Reviewerly/exHarmony huggingface dataset:
A New Approach to Calculate Earthquake Slip Distributions

A transdimensional, probabilistic approach is more flexible than traditional least squares fits and provides better handling of sharply varying slip distributions.
Review on Augmented Reality and Virtual Reality Enabled Precious Jewelry Selling

In this new era of social distance, technology can help prevent the spread and build a resilient society. Technology is playing a vital role in keeping our society functioning without much disturbance. In such situations, online shopping becomes the easiest and the most suitable way for shopping. Considering India is one of the largest consumers of gold, diamonds, and other precious metals in the world. The jewelry and gem exchange of India plays a major role in the country’s GDP. This paper shows jewelry shopping using augmented reality and virtual reality that will allow the users to virtually examine the jewelry in the comfort of their home and give them the joy of conventional shopping.
A Study on Using Deep Learning for Segmentation of Medical Image

Segmentation of medical images using deep learning has provided state-of-the-art performances in this area of work. With the availability of large digital datasets and access to powerful GPUs, deep learning has transformed our world. We are now able to make computers mimic and replicate the functions of the human mind simply by providing enough data and computing the problem. Deep learning has a huge potential for medical image analysis and now it has been firmly established as a robust tool in image segmentation. This paper addresses the six popular methods that have employed deep-learning techniques for the segmentation of medical images which play a massive impact in the medical healthcare industry and in turn make a contributing role towards the concept of smart cities. A comparative study on these deep learning-based segmentation techniques will provide a researcher working in the field of medical imaging to explore further in this area for higher accuracy and better results.
Efficient Threshold Public Key Encryption from the Computational Bilinear Diffie-Hellman Assumption

In this paper, we show the first efficient threshold public-key encryption (TPKE) scheme under the difficulty of search problems. More specifically, our TPKE scheme simultaneously achieves: (1) chosen ciphertext security (CCA security) under the computational bilinear Diffie-Hellman (CBDH) assumption in the standard model; (2) re-splittability, which is a useful property that makes partial secret keys refreshable; and (3) O(√κ )-bit ciphertexts, public keys, and secret keys, where κ is a security parameter. Most previous CCA-secure TPKE schemes rely on decisional complexity assumptions or random oracles. Although there exist CCA-secure TPKE schemes under the difficulty of search problems, all such schemes are inefficient or work over small plaintext spaces. Technically, we begin with a direct construction of a threshold identity-based key encapsulation mechanism (TIB-KEM) with a weak security notion. Then, we transform the weakly-secure TIB-KEM into a CCA-secure TPKE scheme via the tag-KEM/DEM approach.
Modular Communication as a Structure for Sustainability Within Social Enterprises

How can communication designers think systemically, within the context of modular communications, to design for social enterprises aiming to create socio-environmental impact? This research explores modular communications as a structure to empower sustainability within social enterprises and to create social impact through messaging. It contextualizes the working of more with less over less with more. By using a multiple case study approach (five social enterprises mainly from India, South Africa, and Kenya), the findings of which communication strategies work (education, participatory action, positive messaging, action-oriented engagement) and which do not (negative messaging, blind reliance on social media, no social proof), become core themes. Interviews with the enterprises’ key leaders address the intention and outcome of their messaging. Thematic analysis was used as a systematic yet flexible approach to achieve the core themes. The study also led to the creation of a diagram to visually analyze the communication touchpoints of the enterprises, in modules. The core themes (as foundation) and modular communication (as structure) together suggest the achievement of sustainability within social enterprises and on-going effectiveness of their communication systems. This paper can act as a guide for designers passionate about creating impactful communication. It sprouts conversations about processes that are required in order to achieve the desired end-product.
A survey to estimate the proportion of Southeast Sulawesi elementary school students reading fluently

Abstract We conducted a survey to estimate the proportion of elementary school students in Southeast Sulawesi who have been able to read fluently while sitting at second grade. The information is needed to contribute to the policy towards enhancement of elementary student ability to read in their early stage of schools. A two-stage cluster sampling method had been applied in order to maintain the representativeness of selected samples over the whole population. At the first stage, we randomly chose the districts and, then, randomly selected the sub-districts. Finally, all elementary schools in each selected sub-district were taken as well as all their second grade students as the sampling units. Doing these, wegot 230 respondents randomly selected from 78 elementary schools of 20 sub-districts in all 6 selected districts or regencies. Based on the selected samples, it is approximately only 32% of students were able to read fluently, while the rest of them were unable or less abilityto read.
Enhancing Intent Detection in Customer Service with Social Media Data

Intent detection plays an important role in customer service dialog systems for providing high-quality service in the financial industry. The lack of publicly available datasets and high annotation cost are two challenging issues in this research direction. To overcome these challenges, we propose a social media enhanced self-training approach for intent detection by using label names only. The experimental results show the effectiveness of the proposed method.
Impact video properties to video transmission performances

Video streaming has now been popular in many social media such as facebook and whatsapp. Its transmission is no longer to be a problem as video codec technology is able to compress bytes to minimum amount. However, as real-time response is urgently required, suitable parameters should be adjusted accordingly to obtain smooth real-time stream. This article studies impact video parameters to video transmission performances. Streaming experiment on an ad hoc network shows that delay average delay decreases by the equation of y = 0.2218(x/10)−0.287 to frame rates and increases for higher frame size. Higher frame rates (for the same bit rates) and lower frame size are preferred for real-time applications.
Lan: Learning to Augment Noise Tolerance for Self-report Survey Labels

The prevalence of mobile sensors makes it possible for researchers to collect and analyze the pervasive sensed human activity data with machine learning tools. These analyses and applications heavily rely on the self-report survey data that reflect human physical and psychological behaviors. However, many factors can affect the reliability of such self-report surveys, such as participants' trusty and ambiguous descriptions in questionnaires. These inaccurate survey labels challenge the development of the models, especially when it comes to the case where deep learning approaches tend to memorize the noisy information from training data. In this paper, we set out to deal with the noisy self-report survey labels. We aim to address the following challenges: 1)the limited access to clean labeled data in real-world scenarios; 2)different noise ratio of the data from different participants; 3)various types of noise that appear in the data; 4)how to evaluate the reliability of participants. As such, we propose a generalizable model, Lan, for learning from multi-modal sensory data and noisy labels. The proposed model incorporates the temporal information from sensory data to guide the model training and thus to augment noise tolerance. Extensive experiments on a real-world dataset were conducted to show our model outperforms state-of-the-art benchmarks.
Dynamic Model Based Detection of Cyberattacks in Industrial Facilities

The proceeding digitalisation of industrial facilities does not only provide meeting the requirements of flexibility and just-in-time production. It comes with increasing vulnerability against cyberattacks. This already manifests in high numbers of cyberattacks against industry 4.0 facilities (Kaspersky Lab, 2020). In these cyber-physical-systems, cyberattacks do not only affect the informational system, they can also cause damage in the physical part that is closely coupled to the informational part. Therefore, new methods for securing industry 4.0 facilities against cyberattacks are required. The method introduced in this paper continuously checks if the measurements and setpoints fulfil the systems physical relations utilizing data reconciliation and extends it by consideration of informational properties for detecting, localizing and selectively combatting cyberattacks. Conventional protection methods like the comparison of each measurement with a specific range of save service cover only basic sensor failures and trivial cyberattacks. A more sophisticated protection method, which analyses the consistence of the static system state with respect to a static system model of the process was introduced in (Reibelt et al. 2020) using static data reconciliation. The new method presented here is also applicable in dynamic system states. This paper shows the adaptation of the data reconciliation based approach for dynamic system relations and demonstrates its application.
Study on Fuzzy Soil Balanced Fertilization Model System Based on Neural Network

With the rapid development of economy and society, a series of problems have become more and more serious, such as the increase of people and the decrease of land, the shortage of resources and the deterioration of ecological environment. The deep integration of the new generation of information technologies such as artificial intelligence, big data, Internet of things and the agricultural field has laid a solid foundation for the development of precision agriculture and smart agriculture. The results will provide a new way to solve the problems of resource shortage and environmental pollution. Precision fertilization is the core link of precision agriculture. It is very necessary to understand the relationship and law between soil nutrients and yield, and carry out precision fertilization to improve crop yield; maize is the largest crop in the world and China, and plays an important role in ensuring national food security. In the existing agricultural technologies to solve the problem of precision fertilization, there is no good fitting for the complex nonlinear relationship between multiple parameters of soil fertility and yield in the process of fertilization, which affects the accuracy of the fertilization model to a certain extent, and limits its application scope, Therefore, based on the project of “integration and demonstration of corn precision operation technology based on Internet of things” of national Spark Program, this paper selects soil nutrients and corn yield as research objects, and studies the optimization algorithm of maize precision fertilization model based on Fuzzy means (FCM) and RBF, It can provide decision-making basis for Precise Fertilization of maize. The main research work of this paper is as follows
Amazigh Part Of Speech Tagging using Gated recurrent units (GRU)

Gated recurrent units (GRUs) are a gating part in monotonous neural associations, introduced in 2014. The GRU looks like a long short-term memory (LSTM) with a memorable neglect entryway, however has fewer boundaries than LSTM, as it misses the mark on a yield entrance. GRU's introduction on explicit tasks of polyphonic music showing, discourse signal displaying, and normal language preparing was found to resemble that of LSTM.GRUs have been seemed to show better execution on certain more unobtrusive and less ordinary datasets. This paper investigates Gated recurrent units in TIFINAGH grammatical form (POS) marking when it has diverged from long short-term memory (LSTM). The test outcomes show that Gated recurrent units (GRU) networks act in a way that is superior to long short-term memory (LSTM).
Brand Quality Detection Based on a Comparative Analysis

Today, duplicate copies of branded products through various ways cheat us. We could not determine whether the purchased product is original or fake. Therefore, we are losing our money in these fake products. We need to become more careful while purchasing the branded products. There is no system or any ways to analyze the branded products. So we are introducing an embedded system designed for brand analysis. Brand quality detection based on comparative analysis is a controller with a dedicated function called an embedded system used often with real-time computing constraints. It consisted of hardware and mechanical parts including a Raspberry Pi module used with sensors for brand analyzer. A convolutional neural network was used to detect the object. Through the process of image processing, the original and copy product was differentiated by training the input parameters. The result showed that the proposed system is highly useful to determine the brand of a product is original or not.
APPLICATION OF UT MULTIPLIER IN AES ALGORITHM AND ANALYSIS OF ITS PERFORMANCE

From the previous few years numerous cryptographic algorithms have been implemented, giving scrupulous significance to high safety applications, i.e. for ATMs, smart cards, WWW servers & many others. Amid the specified cryptographic algorithms, the Advanced Encryption Standard (AES) algorithm is preferred algorithm. The algorithm is implemented in various bit sizes. An AES algorithm recognizes a 128-bit plain data text and generates a 128-bit cipher text under the secret key control of 128, 192 or 256-bits. Moreover in this brief, an AES algorithm with 128/192/256 bits is implemented by using Vedic Mathematics. The conventional pipelined based algorithm is compared with proposed Vedic mathematics based AES algorithm in turns of area. With the help of Xilinx the AES algorithm is simulated and synthesized. Also it can be seen that proposed algorithm occupies approximately 40% less area when compared with conventional algorithm.
Statistical causality and measurable separability of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e22" altimg="si3.svg"><mml:mi>σ</mml:mi></mml:math>-algebras

In this paper we consider a concept of statistical causality, based on Granger’s definition of causality and analyze the relationships between given causality and the concept of measurable separability of σ-algebras. The measurable separability of σ-algebras is defined in Florens et al. (1990). We give a generalization of that definition for flows of information represented by filtrations and consider some properties of measurable separability that are directly connected to the concept of statistical causality. Also, we apply some of these results on Bayesian experiment.
Reception of Perempuan Berkalung Sorban Movie among Islamic Senior High School Students

This study aims to describe the responses and reception of Perempuan Berkalung Sorban movie among Islamic senior high school students’ and its implementation in Indonesian language learning. This qualitative descriptive study employs a reception approach. Interviews, questionnaires, and documentation techniques have been performed in collecting the data. The data analysis techniques consist of several procedures, (1) identifying data from parts of the movie that elicit students’ response in the form of mimics or expressions as well as oral and written responses; (2) classifying response and reception data; (3) interpreting and describing the results of the analysis; and (4) concluding the results of the analysis. The results reveal that (1) the students’ positive response is shown by happy expression, while negative responses consist of sad, annoyed, angry, scared, shocked, disgusted, disappointed, and concerned expressions; (2) The students' verbal and written receptions to the movie are shown with positive explanations as the movie exuded the values for guide of life, and the negative ones are shown due to the treatment that marginalized women; (3) the implementation of the results on Indonesian language learning is carried out by designing a learning development. The design is based on a reception approach on the basic competencies of understanding the contents of literary works (movie) for the second semester of twelve grade students in Islamic senior high school.
Can you fool AI by doing a 180? — A case study on authorship analysis of texts by Arata Osada

This paper is our attempt at answering a twofold question covering the areas of ethics and authorship analysis. Firstly, since the methods used for performing authorship analysis imply that an author can be recognized by the content he or she creates, we were interested in finding out whether it would be possible for an author identification system to correctly attribute works to authors if in the course of years they have undergone a major psychological transition. Secondly, and from the point of view of the evolution of an author's ethical values, we checked what it would mean if the authorship attribution system encounters difficulties in detecting single authorship. We set out to answer those questions through performing a binary authorship analysis task using a text classifier based on a pre-trained transformer model and a baseline method relying on conventional similarity metrics. For the test set, we chose works of Arata Osada, a Japanese educator and specialist in the history of education, with half of them being books written before the World War II and another half in the 1950s, in between which he underwent a transformation in terms of political opinions. As a result, we were able to confirm that in the case of texts authored by Arata Osada in a time span of more than 10 years, while the classification accuracy drops by a large margin and is substantially lower than for texts by other non-fiction writers, confidence scores of the predictions remain at a similar level as in the case of a shorter time span, indicating that the classifier was in many instances tricked into deciding that texts written over a time span of multiple years were actually written by two different people, which in turn leads us to believe that such a change can affect authorship analysis, and that historical events have great impact on a person's ethical outlook as expressed in their writings.
System Identification of Dampers Using Chaotic Accelerated Particle Swarm Optimization

Aims: Different chaotic APSO-based algorithms are developed to deal with high non-linear optimization problems. Then, considering the difficulty of the problem, an adaptation of these algorithms is presented to enhance the algorithm. Background: : Particle swarm optimization (PSO) is a population-based stochastic optimization technique suitable for global optimization with no need for direct evaluation of gradients. The method mimics the social behavior of flocks of birds and swarms of insects and satisfies the five axioms of swarm intelligence, namely proximity, quality, diverse response, stability, and adaptability. There are some advantages to using the PSO consisting of easy implementation and a smaller number of parameters to be adjusted; however, it is known that the original PSO had difficulties in controlling the balance between exploration and exploitation. In order to improve this character of the PSO, recently, an improved PSO algorithm, called the accelerated PSO (APSO), was proposed, and preliminary studies show that the APSO can perform superiorly. Objective: This paper presents several chaos-enhanced accelerated particle swarm optimization methods for high non-linear optimization problems. Method: Some modifications to the APSO-based algorithms are performed to enhance their performance. Then, the algorithms are employed to find the optimal parameters of the various types of hysteretic Bouc-Wen models. The problems are solved by the standard PSO, APSO, different CAPSO, and adaptive CAPSO, and the results provide the most useful method. The sub-optimization mechanism is added to these methods to enhance the performance of the algorithm. Result: Seven different chaotic maps have been investigated to tune the main parameter of the APSO. The main advantage of the CAPSO is that there is a fewer number of parameters compared with other PSO variants. In CAPSO, there is only one parameter to be tuned using chaos theory. Conclusion: To adapt the new algorithm for susceptible parameter identification algorithm, two series of Bouc-Wen model parameters containing standard and modified Bouc-Wen models are used. Performances are assessed on the basis of the best fitness values and the statistical results of the new approaches from 20 runs with different seeds. Simulation results show that the CAPSO method with Gauss/mouse, Liebovitch, Tent, and Sinusoidal maps performs satisfactorily.
Approach to the Solution of the Optimum Control Problem of the Water Supply System

This paper considers the problem of optimal control of branched water supply systems. To control the system, the problem of optimal distribution of products is delivered whereas non-linear programming problems are applied. We consider a system for providing products, consisting of magistral and distribution pipelines, taking products from the magistral pipeline. Each distribution line has many warehouses. Products are taken into the system using the main intake facility and transferred between the warehouses using intermediate distribution facilities. To eliminate the deficiencies in management, tasks are set to determine the necessary intensities of product supply in the facilities, allowing timely provision of consumers with the necessary volume of products, to minimize losses, product discharges of facilities in the system during a certain control period.
Natural disaster topic selection using decision tree classification

Abstract Social media like Twitter used as a reference for popular and trusted news today. This opportunity could be useful manner to inform the natural disasters that occur in certain condition related to their locations. This study aims to proposed model to select the topic related to the natural disasters that occur in Indonesia based on Twitter user timeline. The selected topic will use as a reference to ascertain events, conditions of victims, and position of disaster locations. In this study, the data taken from the official Twitter account of @BNPB_Indonesia. The data extracted into two main classes, and grouped based on types of disasters, victims, and disaster locations or safe point position using decision tree algorithm.
Optimization Method for Parallel Algorithm for Face Recognition in Graphic Images

The aim of the research is to study the effectiveness of high-performance Symmetric Multiprocessing (SMP) systems. The criterion for effectiveness is the running time of the face recognition algorithm. The principal component analysis (PCA) as well as methods for parallelization and optimization of the PCA algorithm are described. The main part of the algorithm that takes the most time is parallelized and optimized for CUDA. A new mathematical model is used, which increases the speed of the algorithm. Also, work was done to find the optimal strategy for using CUDA kernels. Result: The face recognition algorithm based on the PCA is built and tested. Efficiency of the approach is illustrated with examples.