Reviewerly/exHarmony huggingface dataset:
AUTO-DISCERN: Autonomous Driving Using Common Sense Reasoning.

Driving an automobile involves the tasks of observing surroundings, then making a driving decision based on these observations (steer, brake, coast, etc.). In autonomous driving, all these tasks have to be automated. Autonomous driving technology thus far has relied primarily on machine learning techniques. We argue that appropriate technology should be used for the appropriate task. That is, while machine learning technology is good for observing and automatically understanding the surroundings of an automobile, driving decisions are better automated via commonsense reasoning rather than machine learning. In this paper, we discuss (i) how commonsense reasoning can be automated using answer set programming (ASP) and the goal-directed s(CASP) ASP system, and (ii) develop the AUTO-DISCERN system using this technology for automating decision-making in driving. The goal of our research, described in this paper, is to develop an autonomous driving system that works by simulating the mind of a human driver. Since driving decisions are based on human-style reasoning, they are explainable, their ethics can be ensured, and they will always be correct, provided the system modeling and system inputs are correct.
Web Service Sistem Informasi Terpadu (SIMIPA) Menggunakan REST API

SIMIPA web service is a service that is used to provide services in the form of information to the Android mobile apps, is the Student module, the Lecturer module, and the Parent module. The service provided uses the REST API as a bridge between the server and the client. The goal is that the REST API can be accessed on the Android mobile apps. The development of web services in this study uses the Scrum development method which has 26 product backlogs with 9 iterations of sprints. Each sprint has several stages, namely sprint planning, daily sprint, sprint review, and sprint retrospective. Web services developed using the PHP programming language and MySQL database. The result of this research is the SIMIPA REST API which can be accessed on the Android front-end. Android accesses the REST API via the GET and POST request methods using a URL that generates a JSON response. Based on several tests, the SIMIPA REST API URL is said to have been successfully developed and can be accessed on Android mobile apps according to the expected response. Data security using the REST API is focused on the POST method using JSON Web Token (JWT).
Development of a Fraud Detection Model Using an Integrated Approach Based on the Factor Analysis Model and the Artificial Neural Network Method in Firms Listed in Tehran Stock Exchange

The main purpose of this research is to propose a fraud detection model using an integrated approach based on the factor analysis model and the artificial neural network method. The network used to predict corporate financial fraud has 17 neurons (selected financial ratios) in the input layer and 1 neuron (corporate fraud status) in the output layer. The conversion function used in the output layer is linear and for the middle layer a non-linear sigmoid function is selected. The neural network used in this research is a feed forward neural network with back propagation algorithm. The statistical population of this study is comprised of the companies listed in Tehran Stock Exchange in the time interval from1392 to 1393. Out of these companies, 140 have been selected as the research sample. The Beneish M-Score model has been used in order to classify the companies with the likelihood of fraudulent and non-fraudulent reporting. According to the Beneish M-Score Model, 78 companies were fraudulent in terms of their reports and 62 were non-fraudulent. For the final selection of the input variables (financial ratios) in the artificial neural network, the confirmatory factor analysis model and the principal component analysis model have been used. The results obtained from the aforementioned models have shown that the reported structure of the neural network model has 7 hidden layer neurons and the momentum learning algorithm has been used for training the network. This algorithm was more precise and functioned better than other reviewed structures. Therefore, it was selected as the final adjustment of the neural network.. The obtained results indicated that the artificial neural network method had a higher performance in this regard; in that the precision of classification of fraudulent and non-fraudulent firms and the overall performance of the artificial neural network method was57and 69%,72,73%,61,62% respectively.
A Statistical Hypothesis Testing of Analysis of Variance for Two Way Classification in Fuzzy Environments

The Analysis of Variance (ANOVA) is the most popular technique for testing of significance. ANOVA for one way classification model is a series of observations distributed over the different levels at single factor and the two-way classification model is the set of observations distributed over different levels of two simultaneous factors. In real-life situations, most of the observations cannot be recorded or collected precisely. A fuzzy environment is the decision-making process in which the goals and the constraints, but not necessarily the system under control, are fuzzy in nature. The two-way classification model is the collection of observations of two simultaneous variables distributed over challenging levels without the h -level concept of Trapezoidal Fuzzy Numbers (TZFNs). In this paper a statistical hypothesis testing of ANOVA two way classification with fuzzy environments through an numerical examples.
Diretrizes para o estabelecimento de um checklist para curadoria de dados de pesquisa

Research data curation is a challenge for many research institutions and professionals responsible for managing data repositories. The scientific community has been adhering to the practices of sharing and reuse, and in this sense, it is increasingly necessary to decide on the preservation of certain data sets to the detriment of others. This paper presents the results of a documentary research on requirements for data curation, established by repositories or institutions dedicated to research on the subject, and proposes some guidelines for a checklist of criteria that can be applied to Brazilian repositories.
Concise Review of Acoustic Guitar Modeling Technologies

Numerical modeling and simulation of multiphysics coupled systems is still a potentially fruitful field of research, even when it comes to intrinsically linear or linearized formulations. This is usually the case with computational models of vibroacoustics coupled systems, where it is not uncommon that the combined effect of many localized geometrical missmodeling with significant uncertainty in mechanical characterization of some materials - typical for organic fibers, for instance - propagates to large discrepancies in the natural frequencies and mode shapes obtained from ordinary finite element analysis. The main goal of this work is to compare two basic approaches for the modeling of stringed musical instruments in the frequency domain: finite element modal analysis and simplified lumped-parameters analytic modeling considering only the most influencing degrees-of-freedom (accordingly yielding the first few resonances). Thus, the focus here is to review some important references in this topic, including previous work from the authors which is expected to bring useful alternative ways of addressing some specific problems. Albeit restricted to the slowly varying structural behavior, simplified analytic models proved well capable of closely representing the dynamics of the lower frequency range, while still amenable to the identification of its parameters through simple routines relying on experimental data. On the other hand, properly updated finite element models allowed long-sought connections between physically relevant values and crucial subjective quality descriptors - essential pieces of musicians' and luthier's vocabulary - to be eventually found.
A Survey of Approaches to Automatic Question Generation:from 2019 to Early 2021

To provide analysis of recent researches of automatic question generation from text,we surveyed 9 papers between 2019 to early 2021, retrieved from Paper with Code(PwC). Our research follows the survey reported by Kurdi et al.(2020), in which analysis of 93 papers from 2014 to early2019 are provided. We analyzed the 9papers from aspects including: (1) purpose of question generation, (2) generation method, and (3) evaluation. We found that recent approaches tend to rely on semantic information and Transformer-based models are attracting increasing interest since they are more efficient. On the other hand,since there isn’t any widely acknowledged automatic evaluation metric designed for question generation, researchers adopt metrics of other natural language processing tasks to compare different systems.
Incorporating Domain Knowledge into Language Transformers for Multi-Label Classification of Chinese Medical Questions

In this paper, we propose a knowledge infusion mechanism to incorporate domain knowledge into language transformers. Weakly supervised data is regarded as the main source for knowledge acquisition. We pre-train the language models to capture masked knowledge of focuses and aspects and then fine-tune them to obtain better performance on the downstream tasks. Due to the lack of publicly available datasets for multi-label classification of Chinese medical questions, we crawled questions from medical question/answer forums and manually annotated them using eight predefined classes: persons and organizations, symptom, cause, examination, disease, information, ingredient, and treatment. Finally, a total of 1,814 questions with 2,340 labels. Each question contains an average of 1.29 labels. We used Baidu Medical Encyclopedia as the knowledge resource. Two transformers BERT and RoBERTa were implemented to compare performance on our constructed datasets. Experimental results showed that our proposed model with knowledge infusion mechanism can achieve better performance, no matter which evaluation metric including Macro F1, Micro F1, Weighted F1 or Subset Accuracy were considered.
Novel Methods for Secure Image Transmission: A Comprehensive Review

Owing to numerous factors such as continuous developments in technology and communication systems, a massive increase in the bulk production of image data for different purposes and many more, the confidentiality of images is becoming more difficult in the modern world. Different strategies for safe image transmission over communication networks have been studied, but in the real world, some of the latest technologies are not very suitable as the number of users grows day by day. In the world, almost every person connects with the aid of the internet through communication devices. There are several types of image formats that are transmitted by communication media.These types of images transmitted through communication media consist of either some sensitive or reserved data requiring high secrecy in order to protect the confidentiality, integrity or authentication of the images.
Pandemic Surveillance Discrimination

The COVID-19 pandemic has laid bare the abiding tension between surveillance and privacy. Public health epidemiology has long utilized a variety of surveillance methods—such as contact tracing, quarantines, and mandatory reporting laws—to control the spread of disease during past epidemics and pandemics. Officials have typically justified the resulting intrusions on privacy as necessary for the greater public good by helping to stave off larger health crisis. The nature and scope of public health surveillance in the battle against COVID-19, however, has significantly changed with the advent of new technologies. Digital surveillance tools, often embedded in wearable technology, have greatly increased the ability of governments and private corporations to monitor large sections of society while collecting massive amounts of personally identifiable data from millions of persons around the world—often with little to no regulatory oversight (or legal limits) on how that information may be later used. Surveillance responses to public health crises have also historically, disproportionately, targeted racialized communities, leading to a normalization of both racial discrimination and inequality. The world certainly must use all means to end the devastating COVID-19 pandemic. We also need to be careful, however, to not undermine individual privacy rights or engage in racialized responses to the current crisis. This Essay examines the discord between public health surveillance and privacy rights and argues that the bio-surveillance technologies being used to respond to the COVID-19 pandemic—such as contact tracing apps, GPS ankle monitors and other wearables, the collection of cell phone location data, genomic testing, and targeted quarantines—can potentially exacerbate discrimination against racial minorities and immigrants. The Essay concludes with legal and policy solutions on how to utilize public health surveillance tools to prevent the spread of COVID-19 while guarding against privacy violations and racial bias.
Anonymous IBE from PEKS: A Generic Construction

Abdalla et al.[, ] proposed a transform of an anonymous IBE (A-IBE) scheme to a PEKS (Public key encryption with keyword search) scheme. Boneh et al. proposed a transform of a PEKS scheme to an A-IBE scheme for only one-bit message. A transform for constructing an A-IBE scheme for polynomially many-bit message by using a PEKS scheme has not been proposed yet. To construct a transform from any PEKS scheme to an A-IBE scheme for a polynomially many-bit message, we firstly define a multiple PEKS (mPEKS) scheme and show that a mPEKS scheme can be constructed from a PEKS scheme. We also prove that if a PEKS scheme is confidential, so is the resulting mPEKS scheme. We then provide a transform from a mPEKS scheme to an A-IBE scheme for a polynomially many-bit message.
AFEC: Active Forgetting of Negative Transfer in Continual Learning

Continual learning aims to learn a sequence of tasks from dynamic data distributions. Without accessing to the old training samples, knowledge transfer from the old tasks to each new task is difficult to determine, which might be either positive or negative. If the old knowledge interferes with the learning of a new task, i.e., the forward knowledge transfer is negative, then precisely remembering the old tasks will further aggravate the interference, thus decreasing the performance of continual learning. By contrast, biological neural networks can actively forget the old knowledge that conflicts with the learning of a new experience, through regulating the learning-triggered synaptic expansion and synaptic convergence. Inspired by the biological active forgetting, we propose to actively forget the old knowledge that limits the learning of new tasks to benefit continual learning. Under the framework of Bayesian continual learning, we develop a novel approach named Active Forgetting with synaptic Expansion-Convergence (AFEC). Our method dynamically expands parameters to learn each new task and then selectively combines them, which is formally consistent with the underlying mechanism of biological active forgetting. We extensively evaluate AFEC on a variety of continual learning benchmarks, including CIFAR-10 regression tasks, visual classification tasks and Atari reinforcement tasks, where AFEC effectively improves the learning of new tasks and achieves the state-of-the-art performance in a plug-and-play way.
GROWING STUDENTS’ WRITING FLUENCY USING FREE WRITING ACTIVITY

For most EFL students especially novice writers, writing English was hard to do. They were often stuck with a lot of obstacles. The obstacles were not only about students’ knowledge in linguistic elements, such as grammar, structure, vocabulary, spelling, punctuation, capitalization but also about students’ attitudes toward writing. Writing was not easy and not fun for students. To solve this hardship, students required a particular activity which helped them improve their writing ability and change their attitudes. Free writing was the activity chosen to overcome this case. It was done in series over five times with time limit, and after that students did reflection on their five results of free writing. The purpose of this study was to examine the beneficial effects of using free writing activity toward students’ writing fluency and writing ability. The method used in this study was descriptive qualitative. This method described how effective free writing activity on students’ writing. Data were taken from students’ free writing samples as primary data, and the reflection as the secondary data. There were 75 free writing samples and 60 reflection samples. This study revealed that free writing made students get used to writing, improve linguistic elements, and be confident and comfortable in writing. Finally free writing gave beneficial effects for learners, it could make learners write more, write easier and write better.
Sound: From added-value to cohesion

Material Media-Making in the Digital Age; A journey through a variety of tools and technologies from vlogging to drone cinematography to considerations of time, editing and sound design, Material Media-Making in the Digital Age offers professionals, scholars and students alike the chance to re-think how they engage with new media technology, about media and its place in the world. 17 b/w illus.
Artificial Neural Network Architectures and Orthogonal Arrays in Estimation of Software Projects Efforts Estimation : PLENARY TALK

In the software engineering, estimation of the effort, time and cost required for the development of software projects is an important issue. It is a very difficult task for project managers to predict the cost and effort needed in the premature stages of planning. Software estimation ahead of development can reduce the risk and increase the success rate of the project. Many traditional and machine learning methods are used for software effort estimation by researchers, but always it has been a challenge to predict the effort accurately. In our study, different Artificial Neural Network (ANN) used for effort estimation is discussed. It is observed that the prediction of software effort by using ANN is more precise and better compared to traditional methods such as Function point, Use-case methods and COCOMO etc. Models based on neural networks are competitive in nature as compared to statistical and traditional regression methods. This talk explains the overview of various ANN architectures, and deep learning networks used by the researchers for software effort estimation (SEE). In addition, we have set out own criteria, which has been used to compare all the different selected methods. We have also given a score for each evaluation criteria, so that we can compare the different methods numerically for cost estimation. Our observations have shown that it is best to use a number of different estimating techniques or cost models, and then compare the results before determining the reasons for any of the large variations. None of the methods are necessarily better or worse than the others. We found, in fact, that their strengths and weaknesses often complement each other. Therefore, the main conclusion is that there is no one single technique that is best for every situation, and the results of a number of different approaches need to be carefully considered to discover what is the most likely to produce estimates that are realistic.
Gender Classification Based on Speech Analysis using DNN

Language is one of the significant methods for correspondence; Speech is its fundamental medium by which two individuals can communicate. In this current period, technology based on speech are broadly utilized as it has limitless uses. Speech Recognition (SR), as the man-machine interface assumes a fundamental part in the field of AI and ML where precision is a significant challenge. In this proposed system, a model has been processed where to separate two voices by using DNN techniques. Conversation is made on various methods and approaches of the discourse acknowledgment measure utilizing the DNN. DNN can effectively extract low-dimensional features. Hence the accuracy of speech separation can be improved by the designed system. The main motive is to bring to light the progress made in the field of speech recognition.
Graph Neural Network Based Scheduling: Improved Throughput Under a Generalized Interference Model

In this work, we propose a Graph Convolutional Neural Networks (GCN) based scheduling algorithm for adhoc networks. In particular, we consider a generalized interference model called the k-tolerant conflict graph model and design an efficient approximation for the well-known Max-Weight scheduling algorithm. A notable feature of this work is that the proposed method do not require labelled data set (NP-hard to compute) for training the neural network. Instead, we design a loss function that utilises the existing greedy approaches and trains a GCN that improves the performance of greedy approaches. Our extensive numerical experiments illustrate that using our GCN approach, we can significantly (4–20%) improve the performance of the conventional greedy approach.
A Proof of Beal's Conjecture Using Wave Model

Beal's conjecture can play an important role to connect mathematics and physics, and it may help us to achieve a better view of the concept of world. In this this paper, an innovative method as the proof of the Beal's conjecture is proposed. Introduced approach is based on wave model and provides a novel point of view in the topic of prime numbers. Employed wave model suggests an analogy between prime numbers and frequency, and may provide a valuable approach in mathematics.
A Study on the Entrepreneurship, Innovativeness and Overseas Expansion Capacity in the 4th Industrial Revolution

Purpose This study aims to identify the relationship between competences suggested through case studies and suggest policy implications based on this. In particular, in order to understand the structure between competencies to provide a conceptual framework, it is important to understand conceptual characteristics through qualitative analysis and in-depth analysis through case studies rather than quantitative analysis. Design/Methodology/Approach It is necessary to present a new framework to respond to rapid changes such as the advent of the 4th industrial revolution and the current Corona 19 situation. A convergence research methodology to respond to changes such as the advent of a new industrial environment and non-face-to-face environment along with the advent of traditional characteristics and the latest technology of the existing industry is very necessary, but various studies on it are lacking. Findings This study is meaningful in providing a conceptual framework for future competency analysis, and it is expected that various follow-up studies will be possible through this. The conceptual framework of future competency derived from this study will provide the necessary concepts when analyzing the capabilities that entrepreneurs should have as a growth engine in Korea, and through this, policy proposals and industrial investment focused on more important capabilities will be made. Research Implications In order to see the capabilities required in the era of the 4th industrial revolution from the perspective of entrepreneurs, the innovation of future capabilities, the required capabilities of entrepreneurship, and the capabilities required for overseas expansion were analyzed in connection with each other.