Reviewerly/exHarmony huggingface dataset:
Neural Learning of One-of-Many Solutions for Combinatorial Problems in Structured Output Spaces

Recent research has proposed neural architectures for solving combinatorial problems in structured output spaces. In many such problems, there may exist multiple solutions for a given input, e.g. a partially filled Sudoku puzzle may have many completions satisfying all constraints. Further, we are often interested in finding any one of the possible solutions, without any preference between them. Existing approaches completely ignore this solution multiplicity. In this paper, we argue that being oblivious to the presence of multiple solutions can severely hamper their training ability. Our contribution is two-fold. First, we formally define the task of learning one-of-many solutions for combinatorial problems in structured output spaces, which is applicable for solving several problems of interest such as N-Queens, and Sudoku. Second, we present a generic learning framework that adapts an existing prediction network for a combinatorial problem to handle solution multiplicity. Our framework uses a selection module, whose goal is to dynamically determine, for every input, the solution that is most effective for training the network parameters in any given learning iteration. We propose an RL based approach to jointly train the selection module with the prediction network. Experiments on three different domains, and using two different prediction networks, demonstrate that our framework significantly improves the accuracy in our setting, obtaining up to 21 pt gain over the baselines.
APLICAÇÃO DE MACHINE LEARNING NA IDENTIFICAÇÃO DE E-MAILS COMO SPAM

The e-mail service is one of the main tools used today and is an example that technology facilitates the exchange of information. On the other hand, one of the biggest obstacles faced by e-mail services is spam, the name given to the unsolicited message received by a user. The machine learning application has been gaining prominence in recent years as an alternative for efficient identification of spam. In this area, different algorithms can be evaluated to identify which one has the best performance. The aim of the study is to identify the ability of machine learning algorithms to correctly classify e-mails and also to identify which algorithm obtained the greatest accuracy. The database used was taken from the Kaggle platform and the data were processed bythe Orange software with four algorithms: Random Forest (RF), K-Nearest Neighbors (KNN), Support Vector Machine (SVM) and Naive Bayes (NB). The division of data in training and testing considers 80% of the data for training and 20% for testing. The results show that Random Forest was the best performing algorithm with 99% accuracy.
CommonRoad Drivability Checker: Simplifying the Development and Validation of Motion Planning Algorithms.

Collision avoidance, kinematic feasibility, and road-compliance must be validated to ensure the drivability of planned motions for autonomous vehicles. Although these tasks are highly repetitive, computationally efficient toolboxes are still unavailable. The CommonRoad Drivability Checker—an open-source toolbox—unifies these mentioned checks. It is compatible with the CommonRoad benchmark suite, which additionally facilitates the development of motion planners. Our toolbox drastically reduces the effort of developing and validating motion planning algorithms. Numerical experiments show that our toolbox is real-time capable and can be used in real test vehicles.
An examination of automated testing and Xray as a test management tool

Automated testing is a fast-growing requirement for many IT companies. The idea of testing is to create a better product for both the company and the customer. The goal of this study is to examine ...
Cheating and Fraud Detection using Fuzzy Principles and Artificial Intelligence Techniques

Credit card fraud is a wide-ranging term for theft and fraud committed using a credit card as a fraudulent source of funds in a given transaction. Usually, the fraudulent transactions are conducted by stealing the credit card. When the loss of the card is not noticed by the cardholder, a huge loss can be faced by the credit card company. A very little amount of information is required by the attacker for conducting any fraudulent transaction in online transactions. For buying products and services online, the Internet or telephone devices are used. In this paper, proposed a fraud detection method which utilizes the behavioral patterns of the cardholders analyzing the historical transactions of the cardholder and the feature selection is done by the Fuzzy Particle Swarm Optimization method, enhancing the credit card fraud. After extracting the best and effective features the ensemble classification is applied.
A Pufferfish Privacy Mechanism for the Trajectory Clustering Task

Monitoring people’s trajectories can help with many data mining tasks. For example, clustering analysis of users’ trajectories can infer where people work and where they live. However, as far as users are concerned, no one wants their privacy to be compromised so that third parties can benefit from it. The commonly used method of privacy protection today is differential privacy, but differential privacy does not have significant advantages when dealing with correlated data. Pufferfish privacy can be used to address the privacy protection of correlated data for this reason. Our work aims to protect the locations that are extracted from trajectories using clustering methods. To achieve this goal, we first use the DBSCAN algorithm to cluster the trajectories of users, mark the locations where users have stayed, and preserve the chronological order. Privacy requirements for this issue are then specified in the Pufferfish framework. The data is then processed using a mechanism that implements a privacy framework. Finally, the utility is evaluated through experiments.
Context-Agnostic Learning Using Synthetic Data

We propose a novel setting for learning, where the input domain is the image of a map defined on the product of two sets, one of which completely determines the labels. Given the ability to sample from each set independently, we present an algorithm that learns a classifier over the input domain more efficiently than sampling from the input domain directly. We apply this setting to visual classification tasks, where our approach enables us to train classifiers on datasets that consist entirely of a single example of each class. On several standard benchmarks for real-world image classification, our approach achieves performance competitive with state-of-the-art results from the few-shot learning and domain transfer literature, while using significantly less data.
Layer-wise Adversarial Defense: An ODE Perspective

Deep neural networks are observed to be fragile against adversarial attacks, which have dramatically limited their practical applicability. On improving model robustness, the adversarial training techniques have proven effective and gained increasing attention from research communities. Existing adversarial training approaches mainly focus on perturbations to inputs, while the effect of the perturbations in hidden layers remains underexplored. In this work, we propose layer-wise adversarial defense which improves adversarial training by a noticeable margin. The basic idea of our method is to strengthen all of the hidden layers with perturbations that are proportional to the back-propagated gradients. In order to study the layer-wise neural dynamics, we formulate our approach from the perspective of ordinary differential equations (ODEs) and build up its extended relationship with conventional adversarial training methods, which tightens the relationship between neural networks and ODEs. In the implementation, we propose two different training algorithms by discretizing the ODE model with the Lie-Trotter and the Strang-Marchuk splitting schemes from the operator-splitting theory. Experiments on CIFAR-10 and CIFAR-100 benchmarks show that our methods consistently improve adversarial model robustness on top of widely-used strong adversarial training techniques.
Underwater Image Enhancement Method via the Fusion of Retinex and Pulse Coupled Neural Network

To tackle the problems of color deviation, blur and low contrast in underwater images, this paper proposes an underwater image enhancement method based on the fusion of Retinex and pulse coupled neural network. First, this paper uses the multi-scale Retinex algorithm to dispose the original image and obtain the image with high saturation. Second, this paper uses the adaptive color correction algorithm to process the original image. And then this paper uses the pulse coupled neural network to further process the image, so as to obtain the image with clear texture and details. Finally, this paper uses the multi-scale fusion algorithm to obtain the final underwater clear image. The experimental results show that our method can improve the sharpness and contrast of images, and solve the problems of underwater images better than other algorithms.
La pedagogía crítica y la importancia de la lecto-escritura en la emancipación humana para el ejercicio de una ciudadanía responsable

This article points out the importance of Paulo Freire's critical pedagogy for the exercise of a teaching-learning process that involves reading and writing as a critical tool of meaning and appropriation of the student's vital context for social transformation and the exercise of responsible, participatory, informed and analytical citizenship that results in better development opportunities for all the country's habitants.
AKTIVITAS BELAJAR ANAK SELAMA MASA BELAJAR DARI RUMAH DI DESA MEKO

The availability of internet access by junior high school level children in Meko Village has been used to access youTube, Facebook, WhatsApp, Massenger, Twitter, Line and only a few of them use it to add references in completing assignments given by the teacher. Ownership of gadgets and Personal Computers (PC) which are limited to children from economically established families and existing internet networks have not been used to conduct online learning with accessing websites prepared by the Ministry of Education and Culture. Learning activity is done offline by giving assignments that can provide meaningful learning experiences through learning activities undertaken by children while Learning From Home. Activities undertaken by junior high school-level children during the Learning From Home in Meko Village are reading religious books, conduct discussions online, make a summary and searching for online material.
Text, Visual and Multimedia Sentiment-Analysis, and Sentiment-Prediction

Sentiment analysis (SA) is an automated process that uses AI methods such as classifier, collaborative filtering, machine learning, deep learning, and CNNs. SAs can be categorized into three categories: Text, Visual, and Multimedia Sentiment analysis. The SA generates, quantifies, or reveals the opinion, affective state(s), and contextual polarity of the information. The Use-Cases of Text SA are monitoring the social media, news, stock-market, healthcare, customer service, brands, and products. SAs also enable the prediction of sentiments. Earlier research in the field have mostly focused on Text Sentiment-analysis (TSA); recent researches in Affective States computing, Visual Sentiment analysis (VSA) and Multimedia Sentiment analysis (MMSA) have given new dimensions to expand the usage of SAs. Studies show that systematic SAs result in higher prediction accuracies for VSA and MMSA using fine-tuned CNNs (Convolution Neural Networks) compared to the TSA alone.
THE PREFERENCES OF SELECTING THE EFFECTIVE AND EFFICIENT LEARNING MEDIA IN STUDENTS

Research aims to describe the preferences for selecting learning media to meet the information needs of students in completing academic assignments. The research object is based on the accuracy of analysis based on detailed comprehensive studies of the main focus of content, the selection of learning media to complete student academic tasks, quickly and effectively, which have now adapted to the era of the industrial revolution 4.0, towards digitalization in information seeking. The research has the scope of in-depth analysis based on and focused on main studies, namely the preference for selecting learning media to meet the information needs of students in completing academic tasks. Analyzing research, requires realistic critical thinking, the application of a conceptual occurs in the historical period, in the past or when it is happening, so that a historical approach is appropriate, the main characteristic of which is to dominate realistic critical investigation of the development of a thought or term. Primary information sources are used as primary data. Historical approach data collection, empowering research libraries through reading various literatures based on subjected reading materials, preferences, selection of learning media, student information needs and completion of academic tasks. The analysis is carried out by qualitative descriptive analysis, based on sources of analysis originating from a variety of theoretical, research and non-research reading materials. Research predominates the description and evaluation of factual facts in order to obtain problem-solving solutions that are embodied in social phenomena, the selection of learning media for the completion of student academic tasks. Validity is realized through in-depth analysis through content analysis, which is useful for analyzing the meaning of the most important concepts in research. Observation of student activities in fulfilling information needs is used as a support for theory construction. The result of the research is that most students in Indonesia prefer to use digital media rather than print media to fulfill their information needs in completing their academic assignments.
Financial Statement Fraud Prevention Strategy: Fuzzy Analytical Hierarchy Process Method

The purpose of this study is to examine the factors that cause financial statement fraud. This study uses the Fuzzy Analytical Hierarchy Process (AHP) and determines the appropriate model strategy for preventing financial statement fraud at State-owned enterprises (SOEs) in Indonesia. The mixed method is applied in this work as this research also uses the descriptive analysis method to analyze the phenomena that occur. The informants in this study were members of the Indonesian Supreme Audit Institution (BPK) and State Development Audit Agency (BPKP), academics, and financial managers of enterprises SOEs in Indonesia. This study formulated the following fraud prevention strategies in the context of SOEs fraud: (1) Practicing honest culture and high ethics, (2) improving supervision and control systems, (3) formulating anti-fraud values, (4) implementing a firm-wide reward and punishment system, (5) introducing anti-fraud socialization for employees, and (6) establishing change agents. Keywords: financial statement fraud, fuzzy analytical hierarchy, fraud.
LibGuides: Engineering Profession: Common First Year (FEBE1000): Engineering Subject Specific LibGuides

This LibGuide provides relevant information for all FEBE1000A Introduction to the Engineering Profession students.
Big Data against Corona: Mass Surveillance or Privacy by Design? Keynote

Summary form only given, as follows. The complete presentation was not made available for publication as part of the conference proceedings. The corona pandemic is the first major pandemic in times of big data, AI and smart devices. Some nations have demonstrated that these technologies can be deployed successfully at a large scale to support a trace/quarantine/test/isolate strategy in order to contain a pandemic. However, serious concerns have been raised on the privacy implications of some solutions, which makes them incompatible with privacy and human rights that are protected by EU law. This talk surveys these attempts and the related privacy concerns. It will also present the contact tracing solution developed by the DP-3T (Distributed Privacy-Preserving Proximity Tracing) consortium that has been rolled out in more than 20 countries and states, with support of Google and Apple. The talk will discuss the lessons learned from this large-scale deployment in which the principles of privacy-by-design and data minimization have played a central role.
FACING COVID-19 IN INDONESIA: VARIATIONS OF LEARNING MEDIA AND ONLINE TEACHING LEARNING THROUGH YOU TUBE AND ZOOM APPLICATION

Purpose of the study: This research discussed about variation of learning media and online teaching through You Tube and Zoom application during COVID-19. This research is aimed to describe the community service or community service program of Muhammadiyah University of Enrekang. It is a program of the aim for providing knowledge to the academic community about the procedures for learning media.Methodology: This research focused on the models of educational media for the technical guidance activity, participants were able to create learning media such as presentations, videos, interactive, and Android. The data were analyzed using descriptive quantitative method. The purpose of data collection is identified of teaching evaluation by using video data, questionnaire data, documentation data and collection instruments.Main Findings: is the results classified of the evaluation it was found that the implementation of this activity was successful with a high level of attendance of the participants, a level of likeness that was evenly distributed in all material presented by the speakers, and a very high level of satisfaction with the service. Applications of this study: is the researcher analyzed the news from Google form. The survey results show that all lecturers carry out online learning during the COVID-19 prevention period through You Tube, Zoom Application. Most participants organize learning and discussion through media learning. Submission of material is mostly done by sharing files through You Tube, Zoom Application and other media learning. Novelty/Originality of this study: Most of the functions used the variation of media and platforms used in online teaching learning for prospective teachers during the social distancing period. The stage online media are used such as zoom, youtube, sli.do, google sheet, Google form and others.
The Study of the Online Teaching Model of "SPOC +Tencent Classroom" Based on Ke Tang Pai under the Epidemic Situation

Under the influence of the COVID-19, universities began to promote online education in China. However, there were many problems in online teaching. This paper aimed at the problems existing in the current online teaching, analyzed the advantages and disadvantages of small private online course (SPOC) platform, Tencent classroom and Ke Tang Pai, constructed the online teaching model of "SPOC +Tencent Classroom" Based on Ke Tang Pai. This teaching model not only carried out online teaching, but also effectively improved students' interest and participation in learning, and improved the teaching effect through students' comprehensive performance and feedback. This study provided reference for the development of online teaching and the improvement of teachers' online teaching ability.
Use of Cartesius Method on Google Maps Api to Recommend Search for Batik Crafts and Andid-Based Crafts (Case Study of Bangkalan District)

ASmart City increasingly rife discussed in order to be a solution in solving problems arising from the dynamics of an increasingly advanced and modern city. The purpose of applying the concept of smart city to increase the sense of security and comfort for its citizens, making the city more effective and efficient. Madura is an island located north of Java island. The island is dubbed Salt island “Pulau Garam” is quite well known in the field of bull race and batik handicrafts, especially in Bangkalan regency. Bangkalan regency is a developing city to realize the concept of smart city. One way that can be done is to take advantage of the market needs of batik lovers by providing easy access to the location. Utilizing google maps location search technology with Cartesian method on smartphone electronic devices, consumers will be easier in terms of information search and location of batik craftsmen scattered throughout the district of Bangkalan.
PEMBELAJARAN KETERAMPILAN BERBICARA TEMA “MEMBUAT NASI GORENG” DENGAN MENGGUNAKAN METODE COOKING CLASS PADA MAHASISWA PROGRAM BIPA DARMASISWA UNISSULA TAHUN AKADEMIK 2019/2020

Speaking Skill learning is one of cumpolsory courses for students in Darmasiswa Program. This study aimed to describe Speaking Skill learning through cooking class method for students of BIPA level B1, Darmasiswa Program, Sultan Agung Islamic University, 2019/2020. Method used in this study was qualitative descriptive method. This study obtained sources from informants, documents, and notes in observation result. This study conducted observation, interview and document analysis in order to collect data. The data validity was tested by method triangulation, data source triangulation, and informant reviews. Data analysis technique used in this study was interactive analysis technique. Based on the result of assessment on speaking skill, it was obtained that respondent 1 got score of 89 in Perfect category (A), respomdent 2 got 84 in Very Satisfying category (AB), and respondent 3 got 79 in Very Satisfying category (AB). Furthermore, it could be concluded that the usage of cooking class method was helpful and able to facilitate the students in Speaking Skill learning on theme of 'Cooking Anticovid-19 Fried Rice' using Indonesian Language well and fluently. In addition, the students got more interest and motivation to participate in Speaking Skill class.
THE USE OF MIND MAPPING TEHNIQUE TO IMPROVE THE STUDENTS WRITING SKILL IN DESCRIPTIVE TEXT

This study is entitled the use of Mind mapping to improve student’s writing  skill. This research is an experimental research and uses quantitative methods. The authors conducted this study in class X MIPA 1 as a control class and class X MIPA 2 as an experimental class. To obtain data, the researcher conducted experimental teaching for five meetings. The instrument used was 1 1 questions. pre-test and post-test as a research instrument. The results showed that there were a significant difference between students who were taught by using Mind mapping and students who were taught by another method. This was found that by comparing pre-test and post-test. Based on the data obtained, the authors concluded that their writing skill improved after being taught using Mind mapping, then choosing Mind mapping can greatly help students to improve their writing skills. The calculation of the data showed that the experimental class had the total score 4050 , meanwhile in the control class had the total score 2770 . The mean score of experimental class was 78,4 and the mean score of control class was 38,0 . It showed the mean of difference score of experimental class was higher than control class ( 78,4 > 38,0 ). While, the result of was 78,4 . Because of that, higher than ( 78,4 > 1,70 ). It means that was accepted Keywords: . Writing, mind mapping, descriptive text
Bubbles and the Value of Innovation, Working Paper 20-08

Episodes of booming innovation coincide with intense speculation in financial markets leading to bubbles—increases in market valuations and firm creation followed by a crash. We provide a framework reproducing these facts that makes a rich set of predictions on how speculation changes both the private and social values of innovation. We confirm the theory in the universe of U.S. patents issued from 1926 through 2010. Measures based on financial market information indicate that speculation increases the private value of innovation and reduces negative spillovers to competing firms. No commensurate change occurs in measures grounded in real outcomes.