Reviewerly/exHarmony huggingface dataset:
A Comparison of Traditional Machine Learning Approaches for Supervised Feedback Classification in Bahasa Indonesia

The advancement of machine learning and natural language processing techniques hold essential opportunities to improve the existing software engineering activities, including the requirements engineering activity. Instead of manually reading all submitted user feedback to understand the evolving requirements of their product, developers could use the help of an automatic text classification program to reduce the required effort. Many supervised machine learning approaches have already been used in many fields of text classification and show promising results in terms of performance. This paper aims to implement NLP techniques for the basic text preprocessing, which then are followed by traditional (non-deep learning) machine learning classification algorithms, which are the Logistics Regression, Decision Tree, Multinomial Naïve Bayes, K-Nearest Neighbors, Linear SVC, and Random Forest classifier. Finally, the performance of each algorithm to classify the feedback in our dataset into several categories is evaluated using three F1 Score metrics, the macro-, micro-, and weighted-average F1 Score. Results show that generally, Logistics Regression is the most suitable classifier in most cases, followed by Linear SVC. However, the performance gap is not large, and with different configurations and requirements, other classifiers could perform equally or even better.
Curriculum Learning for Wide Multimedia-Based Transformer with Graph Target Detection

The social media prediction task is aiming at predicting content popularity which includes social multimedia data such as photos, videos, and news. The task can not only help make better decisions for recommendation, but also reveals the public attention from evolutionary social systems. In this paper, we propose a novel approach named curriculum learning for wide multimedia-based transformer with graph target detection(CL-WMTG). The curriculum learning is designed for the transformer to improve the efficiency of model convergence. The mechanism of wide multimedia-based transformer is to make the model capable of learning cross information from text, pictures and other features(e.g. categories, location). Moreover, the graph target detection part can extract different features in the picture by pretrained model and reconstruct the features with a homogeneous graph network. We achieved third place in the SMP Challenge 2020.
Cooperative Methods and Development of Social Competence in Training of Technical Teachers

The requirements of job market nowdays put emphasys on the importance of emotional intelligence and social behaviour. This fact determines the educational requirements of future teachers. One offthe efficient ways for this is to use imp-roving and student focused methods. &#x0D; In this particular study we introduce a small part of the results of an experi-mental program that helped to acknowledge some of the cooperative methods and the improvement of soical skills among the participants. &#x0D; From 2015, by the ivolvement of more and more new students groups the students experienced using the methods, the effects of them and the individual use fo them during the pedagogy program. According to the results and feed-backs we can that we got the following result like the imporvement of social skills,the usage of these skills in the future, the need of getting to know new methods and the deeper connection towards students. &#x0D; Prior methodological knowledge and the measurement of participants' social skills are the subject of another study. Here, attitudes and opinions about joint work and changes, as well as some aspects of the development of social compe-tence, are summarized.
Method for assessing the information content of factors forming the cognitive independence of students

Abstract The article describes the problem of awakening the cognitive activity of students, arising due to revolutionary transformations in teaching technologies. It shows the need to study the factors that affect cognitive activity and assesses the information content of those factors. Based on the method of pair comparisons, a technology for ranking the factors affecting cognitive activity has been developed. A mathematical model of the formation and processing of expert assessment results and an example of calculations are given, and the feasibility of including the subsystem for evaluating factors affecting cognitive activity in the computer control system of the university is shown. The structure of an adaptive e-learning system based on the technology of taking into account the factors that form the cognitive independence of students is described Experiments carried out in 3 universities of Ukraine have shown that the use of the developed mathematical model and information technology allows to increase significantly academic performance and to decrease the frequency of refusals from independent work of students using the e-learning environment.
OER4Schools: Outcomes of a Sustained Professional Development Intervention in Sub-Saharan Africa

Sustaining educational initiatives beyond short-term pilot projects is highly challenging in low-income countries. We describe the outcomes and implications of our iterative Design-Based Implementation Research conducted in Zambia. This focused on a unique, school-based, peer-facilitated professional learning programme for primary teachers: OER4Schools integrates interactive pedagogy, open digital educational resources and mobile learning. Teacher interviews carried out 18 months after a year-long intervention showed that the programme became self-sustaining; earlier participants reported further development of their interactive teaching strategies and awareness of pupil progress; recent joiners developed similarly. Roles of teachers and pupils changed and a new classroom culture emerged. The study identifies the key mechanisms involved in sustainability, including culturally sensitive and participatory development and implementation, semi-structured multimedia materials, and supportive organisational structures for sustained professional learning. Our findings are hence framed by sociocultural influences as well as the wider policy context.
Internal Learning for Image Super-Resolution by Adaptive Feature Transform

Recent years have witnessed the great success of image super-resolution based on deep learning. However, it is hard to adapt a well-trained deep model for a specific image for further improvement. Since the internal repetition of patterns is widely observed in visual entities, internal self-similarity is expected to help improve image super-resolution. In this paper, we focus on exploiting a complementary relation between external and internal example-based super-resolution methods. Specifically, we first develop a basic network learning external prior from large scale training data and then learn the internal prior from the given low-resolution image for task adaptation. By simply embedding a few additional layers into a pre-trained deep neural network, the image-adaptive super-resolution method exploits the internal prior for a specific image, and the external prior from a well-trained super-resolution model. We achieve 0.18 dB PSNR improvements over the basic network’s results on standard datasets. Extensive experiments under image super-resolution tasks demonstrate that the proposed method is flexible and can be integrated with lightweight networks. The proposed method boosts the performance for images with repetitive structures, and it improves the accuracy of the reconstructed image of the lightweight model.
Exploring governance in a decentralized energy trading eco-system

Increasingly, large tech firms dominate eco-systems. From a societal perspective this is not always beneficial since these companies behave as value extractors; they charge an unreasonable high fee for their services and they can do so because they are monopolists. A possible solution to this substantial power concentration can be decentralized ecosystems, e.g., enabled by blockchain technology, in which decision power is distributed fairly. However, this comes also with the requirement that such eco-systems need a decentralized governance model. This paper explores if such a governance model can be represented by conceptual models, in particular, e3value. We answer this question by designing a decentralized eco-system in the field of electricity supply, which enables peer to peer energy trading, and checking if important governance decisions, motivated by a systematic literature review, can be represented.
Keyphrase Extraction from Document Using RAKE and TextRank Algorithms﻿

Traditional approaches to extract useful Keyphrase from a sentence rely heavily on human effort. In this paper, to overcome this challenge, Automatic Keyphrase Extraction algorithm has been used to extract a Keyphrase efficiently that reduces the scope for human errors and saves time. The Machine Learning algorithms detect the Keyphrase from a sentence that the user feeds as an input and sets a reminder using the Keyphrase. RAKE and Textrank algorithms help to extract Keyphrase or important terms of a given text document. RAKE and TextRank techniques applied to find and analyze the best possible way of extracting the Keyphrase efficiently. With slight modifications to the code, the algorithms can be implemented to serve different application domain such as message or threat decoding in military purposes and can be extended to use in speech-to-text translation and sentimental analysis of the data.
Contrast Motif Discovery in Minecraft

Understanding event sequences is an important aspect of game analytics, since it is relevant to many player modeling questions. This paper introduces a method for analyzing event sequences by detecting contrasting motifs; the aim is to discover subsequences that are significantly more similar to one set of sequences vs. other sets. Compared to existing methods, our technique is scalable and capable of handling long event sequences. We applied our proposed sequence mining approach to analyze player behavior in Minecraft, a multiplayer online game that supports many forms of player collaboration. As a sandbox game, it provides players with a large amount of flexibility in deciding how to complete tasks; this lack of goal-orientation makes the problem of analyzing Minecraft event sequences more challenging than event sequences from more structured games. Using our approach, we were able to discover contrast motifs for many player actions, despite variability in how different players accomplished the same tasks. Furthermore, we explored how the level of player collaboration affects the contrast motifs. Although this paper focuses on applications within Minecraft, our tool, which we have made publicly available along with our dataset, can be used on any set of game event sequences.
Scheduling Methods to Reduce Response Latency of Function as a Service

Function as a Service (FaaS) permits cloud customers to deploy to cloud individual functions, in contrast to complete virtual machines or Linux containers. All major cloud providers offer FaaS products (Amazon Lambda, Google Cloud Functions, Azure Serverless); there are also popular open-source implementations (Apache OpenWhisk) with commercial offerings (Adobe I/O Runtime, IBM Cloud Functions). A new feature of FaaS is function composition: a function may (sequentially) call another function, which, in turn, may call yet another function - forming a chain of invocations. From the perspective of the infrastructure, a composed FaaS is less opaque than a virtual machine or a container. We show that this additional information enables the infrastructure to reduce the response latency. In particular, knowing the sequence of future invocations, the infrastructure can schedule these invocations along with environment preparation. We model resource management in FaaS as a scheduling problem combining (1) sequencing of invocations, (2) deploying execution environments on machines, and (3) allocating invocations to deployed environments. For each aspect, we propose heuristics. We explore their performance by simulation on a range of synthetic workloads. Our results show that if the setup times are long compared to invocation times, algorithms that use information about the composition of functions consistently outperform greedy, myopic algorithms, leading to significant decrease in response latency.
A Student’s E-Learning Self-Control Method Based On A Topological Knowledge Tree

Improving the quality of self-educational activity of trainees using e-learning is currently extremely topical. The performance of independent work of the student is largely dependent on consistency, continuity, and perfection of educational-cognitive activity. High motivation and self-organization of this activity largely depends on self-government and self-control of the learner. In this context, the aim of this work was to develop and justify a method of student self-control by creating an "upside-down" information resources, which visualizes the process of learning and takes into account the characteristics of Z generation. The basis of the proposed method is a topological question- task knowledge tree of the subject area with regulatory tasks or tests. For each question there is a response in the form of concept maps with the necessary educational review, and for the task – an explanation of its decision. With the positive outcome the corresponding elements of the knowledge tree are painted with a certain colour that visually defines the amount and the dynamics of the subject area material studied. The proposed method of the visualized self-learning process will significantly improve the effectiveness of self-educational activity of the student, question- task format of the inverted electronic resources will provide motivation for learning effectiveness.
Superpixel-Based Shallow Convolutional Neural Network (SSCNN) for Scanned Topographic Map Segmentation

Motivated by applications in topographic map information extraction, our goal was to discover a practical method for scanned topographic map (STM) segmentation. We present an advanced guided watershed transform (AGWT) to generate superpixels on STM. AGWT utilizes the information from both linear and area elements to modify detected boundary maps and sequentially achieve superpixels based on the watershed transform. With achieving an average of 0.06 on under-segmentation error, 0.96 on boundary recall, and 0.95 on boundary precision, it has been proven to have strong ability in boundary adherence, with fewer over-segmentation issues. Based on AGWT, a benchmark for STM segmentation based on superpixels and a shallow convolutional neural network (SCNN), termed SSCNN, is proposed. There are several notable ideas behind the proposed approach. Superpixels are employed to overcome the false color and color aliasing problems that exist in STMs. The unification method of random selection facilitates sufficient training data with little manual labeling while keeping the potential color information of each geographic element. Moreover, with the small number of parameters, SCNN can accurately and efficiently classify those unified pixel sequences. The experiments show that SSCNN achieves an overall F1 score of 0.73 on our STM testing dataset. They also show the quality of the segmentation results and the short run time of this approach, which makes it applicable to full-size maps.
Generating Game Levels for Multiple Distinct Games with a Common Latent Space

Generative adversarial networks (GANs) are showing significant promise for procedural content generation (PCG) of game levels. GAN models generate game levels by mapping a low dimensional latent space to game levels in the game space. An intriguing challenge in GAN-based PCG is enabling GANs to produce game levels for multiple distinct games with similar gameplay characteristics using a common underlying low-dimensional representation. In this paper, we present a method for training a novel GAN-based PCG architecture that generates levels in multiple distinct games, starting from a common gameplay action sequence. We evaluate the solvability of the generated games using an automated playing agent and show how the generated game levels are separate representations of the same gameplay by quantifying the similarity between the solution action sequences for the game levels. By probing the common latent space, we show how our approach provides control over the levels generated in distinct games for the presence of desired gameplay patterns in the generated game levels. Results also demonstrate that the GAN-based PCG approach creates novel game levels in multiple distinct games, as indicated by the distance between the action sequences required to solve the game levels.
The impact of knowledge management practice on the hospital performance in Abu Dhabi

This study proposes a conceptual model that simultaneously involves five factors of Knowledge Management (KM) and six factors of Hospital Performance (HP), in their respective constructs. The significance of this model is that it has logically linked the constructs of both KM and HP. Exploratory factor analysis, reliability analysis procedure, and construct validity procedures have been used to verify and validate the underlying dimensions of KM and HP. The relationship has been analyzed through Structural Equation Modeling approaches, in the last stage of the data analysis, the results found that only three variables of KM construct have a significant directional impact on the only three variables of HP construct effects. Further, the in-depth data analysis has revealed that the three variables of KM construct have a statistically significant influence on the three variables of HP construct. The statistically rigorous and strong results obtained in this study suggest that this conceptual model can be used as a management tool to assess performance. The significant impact of KM on HP has been argued to be positive. The value of the paper is that it clarifies the influence of KM on HP in Abu-Dhabi hospitals which can be used to improve hospital performance and efficiency.
AI-Based Elderly Assistance Systems.

The demographic change is no longer a prognosis, but a reality seen in everyday life situations and requires mechanisms to make the public and private space elderly-adequate. These required mechanisms need to consider the varying aging process for each individual as well as adapt to the dynamic daily life of individuals characterized by spatial, temporal and activity variance. Developing assistance systems that are user-adaptive within dynamic environments is a challenging task. AI-based cyber-physical assistance systems enable such adaptive, flexible and individual assistance by processing acquired data from the physical environment using cyber resources and delivering intelligent assistance as well as interfaces to further medical services. This contribution discusses a flexible, reusable, and user-specific concept for AI-based assistance systems. Relying on distributed and heterogeneous data, the user's context is continuously modeled and reasoned over to infer actionable knowledge within a middleware between the data layer and the application layer. To demonstrate the applicability of the concept, the use case of intelligently supporting patients' medication adherence is shown.
A Comparison Between Naïve Bayes and Random Forest to Predict Breast Cancer

Accurate diagnosis of breast cancer is very beneficial as breast cancer is the second-leading cause of cancer death in women after lung cancer in the US. This study compares two machine learning approaches to diagnose breast cancer using a publicly available dataset, which comprises of features computed from a digitized image of a fine needle aspirate (FNA). We employ two different machine learning techniques, namely Naïve Bayes and Random Forest to measure the accuracy of the diagnosis. Using 569 patient's information and 31 features, the above three machine learning classifiers are implemented. According to the findings, the Random Forest classifier performed better than the Naïve Bayes method by reaching a 97.82% of accuracy. Furthermore, classification accuracy can be improved with the appropriate selection of the feature selection technique. Furthermore, this section explains the feature selection technique used in the study. The analysis procedure is discussed, and the dataset and the performance indicators are described.
Using Domain Compilation to Add Belief to Narrative Planners

Using domain compilation, we present a narrative planning system that is capable of creating narrative plans that use both character intention and character beliefs. We introduce a model capable of representing character beliefs in PDDL domains. This model allows characters to fail at actions when their beliefs about the world differ from the actual world state. Domains of this type can be compiled into purely intentional domains, and fed as input to intentional planners. The resulting stories feature characters that pursue their own intentions based on their own knowledge of the world, learn from mistakes to update their beliefs, and communicate information to each other. These types of stories are not possible with purely intentional domains.
Partial Retraining Substitute Model for Query-Limited Black-Box Attacks

Black-box attacks against deep neural network (DNN) classifiers are receiving increasing attention because they represent a more practical approach in the real world than white box attacks. In black-box environments, adversaries have limited knowledge regarding the target model. This makes it difficult to estimate gradients for crafting adversarial examples, such that powerful white-box algorithms cannot be directly applied to black-box attacks. Therefore, a well-known black-box attack strategy creates local DNNs, called substitute models, to emulate the target model. The adversaries then craft adversarial examples using the substitute models instead of the unknown target model. The substitute models repeat the query process and are trained by observing labels from the target model’s responses to queries. However, emulating a target model usually requires numerous queries because new DNNs are trained from the beginning. In this study, we propose a new training method for substitute models to minimize the number of queries. We consider the number of queries as an important factor for practical black-box attacks because real-world systems often restrict queries for security and financial purposes. To decrease the number of queries, the proposed method does not emulate the entire target model and only adjusts the partial classification boundary based on a current attack. Furthermore, it does not use queries in the pre-training phase and creates queries only in the retraining phase. The experimental results indicate that the proposed method is effective in terms of the number of queries and attack success ratio against MNIST, VGGFace2, and ImageNet classifiers in query-limited black-box environments. Further, we demonstrate a black-box attack against a commercial classifier, Google AutoML Vision.