Reviewerly/exHarmony huggingface dataset:
A Sequence-to-Sequence Text Summarization Using Long Short-Term Memory Based Neural Approach

The massive development in the different fields of engineering and science brought a significant change in the modern years.In the field of natural language processing, automatic text summarization is known as one of the important research directions.The text's primary concepts and flow should be considered to provide a solid summary that limits the repetition in the text as a summary.A sentence is referred to as a basic language unit and there are many semantic links present among the prison terms like example-of, cause-effect, sequential, etc. within a meaningful text.In this research manuscript, an automated text summarization model is developed and its performance is validated on the two challenging datasets such as daily-mail and Gigaword.In this manuscript, a transformer: long short term memory (LSTM) neural network is used along with the Huber loss function and Adam optimizer.The transformer used in this research (LSTM with Huber loss function and Adam optimizer) includes the advantages like requiring limited memory, computationally effective, and easy implementation.The proposed neural network mainly aims in exhibiting the summaries that are composed by the groups or paragraphs that includes more keywords or phrases than summaries composed by sentences.The non-differential evaluation metrics are utilized in the proposed sequence-tosequence model to provide semantic information of input text and it stores important features for effective text summarization.The size of the parameters is specified by the maximum number of sentences in the same group.The proposed LSTM with Huber loss function and Adam optimizer has 44.51 ROUGE-1, 20.43 ROUGE-2, and 40.08 ROUGE-L on dailymail dataset.Experimental results of the trial demonstrated that the proposed model outperformed the existing models like dynamic residual network, convolutional neural network with LSTM, and knowledge powered topic level attention model in terms of the rouge parameter.
Unprecedented Security Analysis Results for a Novel Steganography Approach Based on Protein Sequences

With the rapid advancement of the digital network, information technology, digital libraries, and especially the services of the world wide web, many types of information can be retrieved at any time.Thus, the issue of security has become one of the most important problems in distributing new information.It is essential to protect this information while it is passing through unsecured channels.Steganography offers a powerful approach to hiding confidential data in suitable media vectors such as images, audio files, text files, and video files.In the field of steganography, the most common measurements of hiding the intended data in a cover media are hiding capacity, cracking probability, payload, and bit per nucleotide (bpn), and these measurements are considered the most of the prime challenges in the field of steganography.The main objective of this research is to provide a novel data hiding schema using protein-based steganography that provides good security performance measurement and outperforms DNA-based steganography approaches, where protein sequences are not utilized for steganography purposes.According to the proposed method, each byte of the secret message is partitioned into two 4-bit parts, and then these parts are converted into the decimal system.Finally, the decimal values of the secret message are randomly included in the cover protein sites using a pseudo random number generator (PRNG) for each cover protein base value instead of being sequentially embedded in the cover protein bases.It is considered that this random distribution increases system security.The proposed method works with a one-digit protein decimal coding rule (PDCR), and the byte of the message will be embedded in two bases of amino acids.From the experimental results, it has been found that the proposed method preserves the reference protein's original function (zero modification rate in the original protein sequence) and achieves a very high hiding capacity of 4 bpn when the sequence's bases are entirely data-embedded, a very low cracking probability, and a payload of zero.Furthermore, the proposed method saves 50% of the bandwidth compared with the other existing technique.
High Reliability Through Cache Failure Minimization Technique for Workload Execution Under Multi-Core Edge-Cloud Platforms

The Internet of Things (IoT) has become an infrastructure for various domain applications such as smart homes, smart cities, wearables, smart grids, etc. Due to these applications, the internet of things search engines has attained some attention from various researchers, industry, and users.The search throughput of IoT search engines is crucial because they must execute hundreds of spatial-time-keyword inquiries in a second.Moreover, IoT search engines use cloud resources to execute tasks.Some of the tasks might have a small workflow but some of the tasks might have a larger workflow and require more virtual machines.Instead of using the virtual machine the best option is to utilize a caching mechanism.In recent years, there are only a few methods that use the caching mechanism for the execution of the task.Furthermore, these methods are not reliable, and efficient and have not considered multicore edge-cloud computing.To overcome this issue, this paper proposes a high-reliability method using the cachefailure mechanism for the multi-core edge-cloud computing architecture.The proposed High Reliability through cache-failure minimization (HRCFM) technique's main aim was to provide better tradeoffs during the execution of the task using the caching mechanism and reduce energy consumption.The results have been compared with the existing energy minimizing scheduling (EMS) and reliability (REL) technique in terms of execution time, power sum, power average, and energy consumption.The results have been compared and it shows that the proposed HRCFM technique reduces the time, energy consumption, power sum, and average by 83.3%, 90.92%, 38.44%, and 91.59% for EMS technique and 81.91%, 91.4%, 41.24% and 91.85% for REL technique respectively.
An Adaptive Compression Technique for Efficient Video Reconstruction in Future Generation Wireless Network

The mmwave (millimeter wave) with massive MIMO (Multiple-input and multiple-output) leverages larger antennas with spatial diversity and multiplexing gain brings about positive vibe for future generation wireless networks for provisioning bandwidth hungry multimedia applications However, the fronthaul link that links a remote radio head (RRH) and carries video frames as bitstreams to the baseband unit (BBU) can fluctuate the overall speed and capacity of massive MIMO if suitable data compression (encoding-decoding) techniques are not functional.This paper presents an error detection aware compression (EDAC) scheme which first performs encoding using low-rank approximation considering both spatial and time domain to reduce video frame size; later, the encoded video frames are compressed using Huffman codewords.Experiment outcome shows the proposed video compression model achieves much better compression efficiency with improved bit error rate (BER), symbol error rate (SER), error vector magnitude (EVM), and compression ratio when compared with the fixed code dictionary huffman video coding (FCDHVC), exiting video compression (EVC) technique and huffman video coding (HVC).The proposed EDAC model achieved compression efficiency of 30.52 whereas the FCDHVC, EVC and HVC model achieved compression efficiency of 26.50, 21.75 and 22.35 respectively.
Intensified Convolutional Neural Network for Enhancing Quality of Encrypted and Compressed Optical Images

The compression and encryption of images is essential to securely transmit high quality images over the optical network.In earlier studies, an optical image encryption using Loxodromic cat map with improved double random phase encoding (LCMIDRPE) has been developed.Hilbert huang transform (HHT) used in this encoding model decrease deviations of encrypted images.However, efficient image compression technique is required for compression.An efficient optical image compression and encryption (OICE) technique is developed in this paper by Direction-adaptive discrete wavelet transform (DA-DWT) along with directional lifting and LCMIDRPE.The original image is compressed using the DA-DWT with directional lifting and then encrypted by LCMIDRPE.The inverse version of LCMIDRPE and DA-DWT regain the original image.The missed information's due to lossy compression, decompression, encryption, decryption and transmission of images degrade the quality of regained images.The regained image is enhanced using intensified convolutional neural network (ICNNet).The whole process is termed to be LCMIDRPE-DADWT-ICNNet.Finally, the experimental results exhibits that the LCMIDRPE-DADWT-ICNNet model achieves correlation coefficient (CC) of 0.98 , Peak signal-to-noise ratio (PSNR) of 97db, Mean absolute difference (MAD) of 20 and mean square error (MSE) of 0.45 which is higher than OICE methods like Compressive Sensing and Rivest-Shamir-Adleman method (CS-RSA), Fresnel Diffraction and Discrete Wavelet Transform (FD-DWT), 2Dimentional Sparse Representation and Chaotic Map (2DSR-CM), 2Dimensional Compressive Sensing and Hyperchaotic System (2DCS-HS), Diffractive-Imaging-Based Encryption (DIBE) and Content-Adaptive Image Compression and Encryption using Optimized Compressive Sensing with Double Random Phase Encoding (CAIE-OCS-DPRE) methods.
Parallel Implementation of Statistical DBSCAN Algorithm for Spark-based Clustering on Google Cloud Platform

We present a new parallel density-based spatial clustering of applications with noise (DBSCAN) algorithm for spark on the google cloud platform (GCP).Statistical analysis is applied to determine DBSCAN's optimal parameters to enhance clustering performance.for scalability cost-based, R-tree partitioning is selected based on the distribution of the dataset into balanced workloads.Parallel DBSCAN consists of three parts: local DBSCAN, partitioning, and merging.Optimizing the partitioning of parallel DBSCAN is important to save time and space compared to serial DBSCAN.This approach can improve the performance and time cost of large datasets.the modified statistical cost-based (SCbs-DBSCAN) is applied to the UCI (university of california irvine) standard datasets, basic benchmark clustering and large different scales of data.For clustering performance and time cost, the experimental results show that the proposed algorithm achieve 10~15% more efficiently, and can run about 1.5x~3x faster than alternative Parallel DBSCAN method on Spark without sacrificing clustering quality.
Improved ABC Algorithm Based 3D Otsu for Breast Mass Segmentation in Mammogram Images

One of the most prominent indicators for the detection of breast cancer is a breast mass.In this regard, effective mass segmentation for any type of mammography is crucial for improving breast cancer detection accuracy and lowering mortality.In order to pace up the process of mammogram segmentation for breast mass, an ABC3D (artificial bee colony based 3 dimensional) Otsu method is proposed in this paper.Firstly, convergence speed of bees in basic artificial bee colony (ABC) is improved by adopting the epsilon greedy method for scout bees.Secondly, proposed improved ABC method is paired with optimal 3D Otsu multilevel thresholding technique to get the better thresholding set for medical mammogram images.Epsilon greed based scout bee technique streamline the exploration-exploitation problem of standard ABC while searching for best threshold set in 3D space.The proposed ABC3D is tested on eight mammogram images collected from the authoritative and publicly available database mini MIAS (mammographic image analysis society).PSNR (peek signal to noise ration), SSIM (structural similarity index) and time cost are measured to record the effectiveness of ABC3D method.The results of experimentations indicate that the proposed ABC3D achieve superior segmentation results than the teaching learning ABC (TLABC), ABCDS (directed scout), gbest guided ABC (GABC), improved particle swarm optimization (IPSO) with 3D Otsu as objective functions.
Ex- and in-situ tests of materials: From design to materials parameters via motion estimation

Materials tests are inevitable to investigate how materials behave under load. This also holds for materials that are not manufactured from bulk material but exhibit an interesting microstructure. Unfortunately, in this case classical stress-strain investigations are no longer sufficient to deduce a proper description of failure mechanisms. To overcome this problem, materials testing is usually combined with an imaging modality to gain information on local material behavior. The combination with classical digital cameras is quite common, but only yields surface information. Here, we present a framework to combine mechanical tests with computed tomography. Such in situ tests are not new, however, we also give information on how to perform tests ex situ. Our special focus is on motion estimation. We use an algorithm that is particularly suitable for delicate behavior of materials with interesting microstructures such as foams. The whole pipeline for materials tests, either in situ or ex situ, is outlined using two foam examples.
N-Dimensional Image Encoding on Quantum Computers

Quantum computing is currently one of the fastest emerging branches of information processing due to its theoretical computation powers exceeding conventional computers by far. However, currently well-known quantum-powered algorithms are of theoretical nature and its effect on practical problems has yet to be discovered. This work reviews current approaches in context of the applicability of quantum computing for the domain of image processing with respect to 3D computed tomography. It focuses on the encoding of n-dimensional images on a quantum computer and also the quantum measurement process, i.e., the image read-out. First results show that while the encoding is indeed highly efficient and is well-suited for representing 3D voxel data as obtained from computed tomography, the decoding to a classical representation is rather expensive. The latter part is also very sensitive to quantum hardware noise as was evidenced by both noisy simulator and real quantum hardware experiments.
Interactive Denoising of 3D Volumes Using Wavelets

Conventional signal denoising methods either focus on statistical properties or employ a (complicated) set of parameters a user needs to tweak appropriately. More recently, AI-based denoising methods are increasingly employed due to their robustness. However, these methods typically require a large number of training datasets, which are costly to produce in a computed tomography scenario. This work introduces a denoising procedure which combines classifier-based decision making with interactive user input in the wavelet domain. These interactive user inputs allows for an easy and straight-forward specification of noisy regions, which works especially well in the wavelet basis, without the need for statistical properties or large numbers of parameters. At the same time, the obtained results show that noise in the scanned data is reduced considerably while keeping the structural similarity intact.
Pengembangan LKS (Lembar Kerja Siswa) Berbasis Literasi Di Sekolah Dasar Awal Untuk Memperlancar Membaca Siswa

Development research that has been carried out under the title "development of literacy-based LKS (Student Worksheets) in early elementary schools to facilitate students' reading". Learning at the early elementary level (grades 1, 2 and 3) students are required to be able to even read fluently, whereas before entering elementary school students have not been taught much to read so that most of the early elementary school students cannot read fluently. This study aims to describe the feasibility of literacy-based worksheets in terms of the criteria for the components of material, presentation, and language; feasibility in terms of expert judgment and student responses. This research refers to the 4D development model but only reaches the Develop stage. The research analysis was carried out in a quantitative descriptive manner, which showed that literacy-based worksheets were appropriate for use in learning in terms of all aspects covering material with a percentage of 88.88%, presentation of 88.88%, and language of 85.00%. In addition, feasibility is also reviewed from student responses which include the feasibility of presentation in learning by 93.42%, the criteria for presenting worksheets by 80.26%, and language by 75.43%. Individual learning completeness with a percentage of 100% complete with KKM achievement of ? 70 and classically 100% complete as a support for eligibility.
Development of a Port Facility Diagnostic System that Utilizes Data Measured by Strong Motion Seismographs

Abstract The Authors developed a damage assessment system for mooring facilities. This system uses information on seismic motion observed by strong motion seismographs when an earthquake occurs in order to determine whether or not mooring facilities can be used based on the degree of damage suffered. The system creates maps estimating damage based on the results. Two methods are available for determining usability—immediate diagnoses and detailed diagnoses—and all calculations are performed automatically, from the acquisition of the seismic motion information to the determination of usability. The resulting maps estimating damage can be used to select surveyed facilities for on-site damage assessments, making this system an effective tool for supporting initial response systems after a disaster strikes.
Introducing mCRL2

AbstractThis chapter provides basic information about mCRL2. Historical notes and comparisons with other similar tools are provided, too. Installations and configuration instructions are also there, along with a road map for learning mCRL2.
A VHDL Code for Offset Pulse Position Modulation Working with Reed Solomon System by Using ModelSim

Error correction codes, often known as ECC, play a significant part in the process of detecting and correcting data mistakes that occur through communication channels that are unreliable or noisy. The essential concept behind error correction through ECC is to supplement the message that is being sent by the transmitter with redundant bits, the values of which are determined by the parameters n and k. These bits can then be utilized by the receiver to identify and correct specific types of errors. ECC is utilized in a wide variety of applications, including but not limited to data storage, the Internet, and telecommunications. There are numerous variations of ECC, including linear block, convolutional, and turbo codes, among others. The results of a simulation of a linear block reed Solomon, for example, with offset pulse position modulation have been presented in this study. The simulation was carried out in very high-speed integrated circuit hardware description language (VHDL), and a field-programmable gate array was used (FPGA) It made use of a Boolean function to function to program code for an algorithm that is working. Because of its performance, time to market, cost, reliability, and long-term maintenance benefits, FPGA is an appropriate platform for implementing error correction code (ECC). As a part of this project, the technique of offset Pulse Position Modulation (Offset PPM) was invented as an outstanding solution to code the fiber-optic applications and Reed Solomon (RS) codes apply to ModelSim SE-64 10.5 software. In addition, this coding scheme has been approved by the simulation and is matched with theory, and it is expected to be implemented shortly. The study begins with a concise introduction to RS encode/decode about design and performance and then moves on to discuss the development result of simulation and hardware implementation.
The Effectiveness of Using Hand Puppet in Teaching Speaking

In this study, the researcher attaches several research problems. Before the treatment, students' ability to speak was still lacking because students were still studying speaking lessons in the usual way, such as only explaining the material and practicing speaking without media. After the treatment, the students' abilities were further improved because they were taught speaking lessons by practicing using the Hand Puppet media. There was a significant difference, namely the increasing ability of students. Students are more straightforward in speaking when using the Hand Puppet media. Researchers used the pre-experimental method to obtain score data. Researchers conducted a normality test to find out whether the data was normal or not. Based on the normality test of the data that has been carried out by the researchers, it shows that sig. 2-tailed is 0.200 meaning that the data is normally distributed, followed by a simple paired t-test. based on the Testing of Hypothesis Terms, If the value of t is sig. &lt;0.05, Ha is accepted and H0 is rejected. on the simple paired t-test calculation, the data shows sig. (2-tail) 0.000. The data is smaller than 0.05, meaning that H0 is rejected and Ha is accepted.
An Analysis about the Key Features of Learning Management System (LMS) and the Ways to Include Parental Participation into LMS

This review paper presents the analysis about the features of Learning Management System (LMS) and the ways to include parental participation into LMS. A list of 20 popular LMs given. The main features of LMS discussed. The ways and importance of including parents into Leaning Management Systems also discussed.
The Design and Implementation of the Natural Handwriting Mathematical Formula Recognition System

The traditional handwritten mathematical formula recognition mode has many shortcomings in recognition. For example, the recognition rate is low, the operation is complicated and other pain points. In order to make handwritten mathematical formula recognition more accurate and easy to use, and to solve the problem of low efficiency of editing data formulas for researchers engaged in mathematics-related professions. In this paper, a natural handwritten mathematical formula recognition system with one-click operation and higher recognition is implemented. The system uses a core algorithm to separate the target formulas based on histogram projection and dynamic comparison word method, and a three-layer convolutional neural network model based on CNN to recognize the segmented strings. Experiments show that the improved algorithm based on the algorithm has strong learning ability and robustness.
Biostratigraphy of Oligo-Miocene marine deposits of Central Iran basin based on calcareous nannoplanktons

Biostratigraphy of Oligo-Miocene marine deposits of Central Iran basin based on calcareous nannoplanktons
PREDIKSI PENJUALAN BARANG PADA TOKO BABY SHOP DENGAN ALGORITMA SINGLE MOVING AVERAGE (SMA)

Sales is an integrated effort to develop strategic plans that are directed at satisfying the needs and desires of buyers in order to obtain profitable sales.The process of selling various equipment products for babies and children at the Baby Shop Shop is very busy.Many customers, especially mothers who need equipment products for their children's needs.However, the process of inputting sales data is still done manually through books and does not utilize computer technology, so there are often excess and even shortages of inventory so that the sales process becomes hampered.Because it feels less effective and efficient in the sales process can run smoothly.This application aims to facilitate sales predictions at the Baby Shop Shop and can find out prediction calculations using the Single Moving Average method.The results of this study can predict each item of sales in the following month with an average accuracy rate of 90% with the Single Moving Average method using PHP and mysql database.