Reviewerly/exHarmony huggingface dataset:
A Mixed-Method Approach to Recommend Corrections and Correct REST Antipatterns

Many companies, e.g., Facebook and YouTube, use the REST architecture and provide REST APIs to their clients. Like any other software systems, REST APIs need maintenance and must evolve to improve and stay relevant. Antipatterns—poor design practices—hinder this maintenance and evolution. Although the literature defines many antipatterns and proposes approaches for their (automatic) detection, their <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">correction</i> did not receive much attention. Therefore, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">we apply a mixed-method approach to study REST APIs and REST antipatterns with the objectives to recommend corrections or, when possible, actually correct the REST antipatterns.</i> <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Qualitatively, via case studies</i> , we analyse the evolution of 11 REST APIs, including Facebook, Twitter, and YouTube, over six years. We detect occurrences of eight REST antipatterns in the years 2014, 2017, and 2020 in 17 versions of 11 REST APIs. Thus, we show that (1) REST APIs and antipatterns evolve over time and (2) developers seem to remove antipatterns. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Qualitatively via a discourse analysis</i> , we analyse developers’ forums and report that developers are concerned with the occurrences of REST antipatterns and discuss corrections to these antipatterns. Following these qualitative studies, using an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">engineering-research approach</i> , we propose the following novel and unique contributions: (1) we describe and compare the corrections of eight REST antipatterns from the academic literature and from developers’ forums; (2) we devise and describe algorithms to recommend corrections to some of these antipatterns; (3) we present algorithms and a tool to correct some of these antipatterns by intercepting and modifying responses from REST APIs; and, (4) we validate the recommendations and the corrections manually and via a survey answered by 24 REST developers. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Thus, we propose to REST API developers and researchers the first, grounded approach to correct REST antipatterns.</i>
Ensemble Creation using Fuzzy Similarity Measures and Feature Subset Evaluators

Current machine learning research is addressing the problem that occurs when the data set includes numerous features but the number of training data is small. Microarray data, for example, typically has a very large number of features, the genes, as compared to the number of training data examples, the patients. An important research problem is to develop techniques to effectively reduce the number of features by selecting the best set of features for use in a machine learning process, referred to as the feature selection problem. Another means of addressing high dimensional data is the use of an ensemble of base classifiers. Ensembles have been shown to improve the predictive performance of a single model by training multiple models and combining their predictions. This paper examines combining an enhancement of the random subspace model of feature selection using fuzzy set similarity measures with different measures of evaluating feature subsets in the construction of an ensemble classifier. Experimental results show that in most cases a fuzzy set similarity measure paired with a feature subset evaluator outperforms the corresponding fuzzy similarity measure by itself and the learning process only needs to occur on typically about half the number of base classifiers since the features subset evaluator eliminates those feature subsets of low quality from use in the ensemble. In general, the fuzzy consistency index is the better performing feature subset evaluator, and inclusion maximum is the better performing fuzzy similarity measure.
Monte Carlo modelling of confidence intervals in translation quality evaluation (TQE) and post-editing dstance (PED) measurement

From both human translators (HT) and machine translation (MT) researchers' point of view, translation quality evaluation (TQE) is an essential task. This is especially the case, when language service providers (LSPs) face huge amount of request frequently from their clients and users to acquire high-quality translations.While automatic translation quality assessment (TQA) metrics and quality estimation (QE) tools are widely available and easy to access, human assessment from professional translators (HAP) are often chosen as the golden standard \cite{han-etal-2021-TQA}.One challenge that comes to this point is this: \textit{to avoid the overall text quality checking from both cost and efficiency perspectives, how to choose the confidence sample size of the translated text, so as to properly estimate the overall text or document translation quality}?This work carries out such an motivated research to correctly estimate the confidence intervals \cite{Brown_etal2001Interval} regarding the sample size of translated text, e.g. the amount of words or sentences, that needs to be taken into account for confident evaluation of overall translation quality. The methodology we applied for this work is from Bernoulli Statistical Distribution Modelling (BSDM) and Monte Carlo Sampling Analysis (MCSA).
RETRACTED ARTICLE: High-performance computing system and artificial language recognition in the visual application of intangible cultural heritage art design

High-speed computing applications such as gene sequencing and MARS business models are especially important in modern computer science. However, the implementation and maintenance of the high-performance computing system platform will cause a lot of waste of resources. Therefore, in order to solve the problem of large-scale coupled processing, this paper proposes to design and implement the integrated processing framework Teno of high-performance computing system, which can calculate the tasks efficiently executed on high-performance computer system. In addition, this article focuses on artificial language recognition based on DNN (deep neural network), which has become a hot spot in academic and industry research. This article focuses on artificial language recognition based on DNN and is committed to completing a complete and well-executed language recognition system. Based on the classification and analysis of a large amount of undeveloped information about national intangible cultural heritage art in a certain province, this paper uses the method of designing information vision to make it a visual image of visual information, so that the intangible cultural heritage art information culture is well. It is possible to understand and use. Research has found that it is feasible to use information visualization to express information in intangible cultural heritage. In the process of designing cultural heritage infographics, designers must refine and perfect information and use various information principles in visualization methods to finally design clear and understandable infographics and then apply them to specific fields.
Evaluation Study of Elliptic Curve Cryptography Scalar Multiplication on Raspberry Pi4

The internet of things (IoT) is defined as a collection of autonomous devices that connect and network with each other via the Internet without the requirement for human interaction.It enhances daily our lives such as through personal devices, healthcare sensing, retail sensing, and industrial control, as well as the smart homes, smart cities, and smart supply chains.Although the IoT offers significant benefits, it has inherent issues, including security and privacy risks, memory size limitations, and processing capability challenges.This paper describes the application of elliptic curve cryptography (ECC) in a simulated IoT environment to ensure the confidentiality of data passed between the connected devices.Scalar multiplication represents the main operation of ECC, and it is primarily used for key generation, encryption, and decryption.The aim of this paper is to evaluate and show the efficiency of adapt lightweight ECC with an IoT devices.In the study outlined in this paper, scalar multiplication was implemented on Raspberry Pi4 and processing time and consumed energy were measured to compare the performance.The comparison was made on the scalar multiplication of both fast and basic ECC algorithms.The result of the performance test revealed that a fast scalar multiplication reduced the computation time in comparison with basic scalar multiplication while consuming a similar level of energy.
Improved Security In Steganography By Amalgaming Cryptography And Quick Response Code

Steganography and steganalysis established a greater pact of consideration fromcommunication services and law practices. Many powerful and secure approaches ofsteganography and steganalysis have been developed with the advent of technology.Combat between steganography and steganalysis has become a significant topic to behandled in information security. Steganalysis is an efficient technique used to hide anyencrypted data behind an image or any other digital media to make them more secure. Thisimage can be converted into a Quick Response Code which can make the encryption morepowerful. It can be said as a amalgamation of steganography and cryptography techniquesinto a single technique. The steganography can preserve the secrecy whereas thecryptography can prevent intrusion. The steganalysis can prevent intrusion and make datasecure by maintaining the secrecy of data. This can be the solution to ensure the security ofthe data as it is concerned by implementing the steganography technique for images withthe improvement on both image security & quality. This technique can prevent the dataand media from the vulnerable attacks and other several threats. It uses more securealgorithms which are unbreakable when compared to its previous techniques.
The Implementation of CTL Method in teaching English to the students of MAN 1 Enrekang

This research shows the implementation of contextual teaching and learning conducted by the English teachers of MAN 1 Enrekang in teaching English. MAN 1 Enrekang is senior high school that is located in Enrekang regency, Sulawesi Selatan, as one of the schools where most of the teachers there implement CTL method in teaching, include teaching English. However, sometimes the students still find it difficult to understand the material being taught, especially English. This research employed descriptive qualitative method, in which the data collected and analyzed qualitatively by giving description about the phenomena or facts have been found in the field. The subjects of the research were the English teachers of MAN 1 Enrekang. They are English teachers who have implemented CTL in teaching English. Based on the result, it can be concluded that the implementation of CTL method in teaching English to the students of MAN 1 Enrekang was not totally or fully conducted. There are seven elements should be applied in teaching and learning for implementing CTL. They are constructivism, inquiry, modeling, reflection, learning community, questioning, and authentic assessment. Eventhough the teachers understood and admitted implementing CTL in teaching English, the result of the observation shows that the English teachers applied only five elements of CTL in the classroom, while there were two elements of CTL those have not been applied because of the limited time in teaching and learning process, they are constructivism and inquiry. The element of CTL that was applied frequently is questioning
Robust Tightly Coupled GNSS/INS Experimental Assessment for Autonomous Aircraft Inspection

The combination of the Global Navigation Satellite System (GNSS), commonly used as economic outdoor navigation solution, and the Inertial Navigation System (INS) is widely developed and has become the baseline for numerous autonomous applications. However, the classical tightly coupled GNSS/INS (TC-GNSS/INS) navigator is very vulnerable to multiple faulty measurements in challenging GNSS conditions, including multipaths, partial sky visibility and non-line-of-sight (NLOS). This article addresses the problem of achieving a robust low-cost TC-GNSS/INS solution of an autonomous robot for the aircraft ground inspection application. In such a challenging navigation scenario, the performances of the classical TC-GNSS/INS can be strongly degraded by contaminated GNSS measurements. In literature we can find a certain number of contributions on the design of estimation algorithms based on robust statistics, which show that the robust TC-GNSS/INS approach is capable of mitigating large positioning errors in simulations or some specific experiments. To the best of our knowledge, for the purpose of aircraft ground inspection by autonomous robot, there is no related evaluation work with experimental data. To fill this lacuna, we propose to assess the use of a robust statistical approach, based on an M-Estimator formulation in a TC-GNSS/INS framework, with inertial and GNSS measurements of a real aircraft ground inspection scenario, where the faulty measurements are no more subject to Gaussian law. The implemented M-estimation-based robust extended Kalman filter (M-EKF) is compared with the standard EKF (S-EKF) and the navigation solution provided by the commercial u-blox M8T GNSS receiver. A series of data analysis on the behaviour of the M-EKF along with the GNSS raw measurements is carried out and discussed in details. The results show that the M-EKF is capable of mitigating the unexpected large errors by applying appropriate weights on the measurements outliers. This work provides a deep analysis on the use of the robust estimator in the case of an autonomous robot for the aircraft ground inspection. We also provide references and perspectives on this subject.
Domain Adaptation-Based Automatic Modulation Recognition

Deep learning-based Automatic Modulation Recognition (AMR) can improve the recognition rate compared with traditional AMR methods. However, in practical applications, as training samples and real scenario signal samples have different distributions in practical applications, the recognition rate for target domain samples can deteriorate significantly. This paper proposed an unsupervised domain adaptation based AMR method, which can enhance the recognition performance by adopting labeled samples from the source domain and unlabeled samples from the target domain. The proposed method is validated through signal samples generated from the open-sourced Software Defined Radio (SDR) GNU Radio. The training dataset is composed of labeled samples in the source domain and unlabeled samples in the target domain. In the testing dataset, the samples are from the target domain to simulate the real scenario. Through the experiment, the proposed method has a recognition rate increase of about 88% under the CNN network structure and 91% under the ResNet network structure.
A Novel FPGA-Based Intent Recognition System Utilizing Deep Recurrent Neural Networks

In recent years, systems that monitor and control home environments, based on non-vocal and non-manual interfaces, have been introduced to improve the quality of life of people with mobility difficulties. In this work, we present the reconfigurable implementation and optimization of such a novel system that utilizes a recurrent neural network (RNN). As demonstrated in the real-world results, FPGAs have proved to be very efficient when implementing RNNs. In particular, our reconfigurable implementation is more than 150× faster than a high-end Intel Xeon CPU executing the reference inference tasks. Moreover, the proposed system achieves more than 300× the improvements, in terms of energy efficiency, when compared with the server CPU, while, in terms of the reported achieved GFLOPS/W, it outperforms even a server-tailored GPU. An additional important contribution of the work discussed in this study is that the implementation and optimization process demonstrated can also act as a reference to anyone implementing the inference tasks of RNNs in reconfigurable hardware; this is further facilitated by the fact that our C++ code, which is tailored for a high-level-synthesis (HLS) tool, is distributed in open-source, and can easily be incorporated to existing HLS libraries.
From Challenges to Opportunities: Reorganization of Teacher Education during the COVID-19 Pandemic in Post-Socialist Countries

Although the COVID-19 pandemic is far from over, there is no doubt that education researchers will analyze the changes in teaching and learning over the years to come. There is no certainty that new waves and variants of COVID-19 will not force humanity to radically change educational technologies in the future. This will bring mankind closer to the predictions of science-fiction authors and futurists, not taken seriously before. In this regard, it is important to scientifically document and analyze various measures for transforming education in the current situation, thus, creating the database for future generations. Analysis reveals that geographically adjacent countries with numerous current or historical interrelations, show the uniqueness of their response to the pandemic because of a number of current economic, social, cultural and geographical factors. Quite promising in this regard are the post-socialist countries of Central and Eastern Europe, which in the second half of the 20th century had almost identical principles for developing national education systems, but changed significantly under the influence of the reforms of the last thirty years. Comparing the cases of five universities from Russia, Germany, Bulgaria, the Czech Republic and the Pridnestrovian Moldavian Republic enable us, not only to identify common problems, but also to describe the most effective measures for the reorganization of higher education during the COVID-19 pandemic, dictated by the need to sustain the quality of teacher training.
A Novel Hybrid Approach for Fault-Tolerant Control of UAVs based on Robust Reinforcement Learning

The control of complex autonomous systems has significantly improved in recent years and unmanned aerial vehicles (UAVs) have become popular in the research community. Although the use of UAVs is increasing, much work remains to guarantee fault- tolerant control (FTC) properties of these vehicles. Model-based controllers are the standard way to control UAVs, however obtaining models of the system and environment for every possible operating condition a UAV can experience in a real-world scenario is not feasible. Reinforcement Learning has shown promise in controlling complex systems but requires training in a simulator (requiring a model) of the system. Further, stability guarantees do not exist for learning-based controllers, which limits their large scale application in the real-world. We propose a novel hybrid FTC approach that uses a learned supervisory controller (together with low-level PID controllers) with key stability guarantees. We use a robust reinforcement learning approach to learn the supervisory control parameters and prove stability. We empirically validate our framework using trajectory-following experiments (in simulation) for a quadcopter subject to rotor faults, wind disturbances, and severe position and attitude noise.
Development of To-Do List and Monetary Management System

Currently, most of the available “To-Do List” Applications on Android or iOS are just consisted of a “To-Do list” and integrated with an electronic calendar. These applications are just suitable for individual use for keeping alert daily “To Do List” and its functionality is limited to update the status of each task. The other limitation of these applications is the “To-Do List” could not be easily shared and updated if involved other group members. Therefore, a new “Group To- Do List” plus Monetary Management System application was proposed. The proposed application can be shared and updated by group members of interest in communicating and monitoring “To Do List’ and fund management. This application was developed based on Rapid Application Development Method (RAD) by understanding the natural practices of task management and quantities of tasks taken on by team members. Therefore, this app can be expanded and customized to integrate other attractive functions based on user requests. This new application is suitable for family members, project group members, or even individuals from all walks of life who are interested in managing a total integrated “To-Do List” plus finance management system. Some of the important features of this application are its ability to identify tasks that need to be completed on daily basis with indications of which tasks need financial support, and which tasks could generate income. An interactive table and bar charts have summarized all of these tasks with details of expenditure/income and are integrated into an electronic calendar. The status of these tasks could be updated by group members with ease on individual tablets/handphones/computers/laptops. This application can be expanded/customized to integrate other attractive functions based on user requests.
Static Analysis of Endian Portability by Abstract Interpretation

We present a static analysis of endian portability for C programs. Our analysis can infer that a given program, or two syntactically close versions thereof, compute the same outputs when run with the same inputs on platforms with different byte-orders, a.k.a. endiannesses. We target low-level C programs that abuse C pointers and unions, hence rely on implementation-specific behaviors undefined in the C standard.Our method is based on abstract interpretation, and parametric in the choice of a numerical abstract domain. We first present a novel concrete collecting semantics, relating the behaviors of two versions of a program, running on platforms with different endiannesses. We propose a joint memory abstraction, able to infer equivalence relations between little- and big-endian memories. We introduce a novel symbolic predicate domain to infer relations between individual bytes of the variables in the two programs, which has near-linear cost, and the right amount of relationality to express (bitwise) arithmetic properties relevant to endian portability. We implemented a prototype static analyzer, able to scale to large real-world industrial software, with zero false alarms.
Optimization of Agent-Based Models Through Coarse-Graining

Optimization and control are important objectives across biology and biomedicine, and mathematical models are a key enabling technology. This paper reports a computational study of model-based multi-objective optimization in the setting of microbial ecology, using agent-based models. This modeling framework is well-suited to the field, but is not amenable to standard control-theoretic approaches. Furthermore, due to computational complexity, simulation-based optimization approaches are often challenging to implement. This paper presents the results of an approach that combines control-dependent coarse-graining with Pareto optimization, applied to two models of multi-species bacterial biofilms. It shows that this approach can be successful for models whose computational complexity prevents effective simulation-based optimization.