Reviewerly/exHarmony huggingface dataset:
Predicting Bug Priority Using Topic Modelling in Imbalanced Learning Environments

Manual classification of bug reports is time-consuming as the reports are received in large quantities. Alternatively, this project proposed automatic bug prediction models to classify the bug reports. The topics or the candidate keywords are mined from the developer description in bug reports using RAKE algorithm and converted into attributes. These attributes together with the target attribute—priority level—construct the training datasets. Naïve Bayes, logistic regression, and decision tree learner algorithms are trained, and the prediction quality was measured using area under recursive operative characteristics curves (AUC) as AUC does not consider the biasness in datasets. The logistics regression model outperforms the other two models providing the accuracy of 0.86 AUC whereas the naïve Bayes and the decision tree learner recorded 0.79 AUC and 0.81 AUC, respectively. The bugs can be classified without developer involvement and logistic regression is also a potential candidate as naïve Bayes for bug classification.
Paying more Attention to Snapshots of Iterative Pruning: Improving Model Compression via Ensemble Distillation.

Network pruning is one of the most dominant methods for reducing the heavy inference cost of deep neural networks. Existing methods often iteratively prune networks to attain high compression ratio without incurring significant loss in performance. However, we argue that conventional methods for retraining pruned networks (i.e., using small, fixed learning rate) are inadequate as they completely ignore the benefits from snapshots of iterative pruning. In this work, we show that strong ensembles can be constructed from snapshots of iterative pruning, which achieve competitive performance and vary in network structure. Furthermore, we present simple, general and effective pipeline that generates strong ensembles of networks during pruning with large learning rate restarting, and utilizes knowledge distillation with those ensembles to improve the predictive power of compact models. In standard image classification benchmarks such as CIFAR and Tiny-Imagenet, we advance state-of-the-art pruning ratio of structured pruning by integrating simple l1-norm filters pruning into our pipeline. Specifically, we reduce 75-80% of total parameters and 65-70% MACs of numerous variants of ResNet architectures while having comparable or better performance than that of original networks. Code associate with this paper is made publicly available at this https URL.
Incremental Learning on Growing Graphs

Graphs have attracted numerous attention in varied areas and are dynamic in many scenarios. Among dynamic graphs, growing graphs with frequently expanding vertex and edge sets are typical and widely existed, e.g. the rapidly growing social networks. Confronting such growing data, existing methods on either static or dynamic graphs take the entire graph as a whole and may suffer from high computation cost and memory usage due to the continual growth of graphs. To tackle this problem, we introduce incremental graph learning (IGL), a general framework to formulate the learning on growing graphs in an incremental manner, where traditional graph learning method could be deployed as a basic model. We first analyze the problems of directly finetuning on the incremental part of graph, and theoretically discuss the unbiased and edge-preserved conditions of IGL. In our method, when the graph grows with new-coming data, we select or generate vertices and edges within restricted sizes from the previous graph to update current model together with the new data. Here, two strategies, i.e. sample-based and cluster-based, are proposed for learning with restricted time and space complexity. We conduct experiments on the node classification and link prediction tasks of multiple datasets. Experimental results and comparisons show that our method achieves satisfying performance with high efficiency on growing graphs.
Collaborative Learning with Pseudo Labels for Robust Classification in the Presence of Noisy Labels

Supervised learning depends on labels of dataset to train models with desired properties. Therefore, data containing mislabeled samples (a.k.a. noisy labels) can deteriorate supervised learning performance significantly as it makes models to be trained with wrong targets. There are technics to train models in the presence of noise in data labels, but they usually suffer from the data inefficiency or overhead of additional steps. In this work, we propose a new way to train supervised learning models in the presence of noisy labels. The proposed approach effectively handles noisy labels while maintaining data efficiency by replacing labels of large-loss instances that are likely to be noise with newly generated pseudo labels in the training process. We conducted experiments to demonstrate the effectiveness of the proposed method with public benchmark datasets: CIFAR-10, CIFAR-100 and Tiny-ImageNet. They showed that our method successfully identified correct labels and performed better than other state-of-the-art algorithms for noisy labels.
Security Analysis and Improvement of Certificateless Aggregate Signature Scheme for Vehicular Ad Hoc Networks

In 2018, Wang Daxing and Teng Jikai proposed a certificateless aggregate signature scheme for vehicular ad-hoc networks, and proved that their scheme was existentially unforgeable in the random oracle model. To analyze the security of this scheme, three types of forgery attacks are given:  honest-but-curious” KGC attacks, malicious KGC and RSU coalition attacks, and internal signers’ coalition attacks. The analysis results show that the certificateless aggregate signature scheme designed by Wang Daxing and Teng Jikai is insecure against these three types of attacks. To resist these attacks, an improved certificateless aggregate signature scheme is further proposed. The new scheme not only satisfies existential unforgeability under adaptive chosen-message attacks, but also resists effectively coalition attacks.
Image Processing Based Signature Duplication and its Verification

Currently, a lot of time is needed for the verification of the signature manually. The need of developing an automated checking system is felt because of signature forgery in various transactions. The dynamic signature is a biometric trait that is used in identification. Automatic signature verification is an application of Image Processing. The aim of the model is to identify the correct signature for reducing fraudulent transactions. It is difficult to have multiple signatures of the same person so the idea is to duplicate a given signature number of times by inducing variations and using verifiers to give a similarity. For duplication of signature, the approach of using the Cognitive Inspired Model is used. The approach for creating human-like signatures can be done by introducing Intra-Component and Inter-Component variability. For verification, Structural Similarity Index (SSIM) and Image Hashing techniques are used.
A Framework for Analysis, Ontological Evaluation, and Visualization in Preparation to Predictive Analytics in Pediatric Brain Tumor Research

We provide a generalizable framework for the systematic analysis of complicated, longitudinal clinical features in pediatric cancer. We use a threefold pipeline of exploratory data analysis, ontological categorization through a multi-modal data transformation process towards predictive analytics. We derive a data-driven phenotype from a subset of a sample of over 1900 brain tumor cases focused specifically on High-Grade Gliomas. We implement an analyst-friendly process to make machine learning-ready data sets based on domain ontologies ready for enumeration and vectorization. The results are clinical domain expert readable data points from 4.3 million observational events across 16,000 patient days. In this research, we address the gap in phenotypic data features by utilizing extensive harmonized observational clinical data and identify resources and specific processes for their use in rare tumor research.
Routing and Capacity Optimization Based on Estimated Latent OD Traffic Demand

This paper introduces a method to estimate latent traffic from its origin to destination from the link packet loss rate and traffic volume. In addition, we propose a method for the joint optimization of routing and link provisioning based on the estimated latent traffic. Observed traffic could deviate from the original traffic demand and become latent when the traffic passes through congested links because of changes in user behavioral and/or applications as a result of degraded quality of experience (QoE). The latent traffic is actualized by improving congested link capacity. When link provisioning is based on observed traffic, actual traffic might cause new congestion at other links. Thus, network providers need to estimate the origin-destination (OD) original traffic demand for network planning. Although the estimation of original traffic has been well studied, the estimation was only applicable for links. In this paper, we propose a method to estimate latent OD traffic by combining and expanding techniques. The method consists of three steps. The first step is to estimate the actual OD traffic and loss rate from the actual traffic and packet loss rate of the links. The second step is to estimate the latent traffic demand. Finally, using this estimated demand, the link capacity and routing matrix are optimized. We evaluate our method by simulation and confirm that congestion could be avoided by capacity provisioning based on estimated latent traffic, while provisioning based on observed traffic retains the congestion. The combined method can avoid congestion with an increment of 23% compared with capacity provisioning only. We also evaluated our method's adaptability, i.e., the ability to estimate the required parameter for the estimations using fewer given values, but values obtained in the environment.
I Catching : Computationally Operationalising Narrative Perspective for Stylometric Analysis

An automated way to measure a text's perspective is essential to a computational analysis of literary style and narrative. However, no such measure of narrative perspective exists to date. We aim to provide such a measure by taking two approaches to operationalise narrative perspective. The first measure is a machine learning approach, the second is based on computing the ratio of combinations of pronouns. Our research demonstrates that narrative perspective can be predicted with high accuracy and provides a reflection on the advantages and pitfalls of both methods.
Automatic Chinese Proofreading Based on Deep Learning

The rapid development of the Internet enables a large number of users to generate a large amount of network text. Correcting the grammatical errors implied in the text can make the text smoother and easier to read. It is obviously unrealistic to process massive text based on manual proofreading, which makes the text automatic. Proofreading tasks have received more attention in recent years. The rise of deep learning makes the sequence-to-sequence learning method widely used in text proofreading tasks. This paper improves the existing Chinese text automatic proofreading method based on deep learning, mainly from the following aspects: This paper uses depth-based Learning to correct and correct texts, compared to the traditional n-gram method in detecting sentence fluency, detecting incorrect words, and also generating alternative words and other accuracy has been greatly improved. The experimental results show that the training of this model can converge quickly, and the training accuracy rate is as high as 90.64%, which can effectively and accurately correct Chinese text.
Parallelization of Local Path Planning for High Reliable Autonomous Drones

Safety is crucial for autonomous drones. This paper proposed a new application to realize a high-reliable drone under the edge computing environment. To realize safer drones, we focus on the unexpected and unsupposed situations in drone flight and aim to cope with the situations by calculating the countermeasures for various expected scenarios beforehand. Especially, this paper focused on local path planning, and parallelization of the local path planning is proposed to calculate the many paths to escape the expected obstacles in various scenarios. Experimental results show the effectiveness of the proposed method.
Playing With Unicorns: AI Dungeon and Citizen NLP

Author(s): Raley, Rita; Hua, Minh | Abstract: AI Dungeon 2 is an indie text adventure game that caught traction within the gaming and hobbyist machine learning communities for its promise of “infinite” customizable adventures, which are generated and narrated by GPT-2, OpenAI’s 1.5 billion parameter language model. Samples of gameplay illustrate AID’s remarkable linguistic competence and domain knowledge, as well as its capacity for what can only be described as wackiness. More striking are AID’s innovative gameplay mechanics, which reimagine how we interact with large language models. Game play entails a procedural and incremental process of engaging with GPT-2 that opens up the possibility of developing a holistic and interdisciplinary framework for meaningful qualitative evaluation of language models that does not have commercial use as its necessary endgame. With respect to both evaluation and writing itself, AID situates human players inextricably “in the loop” as necessary partners with autonomous systems. Our article thus reads AID both as an example of current hobbyist relations with machine learning and as a responsible model for future human-AI collaborative creative practices.
Research on Color Image Defogging Algorithm based on MSR and CLAHE

To study the defogging of color images, an improved multi-scale Retinex (MSR) algorithm combined with contrast limited adaptive histogram equalization(CLAHE) algorithm is proposed in this paper. Two different defogging methods are proposed for the image without color deviation and the image with color deviation. For the image without color deviation, the RGB color space of the input image is converted to the lab color space. Then the image is converted to RGB color space after MSR processing of L channel. Finally, the image contrast is improved by CLAHE algorithm. Realize the image defogging while keeping the original color of the image. For the image with color deviation, the input image is processed by MSR, the stretch degree of contrast is determined according to the mean and variance of the separated reflection components. Finally, the image contrast is further improved by CLAHE processing to realize the defogging of the restored scene. The experimental results show that the two algorithms in this paper have better effects in image defogging, image primary color maintenance, image scene color recovery and so on.
Effect of Family Environment, School Environment, Self Concept Development and Motivation for Students Emotional Intelligence

The study aims to Determine the effect of family environment, school environment, self-concept, and Motivation of Student Emotional Intelligence development. In a study using Smart PLS 3 and the object in this study are the general public. The population in this research was 200 respondents. The sample in this study used a sample of convenience samples of the type of data collected is quantitative of data by using questionnaires and analyzed using validity and reliability. The result showed that there are significant between Family Environment, school environment, Self Concept, and Motivation on Development of Emotional intelligence learners. Based on the validity of the test result that shows, the entire coefficient indicator has a value of > 0.5 and values Average Variance Extracted (AVE) Family Environment, School Environment, Self Concept, Motivation of Student Emotional Intelligence development.
Sensitivity of Arabic Sentiment Analysis Tools

While the accuracy of Arabic text sentiment analysis tools continues to improve, the accuracy continues to lag behind similar tools for Latin-based languages. In this work we review some of the unique challenges inherent in the Arabic language that contribute to this accuracy lag beyond the scale-based economic drivers propelling enhanced accuracy in other languages. We then identify some of the most promising new tools and provide a framework for evaluating the differences in sensitivity and polarity. While there is not yet a universal standard scale for sentiment polarity, we attempt to provide a normalized basis upon which to compare the degree to which various tools tend to classify text segments toward either end (or the middle) of the polarity spectrum.
Approaches for describing reference architectures: A systematic mapping study

This work was supported in part by CNPq (Grant: 141602/2017-1, 312634/2018-8), FAPESP (Grant: 2018/07437-9, 2018/24173-5, 2017/22107-2, 2017/06195-9), and CoSmart Spanish project (contract TIN2017-83964-R).
Multiple Interaction Learning with Question-Type Prior Knowledge for Constraining Answer Search Space in Visual Question Answering

Different approaches have been proposed to Visual Question Answering (VQA). However, few works are aware of the behaviors of varying joint modality methods over question type prior knowledge extracted from data in constraining answer search space, of which information gives a reliable cue to reason about answers for questions asked in input images. In this paper, we propose a novel VQA model that utilizes the question-type prior information to improve VQA by leveraging the multiple interactions between different joint modality methods based on their behaviors in answering questions from different types. The solid experiments on two benchmark datasets, i.e., VQA 2.0 and TDIUC, indicate that the proposed method yields the best performance with the most competitive approaches.
Track report of Future Internet Services and Applications (FISA’2020)

The “Future Internet Services and Applications” (FISA) track focuses on three complementary aspects that have to be considered while setting up future Internet services: (i) their modeling, provisioning and management, (ii) data protection, and (iii) data collection, storage and analysis. FISA is in its fifth edition and aims at offering to academic and industrial researchers as well as practitioners a platform for discussions related to the aforementioned aspects of future Internet services and applications. This report briefly presents the main topics of FISA and lists the accepted papers.
The Effect of Learning Motivation and Social Support on Learning Achievement of the Students in Aisyiyah Female Orphanage Serangan Yogyakarta

This research aims to find out whether there is a positive and significant effect of (1) learning motivation on students' achievement, (2) social support on students' achievement, (3) learning motivation, and social support on students' achievement.This research was quantitative, located at Aisyiyah female orphanage Serangan.The instrument used for data collection was the questionnaire method, and SPSS 17.0 was used as data calculation software.The questionnaire method was used to measure the effect of learning motivation and social support on the students' learning achievement, whereas SPSS 17.0 software was used to measure the students' learning achievement.The research result shows that Adj R Square value is 0,533, which means that learning achievement has been 53% affected by the variable of motivation and social support.The rest, 47%, is explained by the external variable.It can be concluded that the independent variable (learning motivation and social support) has an effect on learning motivation.
Ecumenical Modal Logic

The discussion about how to put together Gentzen’s systems for classical and intuitionistic logic in a single unified system is back in fashion. Indeed, recently Prawitz and others have been discussing the so called ecumenical Systems, where connectives from these logics can co-exist in peace. In Prawitz’ system, the classical logician and the intuitionistic logician would share the universal quantifier, conjunction, negation, and the constant for the absurd, but they would each have their own existential quantifier, disjunction, and implication, with different meanings. Prawitz’ main idea is that these different meanings are given by a semantical framework that can be accepted by both parties. In this work we extend Prawitz’ ecumenical idea to alethic $$\mathsf {K}$$ -modalities.
Los gráficos y la pandemia. Reflexiones para la educación científica en tiempos de incertidumbre

The COVID-19 pandemic shows that certain knowledge and ways of knowing linked to science education do not reach the common denominator of citizenship. In particular, this work focuses on the use of different types of graphical representations during the pandemic in mass media communication and political discourse. This invites a detailed reflection based on our research results on the role of graphics in science education. Thus, it is possible to raise perspectives for a post-pandemic education that includes the representational issue in science education for citizenship.
A Contextual Semi-Assisted Project-Based Learning on Ocean Wave Energy: Pre-service Physics Teachers’ Perceptions

The lack of technological innovation in the utilization of ocean wave energy as a renewable energy source is caused by a lack of public knowledge about this matter. Therefore, it needs to be introduced early, especially to pre-service teachers through innovative and fun learning. The aim of this study was to identify the responses of pre-service physics teachers about the learning program to introduce the concept of utilizing ocean wave energy. The research subjects were 31 sixth semester students who are taking environmental physics lectures in the physics education study program at one of the teachers' university in West Nusa Tenggara Province for the 2019/2020 academic year. The sample was taken by using purposive sampling technique. Data collection was carried out by means of observation, interview, and questionnaire techniques. All data collected were then analyzed simultaneously descriptively. The findings of this study showed that almost 90% pre-service physics teacher students agreed that the learning program had a positive impact on increasing interest, motivation, critical thinking skills, and creative thinking skills, and made them easier to solve problems and grow solid cooperation. Therefore, it can be concluded that pre-service physics teacher students agree to the use of semi-assisted project-based learning programs with contextual problems.
Graph Learning Approaches for Graph with Noise: Application to Disease Prediction in Population Graph

Graph neural networks have been developed for various node classification tasks in graph data. However, the noise in graph data would affect the effectiveness of training and prediction of these graph learning models. In this paper, we propose a graph learning approach, named GCN_CL, which introduces a confident learning method into the graph convolutional networks model to classify nodes in graph data with label noise. The proposed approach includes node classification module and confident learning module, where the confident learning module selects clean nodes with high confident labels for node classification module to train an accurate model in the graphs with label noise. Pseudo label method is further applied on unlabeled data to increase samples for confident learning and improve the classification performance of subsequent node classification module. We evaluated classification performance of GCN_CL and compared our approach against other models to classify autism spectrum disorders patients versus health controls in population graph, which is constructed from ABIDE I dataset. The experimental results show GCN_CL achieves achieves the best performance in the population graph with different artificial noise levels.