Reviewerly/exHarmony huggingface dataset:
An ANN Approach of Twisted Fiestel Block Ciphering

The twenty-first century is a digital era when bulk volume of data is engendering per second over the cyber world which is somehow an indispensability of digital life. So, for the purport of its auspice from unauthorized betokens, sundry encryption and decryption techniques were engendered which are predicated on different aspects of bulwarking the data. One of them is the Feistel cipher algorithm which is predicated on symmetric key cryptography where a single key is utilized for both encryption and decryption of the text. In this method, there is immensely colossal drawback that the Feistel cipher only transmutes a component of the internal state in each round, i.e., after user feeds the data, only half part of the main text is manipulated in each round which as a result is bit insecure as the left half is the pristine text for the very first round. So, to surmount this issue we have proposed one algorithm predicated on Feistel cipher which transmutes the entire internal state in each round and makes the data entered by user more secure. Not only that, our method is more time-efficient than that of the traditional Feistel cipher. In this paper, we introduced an incipient convoluted structure where our main motive is to make an algorithm which is more expeditious, lighter and time preserver than the traditional Feistel cipher.
Efficient prediction of faults in embedded systems with huge data using enhanced superior incongruity detector

Embedded systems are integrated in most modern and nano scale systems. They help these systems perform their functions accurately and quickly. Since embedded systems deal with systems with large data, they suffer from multiple types of errors. The asymmetric detector was an early indicator of errors in embedded systems. The asymmetric detector constructed ‘self’ templates and then matched them with ‘non-subjective’ samples for a sequence of operations. This construction occurred in the stages of training and testing. The main objective of this paper is to propose an improvement of the functions of the asymmetric detector so it not only matches exact sequences but also determines the sequences with the same characteristics so it can choose the most effective sequence. The proposal is for an algorithm that is merged with the normal incongruity detector. The proposed algorithm calculates the evaluation of the test data and the normal database data for the prediction. It then finds the most similar data between the testing database and the normal database. The results show that the proposed method is more effective than previous efforts in detecting defects in embedded systems by measuring the degree of similarity between the original and the target data.
Determinant Factors and Adaptation Features of Mobile Personalized Learning System

Abstract The mobile learning field is grown and enhanced to a more sophisticated platform for the last decades. It quickly becomes pervasive among educational stakeholders. Nowadays, both students and teachers are familiar with mobile devices to complete their tasks and assignments. A personalized system is one of the features contributing to the mobile learning system’s ease and effectiveness. This paper aims to identify the determinant factors and adaptation features from the previous works in this field. There are 30 papers to be included in the analysis using a coding strategy. The result elicits some determinant factors, including prior knowledge in using mobile devices, the ability to access the technology, and the existence of guidance from an expert. Also, several common adaptation features include user preferences, learning behavior, performance, and mobile-specific adaptation features such as device characteristics and content metadata. Study limitations and future directions are also discussed.
Collaborative Relationships in E-resource Acquisition and Delivery: An Examination of Select Academic Libraries in Jamaica

Libraries within higher education institutions in Jamaica make tremendous use of information communication technology in their efforts to provide their users with access to electronic resources. Faced with shrinking budgets, currency exchange issues, and the increasing costs of electronic resources, it has become extremely challenging to fund these expenditures. These libraries must demonstrate value to parent institutions through the provision of electronic resources in support of online program offerings. Faced with an ongoing cycle of assessment for accreditation at the program or university level, libraries are also pressured to ensure that their collections are current and provide full coverage. Librarians have had to find innovative methods to maintain their current resources and, where possible acquire new ones amid these challenges.This paper highlights how libraries in higher education institutions in Jamaica have sought through collaboration to acquire and deliver quality electronic resources to their stakeholders.
Mamdani Modeli tabanlı ANFIS ve Emisyon Tahmininde Uygulanması

This paper proposed Mamdani-based Adaptive Neuro-Fuzzy Inference System (MANFIS).In literature, there are very applications of Sugeno Adaptive Neuro-Fuzzy Inference System (ANFIS) because of simplicity of the Sugeno defuzzification step.Mamdani defuzzification step is linguistic but the Sugeno defuzzification step has constant and linear functions.So, Mamdani parameters training algorithms given in the open literature are not efficient and give worse results when compared to the Sugeno ANFIS.The proposed Mamdani ANFIS is tested for an equation and to predict vehicle soot emission that soot emission is effective at global warming and melting of sea ice in the Arctic.The proposed Mamdani ANFIS is compared to the Sugeno ANFIS for Least Square Estimation method and Gradient Descent method.The training results show that The Mamdani ANFIS consumes less time and needs less epoch number.It is determined that for Gradient Method, the proposed Mamdani has less training error.
Design and Implementation of Occlusion Image Recognition Algorithm Based on Deep Convolution Generative Adversarial Network

Abstract In the image recognition process, the accuracy of image recognition is affected due to partial coverage or light problems. This paper proposes a method to solve the problem of target occlusion based on a deep convolution generation confrontation network. The method actively occludes the feature map after the feature is extracted to generate the confrontation sample, and generates a map and a mask from the input image. At the same time, a method is proposed. This new loss function applies the algorithm to handwriting recognition and trains the network through large-scale sample data sets. Experiments show that this method significantly improves the accuracy of image extraction.
An Adaptive Fusion Model Based on Kalman Filtering and LSTM for Fast Tracking of Road Signs

The detection and tracking of road signs plays a critical role in various autopilot application. Utilizing convolutional neural networks(CNN) mostly incurs a big run-time overhead in feature extraction and object localization. Although Klaman filter(KF) is a commonly-used tracker, it is likely to be impacted by omitted objects in the detection step. In this paper, we designed a high-efficient detector that combines ThunderNet and Region Growing Detector(RGD) to detect road signs, and built a fusion model of long short term memory network (LSTM) and KF in the state estimation and the color histogram. The experimental results demonstrate that the proposed method improved the state estimation accuracy by 6.4% and enhanced the Frames Per Second(FPS) to 41.
Desain Embung Berbantu Komputer Di Kali Sabi Kota Tangerang Banten

The problem of flooding in the city of Tangerang is a problem that requires further treatment. Improper management of water resources is one of the factors that cause flooding which results in losses for the community.The activity carried out as an effort to prevent floods is the creation of water retention. The purpose of this research is to know the flood discharge in Kali Sabi, know volume that must be accommodate, determine the capacity for water retention, produce an effective technical water retention design, and obtain an estimated cost of making the water retention. This research was conducted in April-July 2020 in Uwung Jaya Village, Tangerang City using topographic and rainfall data for 12 years. Rain distribution using the Log Pearson III . The results of the analysis revealed that the effective rainfall of the 5-year return period was 66.314 mm with a maximum flood discharge of 83.69 m³. Volume runoff that had to be overcome was 1084.64 m³. The effective storage capacity of the water retention is ± 1975 m³ with an area of ± 525 m², with construction materials, reinforced concrete with K-225 quality and 10 mm diameter reinforcement. Water retention has two steel sliding gates at the inlet and outlet. Construction of the water retention is estimated to cost Rp. 813,839,000.00.
Security Policy Based Network Infrastructure for Effective Digital Service

In this research a secured framework is developed to support effective digital service delivery for government to stakeholders. It is developed to provide secured network to the remote area of Bangladesh. The proposed framework has been tested through the rough simulation of the network infrastructure. Each and every part of the digital service network has been analyzed in the basis of security purpose. Through the simulation the security issues are identified and proposed a security policy framework for effective service. Basing on the findings the issues are included and the framework has designed as the solution of security issues. A complete security policy framework has prepared on the basis of the network topology. As the output the stakeholders will get a better and effective data service. This model is better than the other expected network infrastructure. Till now in Bangladesh none of the network infrastructure are security policy based. This is needed to provide the secured network to remote area from government.
Calibration of solar radiometers with traceability to the world radiometric reference using an absolute cavity radiometer

This study presents the actual field results from a calibration procedure of reference solar radiometers using an Absolute Cavity Radiometers (ACR), which is directly traceable to the World Radiometric Reference (WRR) scale maintained by the World Radiation Center (WRC). The pyranometer and pyrheliometer traceability is established using Eppley’s AHF-AWX ACR as per the prescribed standards. The Eppley solar radiometers were exposed to solar irradiance for 3.5 years and the Hukseflux solar radiometers were exposed to solar irradiance for 1.5 years. The deviation of the new sensitivity values from the old sensitivity values is reported to be 5.42% (pyranometer, Eppley PSP), −0.21% (pyranometer, Hukseflux SR25), 0.23% (pyrheliometer, Eppley NIP) and 0.4% (pyrheliometer, Hukseflux DR03). The uncertainty values are found to be ±0.04 µV/Wm−2, ±0.05 µV/Wm−2, ±0.03 µV/Wm−2 and ±0.03 µV/Wm−2 for Eppely PSP, Hukseflux SR25, Eppley NIP and Hukseflux DR03 solar radiometers, respectively.
An Approach to Run Pre-Trained Deep Learning Models on Grayscale Images

Transfer learning helps the performance of a learning algorithm significantly when training deep learning models on challenging datasets. However, the pre-trained networks have certain constraints in terms of their architecture. For example, due to the wide availability of color images, state-of-the-art pre-trained networks expect an input image with three color channels. Grayscale images have small sizes as compared to color images and thus can enable real time computer vision applications in scenarios where there are constraints on device memory and bandwidth. Therefore, in this work we propose an approach to run pre-trained models on grayscale images for image classification tasks. We have used the VGG16 pre-trained model to classify Kaggle Dogs vs. Cats dataset. We have compared our results with VGG16 applied on color images. Our results have shown that when the weights for the first hidden layer are initialized as the mean of the pre-trained network weights then the classification accuracy with only 0.04% error can be achieved. Our analysis has shown that comparable benefits can be reaped when using grayscale images for deep learning based classification tasks with only one-third of the bandwidth and storage requirements.
Text-based visual question answering with knowledge base

Text-based Visual Question Answering(VQA) usually needs to analyze and understand the text in a picture to give a correct answer for the given question. In this paper, a generic Text-based VQA with Knowledge Base (KB) is proposed, which performs text-based search on text information obtained by optical character recognition (OCR) in images, constructs task-oriented knowledge information and integrates it into the existing models. Due to the complexity of the image scene, the accuracy of OCR is not very high, and there are often cases where the words have individual character that is incorrect, resulting in inaccurate text information; here, some correct words can be found with help of KB, and the correct image text information can be added. Moreover, the knowledge information constructed with KB can better explain the image information, allowing the model to fully understand the image and find the appropriate text answer. The experimental results on the TextVQA dataset show that our method improves the accuracy, and the maximum increment is 39.2%.
THE EFFECT OF THE IMPLEMENTATION OF CHAT ROOM-BASED BLENDED LEARNING ON STUDENTS' MOTIVATION AND LEARNING OUTCOMES

Motivation and good learning outcomes are important aspects that students must have.Several studies show that students have low motivation and learning outcomes.It is necessary to improve the learning model to overcome this problem.One of the efforts that can be taken is the application of the Chat Roombased Blended Learning model.This study aims to determine the effect of the application of the Chat Roombased Blended Learning model on student motivation and learning outcomes in the human respiratory system material.This is a Quasi-Experimental design with a Pretest-Posttest Non-Equivalent Control Group Design model.The research subjects were 68 students who were divided into two groups (experimental and control) where each group consisted of 34 students.The experimental group used the Chat Room-based Blended Learning model, while the control group learning was carried out using the Blended Learning model.This research was conducted at a high school in Meulaboh, West Aceh Regency, Indonesia.This research was conducted for two months (January-February 2020).The data collection parameters in this study were the learning outcomes and student motivation.Data collection was carried out through pretest and posttest.The test for differences in learning motivation and learning outcomes was analyzed using parametric statistics, namely the ANACOVA test.The results showed that the application of the chat room-based blended learning model had a positive contribution to increasing student motivation and learning outcomes.There needs to be a further study of the learning process with a chat room-based blended learning model.
Research on the Technology Realization of Network Mass Information Processing Platform

Abstract In recent years, With the rapid spread and development of the Internet, especially the huge reserve of information and convenient search function, it attracts more and more of the community to the Internet users ranks. But these need supports of high-performance computer technology. Therefore, in order to enhance the capabilities of Internet vast amounts of information intelligent parallel processing, and promote the comprehensive development of the overall system of information storage, management, integration and intelligent processing, data mining, information retrieval and utilization functions, I proposed a massive information processing platform based on high-performance data mining. The one hand, it analyzes the key technologies to achieve the platform; the other hand, it gives a brief introduction to its hotspot applications. Therefore, it provides a reference and guidance with real-world applications for the technology of high performance and highly efficient network of mass information processing platform.
First-order ASP programs as CHR programs

We present in this paper a way to use the paradigm of Answer Set Programming (ASP) into the Constraint Handling Rules (CHR) paradigm. We present a translation of the ASP language to the Constraint Handling Rules language.
A novel framework for reservoir computing with inertial manifolds

Reservoir computing based on machine learning has garnered significant attention in the field of research regrading time series prediction. Active discussions that were recently held on the theoretical background aided the reservoir to realize some desired properties. In this study, we propose a reservoir computing framework based on the theory of inertial manifolds. Using the theoretical results for infinite-dimensional dynamical systems, we first introduce a new formulation of the echo state network as an extension of our previous work on multivariate input.
A 2.44 Tops/W Heterogeneous DCNN Inference/Training Processor for Embedded System

Since Deep Convolutional Neural Network (DCNN) training involves complex computations and data transmissions, the previous DCNN processors hard to achieve ideal energy efficiency. This paper proposed a DCNN processor supports both inference and training for the embedded system. The processor contains three heterogeneous cores to provide distinct computation patterns and dataflow for different training phases. In addition, since inference takes up more than 90% of the workload of the DCNN application, the three cores of the processor can be reconfigured to efficiently support the inference to achieve leading resources Utilization. The processor is fabricated in 55nm CMOS technology, post-layout simulation shows the processor achieving 1.36 Tops/w energy efficiency for training and 2.44 Tops/w for the inference.
Exact and Convergent Iterative Methods to Compute the Orthogonal Point-to-Ellipse Distance

Computation of the orthogonal distance from a given point to an ellipse is the basis of orthogonal distance based ellipse fitting methods. The problem of this orthogonal distance and the corresponding orthogonal contacting point on the ellipse is investigated, and two algorithms, the exact one and the convergent iterative one, are proposed. The exact algorithm utilizes the closed form solution of quartic equations, but is numerically unstable. The iterative algorithm, however, uses Newton's method to solve the equation, and starts from an initial solution that is proven to lead to a convergent iteration. The proposed algorithms are compared in experiments with an existing rival. Although the rival algorithm is slightly faster and more accurate in realistic scenarios, divergence is likely to occur. On the other hand, both our exact and iterative algorithms can reliably produce the solution needed. While the exact algorithm encounters numeric instability, the iterative algorithm is only slightly outperformed by the existing rival in speed and accuracy, but at the same time provides more reliable computation process, thus making it a preferable method for the task.
Area Partitioning by Intelligent UAVs for effective path planning using Evolutionary algorithms

Unmanned Aerial Vehicles (UAVs) have emerged as one of the most researched topics in recent times. They are being deployed in ample applications without any human intervention. The use of autonomous UAVs has given rise to many new challenges. Path planning has always been one of the most critical problems when it comes to the deployment of UAVs in real-time applications. The current work focuses on the area partitioning algorithm considering a few significant parameters such as static obstacles and altitude of UAVs. For any given geographic area, the proposed area partitioning algorithm finds a division of the entire area of interest by effectively partitioning into rectangles. The algorithm merges two or more equal-sized rectangles based on the obstacle position, resulting in a rectilinear partitioning of the area. Two mid-points are computed for each partition and are further used for the computation of an optimal path. An optimal path for the UAV is found by constructing a graph from the mid-points, posing the problem as Travelling Salesperson Problem(TSP), and finding a solution using Firefly Algorithm(FA) and Particle Swarm Optimization(PSO). Effective comparison is done to find the optimal path using both techniques. The obtained results show that FA outperforms PSO.
A New Coordination Approach to Mitigate Composition Attack

Data anonymization is one of the essential strategies, which is utilized in security protecting data publication. Numerous techniques have been proposed to anonymize various associations' data sets to distribute them autonomously. In any case, a data set is infrequently confined, a nd distributed by numerous associations may get a record of a similar individual. An independently distributed data set may have a little security hazard. Notwithstanding, the blend of two distributed data sets may penetrate people's security, common in informational collections, by composition attack. A couple of researches have been done to forestall it without considering coordination among different associations before data publishing. Even though coordination is a magnificent method to keep away from such issues, associations would prefer not to share their client informational collections with others. In this work, we present a coordination strategy where complete information sharing is not essential. This will successfully beat security concerns. It has been demonstrated tentatively that the proposed strategy anonymizes information to fulfill the security necessity and preserve high data utility than previously proposed models.
DCT/IDCT Filter Design for Ultrasound Image Filtering

In this paper, a new recursive structure based on the convolution model of discrete cosine transform (DCT) for designing of a finite impulse response (FIR) digital filter is proposed. In our derivation, we start with the convolution model of DCT-II to use its Z-transform for the proposed filter structure perspective. Moreover, using the same algorithm, a filter base implementation of the inverse DCT (IDCT) for image reconstruction is developed. The computational time experiments of the proposed DCT/IDCT filter(s) demonstrate that the proposed filters achieve faster elapsed CPU time compared to the others. The image filtering and reconstruction performance of the proposed approach on ultrasound images are presented to validate the theoretical framework.
Deep Topic Modeling by Multilayer Bootstrap Network and Lasso

Topic modeling is widely studied for the dimension reduction and analysis of documents. However, it is formulated as a difficult optimization problem. Current approximate solutions also suffer from inaccurate model-or data-assumptions. To deal with the above problems, we propose a polynomial-time deep topic model with no model and data assumptions. Specifically, we first apply multilayer bootstrap network (MBN), which is an unsupervised deep model, to reduce the dimension of documents, and then use the low-dimensional data representations or their clustering results as the target of supervised Lasso for topic word discovery. To our knowledge, this is the first time that MBN and Lasso are applied to unsupervised topic modeling. Experimental comparison results with five representative topic models on the 20-newsgroups and TDT2 corpora illustrate the effectiveness of the proposed algorithm.