steganography technique is also used in the last two layers of our proposed model. Firstly, the whole of the encrypted string, obtained after the above cryptography algorithms, is hidden inside the first image. Now this encrypted host image is further hidden inside another image by using one bit LSB algorithm. This model offers the double security of the data as here the data is not only encrypted but also hidden in an image which again is hidden inside another image making it very hard even to detect the data.
Towards Enhanced Anomaly Object Detection and Face Recognition (EAODFR) in Surveillance Videos using Recurrent Neural Networks

In the present decade, anomaly object detection and face recognition from surveillance videos from diverse environments have become interesting and challenging research areas in computer vision. This paper works on developing an Enhanced Anomaly Object Detection and Face Recognition (EAODFR) model using Recurrent Neural Networks (RNN). Moreover, fractional derivative based background separation has been incorporated for framing efficient background subtraction model and foreground segmentation with appropriate pixel definitions on each frame of the surveillance videos. The Region of Interest detection has been done using optimal thresholding and for detecting anomaly objects. Further, efficient face recognition has been accomplished by designing the Recurrent Neural Networks (RNN), which is implemented with Long Short-Term Memory (LSTM). The recurrent NN are trained in terms of determining anomalous objects using the extracted features in the each frame of the video. The obtained results are analyzed in terms of precision, recall and f-measure and compared with some existing face recognition models. The comparative analysis provides better results and outperforms others.
Online Fraud Review Detection Using Data Mining

Online reviews have great impact on today’s business and commerce. Decision making for purchase of online products mostly depends on reviews given by the users. Nowadays, there are a number of people using social media opinions to create their call on shopping for product or service. Opinion Spam detection is an exhausting and hard problem as there are many faux or fake reviews that have been created by organizations or by the people for various purposes. They write fake reviews to mislead readers or automated detection system by promoting or demoting target products to promote them or to degrade their reputations, opportunistic individuals or groups try to manipulate product reviews for their own interests. This paper introduces some semi-supervised and supervised text mining models to detect fake online reviews as well as compares the efficiency of both techniques on dataset containing hotel reviews.
A Cloud-Based Distributed Platform for Secured EPUB EBOOK Contents

Digital content creators who also own their digitally created content Utilizing Digital Rights Management or DRM in order to safeguard and govern the distribution and usage of their digital contents.The features of digital content protection (such as ebook) ranges from content limitation or transfer, and reading permission to authorized reading hardware or Software, to corporate delivery policies involving identification of users and devices for a definite period.Until today, standard for EPUB does not provide a specific DRM standard to protect eBook copyrights.In this paper, we propose a cloud-based EPUB content protection and control platform and describe the proposed ways in protecting EPUB contents through a cloud-based solution for digital content protection and access regulation.We have implemented a prototype with OpenStack open source cloud computing platform enforce access control and efficient in data management with data-at-rest encryption.
A cloud computing price model based on virtual machine performance degradation

This paper reports the interference effects in virtual machines performance running higher workloads to improve the resources payment in cloud computing. The objective is to produce an acceptable pay-as-you-go model to be used by cloud computing providers. Presently, a price of pay-as-you-go model is based on the virtual machine utilised per time. However, this scheme does not consider the interference caused by virtual machines running concurrently, which may cause performance degradation. In order to obtain a fair charging model, this paper proposes an approach considering a recovery over the initial price considering the virtual machine performance interference. Results showed benefits of a fair pay-as-you-go model, ensuring the effective user requirement. This novel model contributes to cloud computing in a fair and transparent price composition.
Data Science Recommendation System using Semantic Technology

Data Science is a field in which multidisciplinary blend of data inference, algorithm development, and technology are combined in order to solve analytically complex problems. Data science field tend to focus on a complex and large algorithmic technological problem having data at the core. Students at the starting of the learning phase don’t know all the technological and algorithmic aspects related to data science. Consulting through faculty or knowing from external source helps them to proceed towards the needed expertise that they want to gain. Data science recommendation system using semantic web data-science ontology and service-oriented architecture is proposed in our work to recommend students the appropriate resources for their queries. Recommending student significant information concerning books, on-line documentation, software tools, public code repositories, and experts, tutors to be contacted based on student query or previous exam scores is the main objective of the work. Ontology based service-oriented web platform with a conversational user interface that use NLP (Natural Language Processing) to recommend possible resources is proposed and tested to be efficient
DWT Based Image Steganography with Seven Segment Display Pattern as a Key

Steganography is one of the commanding and commonly used methods for embedding data. Realizing steganography in hardware supports to speed up steganography. This work realizesthe novel approach for generation of Key, for hiding and encoding processes of image steganography using LSB and HAAR DWT.The data embedding process is realized with seven segment display pattern as a secret key with various sizes using HAAR DWT and LSB. Maximum hiding effectiveness is also attained from this work. The same is implemented in hardware using reconfigurable device Field programmable gate array to improve the speed, area and power. The proposed work is also evaluated improved PSNR using MATLAB.
Impact of Transformational Leadership (Idealized Influence, Inspirational Motivation, Intellectual Stimulation, Individualized Consideration) on Employee Performance

This study employs structural equations modeling via PLS to analyze the 732 valid questionnaires in order to assess the proposed model that is based on the transformational leadership characteristics to identify its effect on the performance of employees in the government sector in Dubai. The main independent constructs in the model are idealized influence, inspirational motivation, intellectual stimulation, and individualized consideration. The dependent construct is employee performance. The study will describe relations among the various constructs. Our work has improved our insight in the importance of transformational leadership. Results indicated that all four independent variables significantly predicted employee performance with a various percentage. The proposed model explained 37% of the variance in employee performance