Reviewerly/exHarmony huggingface dataset:
Text Editing for Augmented Distilled BERT

BERT is a pre-trained deep bidirectional transformer model, which can be fine-tuned to achieve state-of-the-art performance in a wide range of downstream tasks. BERT has hundred millions of parameters, which requires long training time and limits its application. Recent studies apply knowledge distillation to compress BERT into a smaller and lighter version of BERT. However, reduced model size also mean compromised performance. In this paper, we propose to use text editing for improving knowledge distillation. We compare the effectiveness of random insertion, swap, deletion and synonym replacement in sentiment classification tasks. Our experimental results show that text editing is a simple yet effective data augmentation technique for knowledge distillation. Our distilled model augmented by text editing outperforms previous distilled models.
Fuzzy Separation And Shrinkage Clustering

Abstract Clustering has many applications in data mining and machine learning. Fuzzy clustering methods have been widely used in clustering. However, fuzzy clustering methods still have a fatal problem: the cluster radius sensitivity problem. The cluster radius sensitivity problem means that clusters with smaller radius will predominate in clustering and obtain more data points. Aiming at this problem, we propose a fuzzy separation and shrinkage clustering algorithm (FSC). FSC uses cluster membership degrees and cluster sizes to construct a new membership distribution, and then moves the data points according to this new membership distribution. The accuracies of our algorithm on wine, iris, balance scale and seeds are as follows: 98.82%, 97.27%, 63.07% and 91.34%. Our contributions are: (1) We propose a fuzzy separation and shrinkage clustering algorithm, which can solve the cluster radius sensitivity problem. (2) The performance of our algorithm on the UCI datasets goes beyond the benchmark algorithms.
AGE BASED PERPETRATION OF MENTAL STRESS USING PHARMED SOCIAL NETWORKING SITES ON INDIVIDUALS IN INDIA

The process of globalization in India has paved the way for rapid growth in social networking sites. The number of people using social networking sites has increased from time to time. The social networking sites have attracted people from different age groups. As a result of which it is used as a medium for illicit activity by criminals across the globe. Pharming is used as the most common way to commit a cyber crime through social networking sites. Pharming attacks divert users from real Web sites they are attempting to visit to false Websites. These false Web sites seem to be like genuine sites. The studies have enumerated that pharming has created adverse effects on the individuals, however the main concept that has to be noted is that social networking sites are used by people from different age groups. In this case a natural ambiguity arises as to whether as to who are likely to endure mental stress and become a victim of cyber crime through pharming in social networking sites. An empirical study done using non probability convenience sampling method and analysed using Chi square method reveals that there is no Association between mental Stress Perpetrated Through Cyber Crime By Pharmed Social Networking Sites on Individuals and age factor and the study recommends that the user have to spend more time to check the URL of the social networking sites.
The Weeks that Could Have Changed the Pandemic Course: on the Conflict Between Health, Economic and Personal Rights

Based on the observation of the behavior of local and national governments during the pandemics and the underlying conflicts between different actors and visions, in this contribution I briefly expose some reflections on the necessary triangulation between health, economic and personal rights and the reasons of governments reluctance to implement the measures recommended by sanitary institutions.
A Relative Comparison of Training Algorithms in Artificial Neural Network

Artificial Neural Networks (ANNs) give a practical, general method for learning discrete-valued, real-valued, and vector-valued functions from examples. The algorithms used for training models aim to construct an optimization framework and apprehend the parameters in the target function from the provided dataset. ANN learning is vigorous to errors in the training data which has been successfully applied to scenarios like speech recognition, interpreting visual scenes and robotics. This paper basically aims to provide an experimental study to compare various optimization or training algorithms and determines the best suited optimization method corresponding to a particular dataset in terms of accuracy and loss. To the best of our knowledge, this paper is the first to consider different learning rate values to study the comparison between different training or optimization algorithms.
Developing Learning Media of Electronic Fuel Injections (EFI) System Android-Based in Piri 1 Yogyakarta Vocational High School

Abstract This research aims to: (1) producing android-based Electronic Fuel Injections (EFI) learning media; (2) knowing the feasibility of android-based EFI learning media; and (3) knowing students’ responses to the learning media of the Android-based Electronic Fuel Injections (EFI) system. The rsearch was conducted using the Borg and Gall model modified by Sugiyono. The technique of data collection is done by questionnaire, while the data analysis technique uses descriptive quantitative methods. The results of the research were obtained: (1) products in the form of Android-based Electronic Fuel Injections (EFI) system learning media; (2) learning media in terms of material included in the very feasible category and in terms of media included in the very feasible category; and (3) students’ responses when testing the product, the aspects of use are included in the category very easily and the aspects of usefulness are included in the category of very useful. The response of students when testing the use, the aspects of use included in the category is very easy and the usefulness aspects including the category is very useful.
Learning Speaking Using Flash Cardboard: English as a Foreign Language Students’ Perception

Flash cardboard is one of the media used for speaking, especially in storytelling. Teaching by using media is very necessary for learning because it greatly influences the results of the learning process. This research wants to know English as a Foreign Language (EFL) students’ perception after they learned storytelling using flash cardboard as a media. This was phenomenological research. This research used semi-structured interviews with some students who used flash cardboard in learning storytelling. Purposive sampling was used in this research. The result shows that the student got positive impacts facilitated by the flash cardboard media, they became more confident in speaking in front of the class, they were not involved because flash cardboard helped them, students also supported speaking fluently, they can produce more than 190 words per minute (wpm) but not necessarily perfect command of intonation, vocabulary, and grammar. By using flash cardboard as the media, the media can be an alternative method in learning English specifically speaking class because it forces students to speak (really make them speak) following the target language. In the end, it can also be concluded that learning English using flash cardboard media can improve students' language skills and speaking skills. The students maintain their ability in speaking smoothly not necessarily perfect command of intonation, vocabulary, and of course the grammar. The recommendation for further study is the other researchers can add the respondents with another English skill.
Modeling Authentication in Mal Sequence Diagram

Software Requirement Specification involves both functional and Non Functional Requirements (NFRs), defines system attributes and serves as a constraint on software design.While designing software, NFR in general and security requirements in specific are neglected or poorly designed since they are not treated as an important part of designing.As a consequence of which our software is vulnerable to security threats.Modeling software threats in early stages of software engineering i.e. design can help developers to identify security threats and design their corresponding mitigation.We therefore in this paper model authentication threats e.g.skimming and replay attack in mal sequence diagram.In order to do so an extended meta-model of sequence diagram designed based upon which profile mal sequence diagram is designed.Both skimming and replay attack and their corresponding mitigation is successfully modeled upon ATM various usage scenarios of ATM case study.
Two-dimensional pattern matching against local and regular-like picture languages

Given a two-dimensional array of symbols and a picture language over a finite alphabet, we investigate how to find rectangular subarrays that belong to the picture language. Two-dimensional pattern matching problems can be formulated by interpreting subarrays as matches and picture languages as patterns. We formulate four particular problems – finding maximum, minimum, any or all match(es) – and describe algorithms solving the problems for basic classes of picture languages, which include local picture languages and picture languages accepted by various types of deterministic two-dimensional finite automata such as four-way, three-way and two-way automata, on-line tessellation automata, and returning finite automata. We also prove that the pattern matching problems cannot be solved for the class of local picture languages in time linear in the input area unless the well known problem of triangle finding in a graph is solvable in quadratic time. This shows a fundamental difference between the complexity of one-dimensional and two-dimensional pattern matching.
Improving the Productivity of Distributed Computer Systems Through the Use of Knowledge Engineering

To solve the problem of improving the productivity of distributed computer systems, special systems are being developed to assess the performance of individual nodes, errors in the composition of services, time delays, etc. The expert can decide to optimize the system based on these data. This paper proposes the use of knowledge engineering tools in systems development. The proposed construction methodology is to present semantic descriptions of all existing nodes in the system. Service ontologies allow you to develop systems that can accumulate expert knowledge to identify common system performance issues and ways to address them. A software environment for working with ontologies and knowledge base was developed in the process of performing this work. The knowledge base for identification of typical problems of productivity of services, described with use of the offered models and methods is developed. The results of the study of the developed models, methods and tools in the analysis of service performance have shown their effectiveness.
Research on Trust Mechanism based on Delegation Relationship in Sharing Economy

Trust plays a very important role in the development of sharing economy. What’s more, delegation is taken one of evaluation factors as service trust computation. However, the sharing economy based on the delegation also brings security risk. Therefore, this paper proposes a novel trust mechanism based on delegation relationship. Firstly, the paper analyzes a variety of deception which exists in the delegation relationship, then puts forward the concept of the transaction evidence and commission evidence. Secondly, by combining these two kinds of evidence, a novel method is presented. It improves computing trust value algorithm in the commission relationship by applying the evidence theory from three aspects characteristics, relevance evaluation information of evidence and user trust degree. At last, the trust mechanism is established on account of the commission relationship. The effectiveness of this trust mechanism is testified through simulation &amp; emulation experiments.
Application of Grey System Model in Overtaking with High Benefit

This paper applies grey theory to predict high-efficiency overtaking in order to achieve effective overtaking prediction. Through the monitoring of overtaking data by electronic police, effective control of road network security is achieved. This model has a certain warning effect on the occurrence of large-scale overtaking, and has a certain guiding significance for the management and control of the road network and the elimination of hidden dangers. The overtaking phenomenon of the road network is affected by many factors and has the characteristics of high noise and multiple disturbances. Applying the GM (1,1) model in the gray system can effectively resist shock disturbance traps. According to the characteristics of high-efficiency overtaking data, small data is used for real-time modeling, and the model will constantly change according to time. This article uses GM (1,1) modeling, which has good performance in small data and high noise modeling, in order to achieve early warning of road overtaking control.
Forecasting the Solar Power Plants Generation in a Meteorological Data-Constrained Environment

This paper is devoted to increasing the accuracy of forecasting the solar power plants generation in Russia in a data-constrained environment. Within this study, an analysis of forecasting parameters influence on the solar power plants generation based on the regression analysis methods was made. The study object was a real power system, with the installed capacity of 3915 MW, 330 MW of which is the solar power plants' share. The study also analyzed the change in the average price indice of the balancing market in Russia, depending on solar power plants presence.
A Tool for Product Derivation and Semi-Automatic Deployment of Web-Based Systems

This paper presents a tool for product derivation and semi-automatic deployment of web-based systems. The tool focuses on cloned web-based systems implemented using the PHP language, which are refactored to become software product lines. The paper also reports the results of a case study of using the tool in the context of a large scale web-based system. Our findings show some preliminary evidence of the benefits of the tool to reduce: (i) the time of product derivation and deployment; and (ii) the manual effort to customize existing cloned web-based systems.
Scaling Experience Measurement: Capturing and Quantifying User Experiences across the Real Estate Journey

Zillow is undergoing a major evolution, transitioning from serving as the world's largest digital marketplace for real estate advertising into an end‐to‐end platform to support customers across the phases of buying, selling, and renting homes. As Zillow expands into more transactional spaces, the company has recognized the need to develop a clear and actionable understanding of users and their experiences as they interact with our products and services. To address this need, we set out to establish an Experience Measurement program to provide organization‐wide visibility into how well our Zillow experiences meet users’ needs and expectations as they progress through their real estate journey. This program will enable teams to gain insights at the intersection of attitudinal and behavioral experience data and lead us to our end goal of empowering informed decision‐making across all levels of the experience and organization. In this paper, we provide an overview of our approach to establishing an Experience Measurement program at Zillow. We focus on a small subset of Experience Outcomes (XOs) in an initial feasibility study to develop a program that would scale and drive impact in assessment and decision making across lines of business. Finally, we share lessons learned throughout the process of developing and validating the framework and discuss the impact of this work on current and future organizational outcomes.
Gaussian Image Denoiser Based on Deep Convolutional Sparse Coding with Attention Mechanism

Image denoising is the foundation of computer vision and a classic research project of computer vision. Currently, image denoising is widely used in various fields. In order to perform image denoising more effectively, we propose a deep convolutional sparse coding based on the attention mechanism (DCSCA) denoising model. In order to identify noise and remove noise more effectively, we sparsely encode the feature map instead of encoding the original image. Our model introduces attention blocks in the network structure, which allows our model to mine the noise hidden behind complex images. Our model has the advantages of a compact network structure and does not involve complex optimization models. We did not use the traditional mean square error (MSE) as our loss function, and chose to combine the loss function L1 with ssim. Compared with the most advanced methods, our model can produce better denoising results than some of the latest methods.
Pietro Monti y el teatro del Siglo de Oro Estrategias de traducción del humor

This article deals with the humor translation in Italian through the analysis of two comedies by Calderón de la Barca. In the 19th century, Pietro Monti published several volumes with different plays of the Spanish Golden Age, among which are Amar después de la muerte and Casa con dos puertas malas es de guardar . This article focuses on two problem areas, language-specific and culture-specific jokes, and it analyses the strategies used for Italian translation, in particular, the forms of compensation and adaptation or cases of ‘untranslatability’.
Prediction of treated water turbidity and its parametric behavior for the coagulation process of a water treatment plant using a joint extreme learning machine-genetic algorithm

Abstract A new genetic algorithm was developed to improve the performance of the extreme learning machine radial basis function (ELM-RBF) neural network model to predict treated water (TW) turbidity for the coagulation process of water treatment. The genetic algorithm was constructed such that a notable improvement in the model could be achieved, within a short period of time. Two sets of models were developed based on high and low turbidity values. The genetic algorithm improved the correlation coefficient (R) and the mean squared error (MSE) of the low turbidity model from 0.76 and 2.16 × 10-4 to 0.81 and 4.85 × 10-5 respectively, in 15 minutes; whilst improving R and MSE of the high turbidity model from 0.62 and 0.0011 to 0.93 to 2.89 × 10-4 respectively, in 2 minutes. The ability of the model to capture the reported variation of TW turbidity with gradually increasing coagulant dosage was also tested. It was noted that ELM-RBF was more capable of capturing such physical and chemical phenomena better than multilayer perceptron and ELM-single layer feed-forward models. The genetic algorithm improved the ability of the model to capture the parametric behavior of TW turbidity too.
Assessment of Heavy Metal Concentration in Soil Using Remotely Sensed Data

Remote Sensing has been used nowadays for environmental monitoring as it offers a faster and less expensive way of monitoring the environment. With various activities conducted around the Tubay catchment (e.g., mining, agriculture), monitoring the quality of its soil by determining the heavy metal concentration (HMC) in soil, mainly its Lead (Pb) content, became the main objective of this study. Remote sensing technologies, together with field data, are used in this study to create a model that would predict the lead content of the soil in Tubay catchment through statistical analysis. The model created in this study is used in an ArcGIS software. It resulted to a model-predicted value of -263.993 ppm of Lead in minimum, and a model-predicted value of 308.482 ppm of Lead in maximum. Due to the soil test result, which yields a majority of Not Detected values, the model created in this study is not validated.
Spark-Tuner: An Elastic Auto-Tuner for Apache Spark Streaming

Spark has emerged as one of the most widely and successfully used data analytical engine for large-scale enterprise, mainly due to its unique characteristics that facilitate computations to be scaled out in a distributed environment. This paper deals with the performance degradation due to resource contention among collocated analytical applications with different priority and dissimilar intrinsic characteristics in a shared Spark platform. We propose an auto-tuning strategy of computing resources in a distributed Spark platform for handling scenarios in which submitted analytical applications have different quality of service (QoS) requirements (e.g., latency constraints), while the interference among computing resources is considered as a key performance-limiting parameter. We compared Spark-Tuner to two widely used resource allocation heuristics in a large scale Spark cluster through extensive experimental settings across several traffic patterns with uncertain rate and application types. Experimental results show that with Spark-Tuner, the Spark engine can decrease the p-99 latency of high priority applications by 43% during the high-rate traffic periods, while maintaining the same level of CPU throughput across a cluster.
Pah Pum

This article presents Pah Pum, a project management tool based on Takt PM, a hybrid project management methodology. The tool was developed at SENAI Institute of Innovation for Information and Communication Technologies to facilitate adherence to Takt PM, offering processes systematization and using best practices suggested by the methodology. The tool supports (1) project planning using a service package diagram called Handoff Network, (2) project monitoring through Kanban and Kanban Quantum boards, (3) automatic Gantt generation and update, according to the project Handoff Network and service packages deliveries, (4) monitoring teams through the observation of a unified board that demonstrates the allocation of the team with the activities that are running or waiting to be performed, (5) automatic allocation of service packages to the teams considering the limit of in progress activities of the team configured to the organization, implementing the pull system, (6) status report of service package delivery and project progress, in a non-declarative way with evidences, and (7) visualization of the project timeline showing the transaction history that includes the project start, service packages deliveries, and service packages rejections that were performed during the project. We used an MVC software architecture to implement the solution, composed by front-end in Vue.js, an API following the REST standard in .Net core, and a SQL Server database. Also, the tool was integrated with the SENAI Technology Management System (a particular ERP system). All modules developed were dockerized and deployed on AWS machines. To access the tool, sign up on https://pahpum.isitics.com, and the administrators will approve the access. We are currently evaluating the tool through a qualitative study to understand the positive and negative impacts through the tool's adoption during the execution of real projects.
Desarrollo e implementación de un perfilómetro óptico por triangulación láser

Profilometers are instruments commonly used in surface metrology tasks. These instruments play a key role in the industry, such as in manufacturing and quality assurance. However, conventional profilometry techniques require direct contact with the object. In this work, we developed a practical and low-cost optical profilometer for contactless profilometry. The proposed profilometer is based on a laser triangulation system. A laser spot is projected onto the surface of an object, which is captured by a camera. The images are processed to detect the laser spot's location and obtain the object's surface height through calibration. We designed and built the translation stage to process the object's surface. Finally, encouraging results show that the profilometer provides high accuracy with fast acquisition.