Reviewerly/exHarmony huggingface dataset:
An Augmented Encoder to Generate and Evaluate Paraphrases in Punjabi Language

Paraphrase generation is an important task in Natural Language Processing (NLP) and is successfully applied in various applications such as question-answering, information retrieval & extraction, text summarization and augmentation of machine translation training data. A lot of research has been carried out on paraphrase generation but in the language of English only. However, no approach is available for paraphrase generation in Punjabi Language. Hence, this paper aims to plug in the gap by developing a paraphrase generation and evaluation model for the language of Punjabi. The proposed approach is divided into two phases: paraphrase generation and evaluation. To generate paraphrases, the current state-of-the-art transformer with improved encoder is being used as transformers can learn long-term dependencies. For evaluation, the sentence embeddings are used to check whether the generated paraphrase is similar to the given sentence or not. The sentence embeddings have been created using two approaches: Seq2Seq with attention and transformers. The proposed model is compared with the currently available state-of-the-art models on Quora Question pair dataset. However, for Punjabi, the proposed approach is evaluated on three datasets: news headlines, the sentential dataset from news articles and the third dataset is the translation of Quora Question pair into Punjabi. The automatic evaluation metrics BLEU, METEOR and ROUGE are used for depth evaluation along with human judgments. The proposed approach is straightforward and successfully applies for augmenting machine translation training data and sentence compression. The proposed approach establishes a new baseline for paraphrase generation in Indian regional languages in the future.
Analysis for Lossless Data Compression Algorithms for Low Bandwidth Networks

Abstract Analysis for loss less data compression delivers the relevant data about variations of them as well as to describe the possible causes for each algorithm and best performing data types. It describes the basic lossless techniques of data compression Huffman encodes, Arithmetic Encoding, and Lempel Ziv Encodings then briefly with their effectiveness under varying data types of Latin text, audio and video. These properties give the solution of which lossless compression algorithm more suitable compared to other from the Saving Percentage, compression ratio, time of compression and time of decompression with Low Bandwidth Network. Moreover here Lossless Data Compression Algorithms (LDCA) being implemented and tested Huffman compression, Arithmetic compression, and Lempel Ziv algorithms, the implemented result shows that LZW algorithm saves more size than that of the others two with text file, Huffman compression algorithm saves more file sizes and the time takes to compressed decompress is higher than that of other two for audio file type and finally Huffman performs greater on very huge data compressions that is due to much compressing capability.
FLIPPED CLASSROOM RESEARCH DURING COVID-19 PANDEMIC: A SCIENTOMETRIC OVERVIEW

E. Caballano-Infantes 1L.G. Borrego 2D.E. Di Zeo-Sánchez 1P. Sánchez-Núñez 1B. Delgado-Martín1C. Sánchez-Gómez1I. Brichette-Mieg 3J. Terrón-Bautista 4A.D. Marrero 1
Accelerometry for Human Activity Recognition: an Overview

In this paper, we discuss accelerometry: an approach to human activity recognition and functional state assessment based on the usage of inertial sensors. Our discussion summarizes the pivotal aspects of accelerometric research, intending to provide an overview for the beginner practitioners in the field. Covered topics include signal processing techniques and machine learning algorithms, as well as general pointers to relevant literature.
Why do the white keys sound the way they do, or How did C come to be?

This paper presents two theories as to the rigidification of the collection of pitch-frequencies that make up &quot;C Major&quot;, visually known as the &quot;white keys&quot; on a standard piano, in teaching and understanding music. First, there is
Turkish Keyphrase Extraction from Web Pages with BERT

Keyword extraction is a natural language processing task that enables the extraction of essential and descriptive words in the text. The task of extracting keywords from wellstructured texts has been studied extensively in the literature. However, because of the difference in the structures of the gathered data from the websites and the difficulty of this process, there is a lack of studies in this field. In this study, data from the websites of 25 large Turkish companies operating in Turkey is collected, and keywords related to these companies are extracted. In the proposed deep learning-based model, Sentence-BERT, a BERT-based method that has recently yielded quite successful results in natural language processing, is used. To evaluate the performance of the proposed method, the data is annotated with human effort and the impressive results are shared with the reader. In addition, clues about the companies' business domains are tried to be detected by clustering the companies' keywords. All results and findings of this study are shared at https://turkishkpe.github.io.
Verification of Probabilistic Latent Semantic Analysis Clustering Solution Stability and Proposal of Optimal Initial Values Setting Method

pLSA is a useful method to know the characteristics of customer or item in marketing. In this study, we proposed a method to set the initial values more efficiently than the existing method for the problem that the final solution depends on the initial values set in the EM algorithm used by pLSA to estimate the solutions. We focused on the dimensional compression and clustering that are the characteristics of pLSA, and thought that the stability of the solution of pLSA would be improved by reflecting it in the initial values. Therefore, first, we performed correspondence analysis and k-means cluster analysis on the original data to express the features of dimensional compression and clustering. Next, we compared the performance of the pLSA results with the initial values of the proposed method and the initial values of the conventional method using random numbers. As a result, it was shown that the proposed method also converges to the same log-likelihood as the conventional method, and that the proposed method is superior in terms of convergence speed and stability.
Effects of Aerosols and Climate Properties on Solar Radiation amount Falling on Horizontal Surface at Kerbala City

In this paper a mathematical description for solar radiance falling on the horizontal surface on Iraq (kerbala city) was investigated and compared with real measurements, the city having the position of (E,32° 37′ 0″ N, 44° 1′ 59.99″ ) 32.61, 44.03, the solar radiation as a clean energy source suffering many process that reduce its efficiency to providing many applications with a free source of energy this process mainly due to the absorption, scattering and diffraction of electromagnetic light wave, the results show that there is a major absorption by aerosols, clouds and atmospheric gases produced significantly by CO2, CH4,N2O,CO and O2 due to their size about 10-4 to 10 µm and its concentration in atmosphere, a main relation between the solar harvesting energy and geometrical, data for the position and day time for the sun on site of study, a climate data for the year 2018 used, finally a good identical among theoretical, real, and results obtained from other solar irradiance models.
SDM-Net: A Simple and Effective Model for Generalized Zero-Shot Learning

Zero-Shot Learning (ZSL) is a classification task where some classes referred to as unseen classes have no training images. Instead, we only have side information about seen and unseen classes, often in the form of semantic or descriptive attributes. Lack of training images from a set of classes restricts the use of standard classification techniques and losses, including the widespread cross-entropy loss. We introduce a novel Similarity Distribution Matching Network (SDM-Net) which is a standard fully connected neural-network architecture with non-trainable penultimate layer consisting of class attributes. The output layer of SDM-Net consists of both seen and unseen classes. To enable zero-shot learning, during training, we regularize the model such that the predicted distribution of unseen class is close in KL divergence to the distribution of similarities between the correct seen class and all the unseen classes. We evaluate the proposed model on five benchmark datasets for zero-shot learning, AwA1, AwA2, aPY, SUN and CUB datasets. We show that, despite the simplicity, our approach achieves a competitive performance with state-of-the-art methods in Generalized-ZSL setting for all of these datasets.
Service Design Based on Customer Value Classification in IoT Systems

There are four stages for development of IoT systems identified as monitoring, control, optimization, and autonomy. The value that the IoT system can provide is constrained by the capabilities of the system, therefore it is necessary to build a service that fits the feature of the system. Based on fifty IoT business cases, the researchers classified fourteen values for four IoT systems and proposed methods to improve on services based on features of IoT system. However, the value classification had a wide range of value expressions, and therefore might not focus correctly on the provided services. This paper is reconstructed with the fourteen value classifications of IoT systems by using elements of value and suggests more appropriate service design.
GP-ConvCNP: Better Generalization for Convolutional Conditional Neural Processes on Time Series Data

Neural Processes (NPs) are a family of conditional generative models that are able to model a distribution over functions, in a way that allows them to perform predictions at test time conditioned on a number of context points. A recent addition to this family, Convolutional Conditional Neural Processes (ConvCNP), have shown remarkable improvement in performance over prior art, but we find that they sometimes struggle to generalize when applied to time series data. In particular, they are not robust to distribution shifts and fail to extrapolate observed patterns into the future. By incorporating a Gaussian Process into the model, we are able to remedy this and at the same time improve performance within distribution. As an added benefit, the Gaussian Process reintroduces the possibility to sample from the model, a key feature of other members in the NP family.
Peningkatan Minat dan Hasil Belajar Materi Memproduksi Teks Prosedur dengan Model Picture And Picture bagi Peserta Didik Kelas XI AKL 3 SMK Negeri 1 Slawi

The purpose of this study is to find out the increased interest and learning outcomes of producing the text of the procedure of students of class XI AKL 3 SMK Negeri 1 Slawi Semester Gasal School Year 2019/2020 after following the learning to produce the procedure text with picture and picture model. This study uses class action research design (PTK) with 2 (two) cycles. The subject of this study was the result of producing the text of the procedure of students of class XI AKL 3 SMK Negeri 1 Slawi which amounted to 34 students. The results showed the use of picture and picture models can increase interest and learning outcomes in producing the text of student procedures. Increased interest is seen in the initial (pre-cycle) conditions of many passive learners to the final condition in cycle II of many active learners. The improvement in the results of learning to produce the text of the procedure was seen in the percentage of students who got a complete score (meet kkm) from the initial condition of the procedure text production value of only 52.94% then in cycle I increased to 67.65% and at the end of the second cycle increased to 91.17%.
A survey on predicting workloads and optimizing QoS in the cloud computing

This paper presents the concept and characteristics of cloud computing, and it addresses how cloud computing delivers quality of service (QoS) to the end-user. Next, it discusses how to schedule one’s workload in the infrastructure using technologies that have recently emerged such as Machine Learning (ML). That is followed by an overview of how ML can be used for resource management. Then, this paper aims to outline the benefits of using ML to schedule upcoming demands to achieve QoS and conserve energy. In addition, we reviewed the research related to ML methods for predicting workloads in cloud computing. It also provides information on the approaches to elasticity, while another section discusses the methods of prediction used in previous studies.
Increasing Data-Knowledge Through Artistic Representation

In a data-driven world personal data is key to every business decision and strategy. Telling a story from raw data is a specialized skill gate-kept by graduate level data visualization education and occupational software. Lack of data literacy is increasingly perceived as embarrassing and detrimental to adults’ understanding of the world around them. In this regard, an user experience case study was conducted to develop a method and tool to empower users to artfully tell stories with data. Robust human-centered design methodologies were employed to identify target users, develop deep user insights, and provide recommendations for a detailed design execution of a new creative data visualization tool concept. The results of this study demonstrated the potential of proposed tools in increasing user’s data-knowledge through the medium of art. The new creative visualization tool developed could be further refined for children to increase their knowledge of the data-driven world and interest in STEAM careers.
A Comparative Study on the Performance of DWT and Huffman Compression Technique on a 2D Signal

Nowadays, the development of technology which involved multimedia data are widely used to help better understanding in spreading information. Image is known as 2D signal which contain huge data especially a high resolution image. In this paper shows the comparison of applying lossy and loss compression on the image data. Image compression is necessary in reducing the size of image for storage or transmission purpose to support most of the application nowadays. Besides, some technique also offers in simplifying the image data to process efficiently. DWT technique and Huffman coding technique are selected as the process of lossy and lossless compression, respectively. The performance of image compression are evaluated in terms of compression ratio, quality of image (MSE, PSNR and SSIM value) and computing time. Several type of evaluation can determine better technique to apply on specific type of application. After conducted a comprehensive observation, DWT technique can provide good performance of compression ratio and computing time as well as the quality of image. Basically, DWT technique only have slight loss of data during the process which the output image is still clear to see by naked eyes. In contrast, Huffman coding technique is lacked in both compression ratio and computing time. However, it helps in maintaining the quality of image without any losses of data. Therefore, certain technique of image compression can be used according to the specific goal of application which depends on the performance of image compression method.
Development of EPS Insulation with Improved Insulating and Non-combustible Performance

In order to save energy in buildings, the insulation standards of buildings are continuously strengthened, and since the fire accident in 2015, the required performance of fire safety of insulation materials has been strengthened. Accordingly, it was attempted to develop an EPS wall insulation with improved semi-non-combustible flame retardant performance and thermal insulation performance. In order to improve the flame retardant performance, the optimum final mixing ratio was derived according to the coating solution surface coating amount, ceramic input amount increase, expansion ratio adjustment, expanded graphite, vermiculite and borate ratio. The final blending ratio is a bead formed by adding 30% aluminum hydroxide to 50 times the ratio of the two beads. The coating blending ratio is EPS 100% with an additional content of an acrylic binder expanded graphite (300 times), expanded vermiculite 10%, and a flame retardant solution of 5% borate. It was mixed at a ratio of 100% of semi-non-flammable coating solution and passed by satisfying the performance of 0.033W/mK of thermal conductivity and suitable for cone calorimeter test. In order to solve the construction defects caused by the shrinkage of EPS insulation, a prototype was produced with an overlap protrusion design and a half-jaw structure design, and the half-jaw structure design was found to be superior.
Bearing-Only Navigation with Field of View Constraints

This paper addresses the problem of navigation using only relative directional measurements (i.e., relative distances are unknown) with field of view constraints. We present a novel navigation vector field for the bearing-based visual homing problem with respect to static landmarks in 2-D and 3-D environments. Our method employs two control fields that are tangent and normal to ellipsoids having landmarks as their foci. The tangent field steers the robot to a set of points where the average of observed bearing vectors is parallel to the average of the desired bearings. The normal field uses the angle between different pairs of bearings as a proxy to adjust the robot's distance from the landmarks and to satisfy the field of view constraints. Both fields are blended together to construct an almost globally stable control law. Our method is straightforward to implement, as it requires only comparisons between average bearings, and angles of pairs of vectors. We provide simulations that demonstrate the robustness of our approach on two systems: a double integrator, and a unicycle.
Classification of YouTube Data based on Opinion Mining

Opinion Mining is a matter of great concern and an emerging research field of omnipresent computing. Nowadays, people find it very difficult to find the exact content which they have searched for due to the enormous amount of content found on google, YouTube etc. Using Machine Learning one can make the exact search and get the best match of the search without spending a lot of time which eventually leads to economizing time. Sentiment analysis systems are being applied in almost every business and social domain. It is primarily used to evaluate or analyse the commenter’s opinions, sentiments, emotions and attitude from a written piece/comment on a particular post/video.
Correction to: Performance analysis of pre-cancerous mammographic image enhancement feature using non-subsampled shearlet transform

A Correction to this paper has been published: https://doi.org/10.1007/s11042-021-11211-5
Design, Making and the Digitisation Trap

The article aims to investigate the evolutionary dynamics and critical issues emerging around makerspace communities, collaborative design and prototyping spaces whose practices have radically impacted the world of education, enterprise and social innovation. Through an exploratory approach, experts from the maker movement and design students have been involved to understand the health of the model underlying makerspaces. The perspective of the research is to graft the debate on the forms of these practices into the dichotomy between physical and digital, socialization practices of design and the ability of these bottom-up models to work on complex solutions. A list of recurring conditions occurring at the time of the creation of these communities has been elaborated. The discussion then covers risks and limitations of the physical dimension of makerspaces in relation with digitization of operations and relations at every level, calling for a necessary re-thinking of co-design practices.
Sequencing of Grammatical Items in Textbooks Oriented Towards International Chinese Language Education

The selection and arrangement of grammar is important in International Chinese Language Education. This paper collects and sorts out some grammatical items in some authoritative Chinese textbooks. Through quantitative investigation, it is found that although different textbooks have different writing styles and reference standards for grammar, there are some commonalities despite the complexity involved. In this paper, the core parameter model and the position parameter model are designed, and the grammatical items are sequentially studied in the two dimensions of commonness and difficulty. After the adjustment of weight coefficients and interval statistics, the two models have achieved good results in determining the two-dimension sequential values.
Modelling the Sense-Making of Diagrams Using Image Schemas

Author(s): Bourou, Dimitra Anna; Schorlemmer, Marco; Plaza, Enric | Abstract: We model the sense-making process of diagrams as conceptual blends of the diagrams’ geometric configurations with apt image schemas.We specify image schemas and geometric configurations with typed FOL theories. In addition, for the latter, we utilise some Qualitative Spatial Reasoning formalisms. Using an algebraic specification language, we can compute the conceptual blends of image schemas and geometry as category-theoretic colimits.We show through several examples how this model captures the sort of direct inferences we confer to diagrammatic representations due to our embodied cognition. We argue that this approach to sense-making might be of value for the design and application of diagrammatic and graphical visualisations, as well as for AI in general.
Improving Sales by Object-Oriented System Approach: E-Commerce Utilization Analysis

The development of Micro, Small, and Medium enterprises from year to year is influenced by the use of Information Technology and Information Systems. E-commerce is a technology that supports the development of Micro, Small, and Medium enterprises today. DM Khasanashop is a boutique company that sells a wide variety of clothes and pants from the lowest prices to the highest prices. Based on the results of the analysis carried out at DM Khasanashop Tangerang, the researcher can conclude that the problem that occurs in the sales system at DM Khasanashop is that sales have not been web-based or e-commerce and the data management system has not been computerized because the data processing process is still conventional and promotional media, still use brochures, banners, and business cards. The research objective was to determine the sales system using conceptual analysis and to determine the supporting and inhibiting factors in Khasanashop DM. The research method used is descriptive qualitative. Data collection techniques were carried out utilizing observation, interviews, and literature study. The results of this research show that management can be seen in several processes, namely making details of product prices, managing order data, employees packing products to sending products to consumers.
Adapted Model Neural-Like Hopfield Network and the Algorithm of Its Training for Finding the Roots Systems of Linear Algebraic Equations

An approach for automatic formation of the structure of the Hopfield neural network model and its training (parameter settings) for solving systems of linear algebraic equations (SLAE) of arbitrary order is proposed. Adapted for solving of SLAE model of network is configured automatically in the environment for modeling Simulink, interacting with the Matlab computing system, which allows the user to vary the input data (order, constants vector and coefficients matrix system of linear algebraic equations). The results of research on the quality of the finding for solutions to SLAEs are presented.