Miracle-dz/UniFAR-FTUs huggingface dataset:
CognIDE: A Psychophysiological Data Integrator Approach for Visual Studio Code

Wearable devices capable of capturing psychophysiological data are a reality today. Recent studies indicate that the developer's cognitive indicators (e.g., level of attention and meditation) might affect code comprehension and maintenance tasks. However, current Integrated Development Environments (IDEs) and code editors like Visual Studio (VS) Code fall short of providing contextual information of cognitive indicators located throughout source code. This article proposes CognIDE, a tool-supported approach for integrating psychophysiological data related to cognitive indicators into the VS Code. CognIDE help VS code to push a step forward, providing actionable contextual information alongside the evolving source code. The CognIDE was evaluated through a survey with 6 professionals and interviews for investigating its effects on their perception of usefulness, ease of use, and intention to use in real-world settings. With a high acceptance of the professionals, the emerging results show the potential for using CognIDE to identify and prioritize the review of source code with specific cognitive indicators, mainly related to bugs or inadequate understanding of code snippets.
ENVIAR: ENVIronment dAta simulatoR

Context-aware applications (CAAs) sense and react to changes from the environment when performing their tasks. Testing such applications is challenging. Firstly, due to the number of combinations of possible events and values they can handle. Secondly, due to the different execution scenarios that may be hard to reproduce manually. We present the ENVIAR tool whose objective is to support test case generation and execution of Android CAAs. Generation combines a set of events that may trigger failure in such applications filtered by pairwise testing. Execution simulates the environment in which the CAA runs by sending mock data. Current version focus on Android events, GPS, and Internet sensor events and values. Empirical results have shown that the tool can generate test cases that detect context defects in real Android applications, particularly covering scenarios that would be tricky to run in a real environment.
Detection of Key Structure of Auroral Images Based on Weakly Supervised Learning

Weakly supervised learning is of interest and research by many people due to the large savings in labeling costs. To solve the high cost of manual labeling in the research of aurora image detection, an Aurora multi-scale network for aurora image dataset is proposed based on weakly-supervised learning. Firstly, the feature learning mechanism of dynamic hierarchical mimicking is adopted to improve the classification performance of the convolutional neural network based on the aurora image. Then, the multi-scale constraint is imposed on the network through the multi-branch input and output of different sizes. The final output of the auroral image class activation maps with more ideal results, the critical structure detection of auroral images based on imagelevel annotation is realized. Experiments show that the algorithm in this paper can effectively improve the class activation maps results of the auroral image, and has an ideal detection effect on the vital structure of the auroral image.
Simple Question Answering Over a Domain-Specific Knowledge Graph using BERT by Transfer Learning

We build and evaluate a baseline for simple question answering over a domain-speciﬁc knowledge graph by using a pretrained open-domain language model BERT. Training a neural network from scratch needs a large annotated dataset whereas transfer learning adapts a pre-trained language model and allows task-speciﬁc ﬁne-tuning with limited-data. However, building a domain-speciﬁc language model needs a large amount of domain-speciﬁc text, resource, and time for pretraining. But open-domain language models such as BERT are readily available for use. Hence, we evaluate the open-domain pretrained BERT for creating a domain-speciﬁc question answering baseline model that requires less amount of training data. In this work, we built a BioMed domain simple question answering system by ﬁne-tuning the open-domain BERT with a manually curated dataset of ˜600 questions from the Drugbank knowledge graph published by Bio2RDF.
Analysis of Machine Learning Methods for Predicting Stock Prices

In this research, we investigated the applicability of Long Short Term Memory (LSTM) and Convolutional Neural Networks (CNN) in forecasting the next day’s closing price of four major stock indices and explored de-noising techniques to improve the performance of these models. Our experiments show the use of Kalman Filters with the LSTM model provide the best forecast accuracy, reducing forecast error by at least 30% in three of the four ﬁnancial time series used in this study.
Dynamic Features Based on Flow-Correlation and HOG for Recognition of Discrete Facial Expressions

Facial expressions are the most preeminent means of conveying one’s emotions and play a significant role in interpersonal communication. Researchers are in pursuit of endowing machines with the ability to interpret emotions from facial expressions as that will make human-computer interaction more efficient. With the objective of effective affect cognition from visual information, we present two dynamic descriptors that can recognise seven principal emotions. The variables of the appearance-based descriptor, FlowCorr, indicate intra-class similarity and inter-class difference by quantifying the degree of correlation of optical flow associated with the image pair and each pre-designed template describing the motion pattern associated with different expressions. The second shape-based descriptor, dyn-HOG, finds the HOG values of the difference image derived by subtracting neutral face from emotional face, and is demonstrated to be more discriminative than previously used static HOG descriptors for classifying facial expressions. Recognition accuracies with multi-class support vector machine obtained on the CK+ and KDEF-dyn datasets are competent with the results of state-of-the-art techniques and empirical analysis of human cognition of emotions.
Mesoporous Silica Nanoparticles with Controllable Pore Size: Preparation and Drug Release

Mesoporous silica nanoparticles (MSNs), with controllable pore size at wider range (2-13 nm) and particle size about 100 nm, have been successfully prepared using different templates. N2 adsorptiondesorption isotherms, scanning electron microscopy (SEM) and transmission electron microscopy (TEM) were used to characterize the material. The results showed that the MSNs have high specific surface area, controllable pore-size and the pore volume, and uniform particle size. Their drug delivery properties were investigated. It was found that the amount of loading DOX is increased with surface area, and the multi-release experiments showed that they had a sustained-release property; MSNs with larger pore size had the larger amount of DOX.
Hierarchical Contextualized Representation Models for Answer Type Prediction

SeMantic AnsweR Type prediction (SMART) challenge proposed a task to determine the types of answers given natural language questions. Understanding answer types play a crucial role in question answering. In this paper, we present Hierarchical Contextualized-based models, namely HiCoRe, for the SAMRT task. HiCoRe builds on top of state of the art contextualized-based models and the hierarchical strategy to deal with the hierarchical answer types. The SMART results show that HiCoRe obtains promising performance for answer type prediction on DBpedia and Wikidata datasets.
Synthesising Tabular Data using Wasserstein Conditional GANs with Gradient Penalty (WCGAN-GP)

Deep learning based methods based on Generative Adversarial Networks (GANs) have seen remarkable success in data synthesis of images and text. This study investigates the use of GANs for the generation of tabular mixed dataset. We apply Wasserstein Conditional Generative Adversarial Network (WCGAN-GP) to the task of generating tabular synthetic data that is indistinguishable from the real data, without incurring information leakage. The performance of WCGAN-GP is compared against both the ground truth datasets and SMOTE using three labelled real-world datasets from diﬀerent domains. Our results for WCGAN-GP show that the synthetic data preserves distributions and relationships of the real data, outperforming the SMOTE approach on both class preservation and data protection metrics. Our work is a contribution towards the automated synthesis of tabular mixed data.
Improved Speech Synthesis using Generative Adversarial Networks

Artiﬁcially generated audio and speech have been a major area of machine learning research in recent years. Generative Adversarial Networks (GANs) have been at the forefront of the research progress in this domain. WaveGAN and SpecGAN are some of the current state of the art methods for speech synthesis, that utilise generative modelling to produce synthetic speech signals. Of particular interest, SpecGAN uses spectrograms of speech as training data for the GAN. With current available models like WaveGAN, the structure and linguistic information of the audio is not captured and leads to synthesized audio with high noise. In this paper, we propose a method we call Mel-spectrogram GAN (MS-GAN) that instead uses the Mel-Spectrogram of the audio signal as an aid in approximating the human auditory system response more closely than narrow frequency bands. It uses a reconstructed spectrogram with a mel-scale. We demonstrate, using this approach a 23% higher inception score than WaveGAN. Further, we establish an improved version of Conditional MSGAN that quickly learns the data distribution of each of the classes to produce better quality speech and further increases the score by 32%. These results suggest that our Conditional MSGAN architecture is a promising approach for improved speech synthesis using GANs.
ChImp: Visualizing Ontology Changes and their Impact in Protégé

Today, ontologies are an established part of many applications and research. However, ontologies evolve over time, and ontology editors---engineers and domain experts---need to be aware of the consequences of changes while editing. Ontology editors might not be fully aware of how they are influencing consistency, quality, or the structure of the ontology, possibly causing applications to fail. To support editors and increase their sensitivity towards the consequences of their actions, we conducted a user survey to elicit preferences for representing changes, e.g., with ontology metrics such as number of classes and properties. Based on the survey, we developed ChImp---a Protege plug-in to display information about the impact of changes in real-time. During editing of the ontology, ChImp lists the applied changes, checks and displays the consistency status, and reports measures describing the effect on the structure of the ontology. Akin to software IDEs and integrated testing approaches, we hope that displaying such metrics will help to improve ontology evolution processes in the long run.
Using Navigable Small Worlds to Speed Up Time-Series Classification

It is not easy to apply standard Machine Learning methods to time-series classiﬁcation because of the nature of the data. k -Nearest Neighbour ( k -NN) can be applied because eﬀective distance measures for time-series are available (e.g. Dynamic Time Warping (DTW)). However, these eﬀective measures are inclined to be quite computationally expensive – DTW is O ( t 2 ) in the length of the time-series. This is a problem because the resultant retrieval time is O ( nt 2 ) when n is the number of training samples. Methods to improve k -NN retrieval performance exist, but some constraints apply. Some methods require that the distance measure is a proper metric; others are only eﬀective in low dimension spaces. In this paper, we evaluate the eﬀectiveness of Navigable Small Worlds (NSW) for time-series classiﬁcation using k -NN. NSW organises training samples into a small-world network for eﬃcient retrieval. We show that this strategy can signiﬁcantly speed up time-series classiﬁcation at the cost of some loss of accuracy.
An Experimental Study of Neural Morpheme Segmentation Models for Russian Word Forms

Morphemic structure of words is useful for various NLP problems, in particular, for deriving a meaning of unknown words in languages with rich morphology, such as Russian. For Russian, several machine learning models for automatic morpheme segmentation of words were built, but only for parsing their lemmas. Meanwhile, signiﬁcantly varying word forms are present in texts, among them unknown words are often encountered, and their lemmas are unknown. The paper reports on experiments for comparing two ways to automatically segment Russian word forms, both ways involve splitting into morphs and classiﬁcation of resulted morphs. The former is based on a neural model trained on a data set automatically augmented with segmented word forms, the latter produces segmentation through predicted lemma and a pre-trained neural morpheme segmentation model for lemmas. It was shown that the models have comparable quality in morpheme segmentation with classiﬁcation, and the model based on the augmented dataset slightly outperforms in word-level classiﬁcation accuracy.
Active Learning for the Text Classification of Rock Climbing Logbook Data

This work applies active learning to the novel problem of automatically classifying user-generated logbook comments, published in online rock climbing forums. These short comments record details about a climber’s experience on a given route. We show that such comments can be successfully classiﬁed using a minimal amount of training data. Furthermore, we provide valuable insight into real-world applications of active learning where the cost of annotation is high and the data is imbalanced. We outline the beneﬁts of a model-free approach for active learning, and discuss the diﬃculties that are faced when evaluating the use of additional training data.
Application Research of Model-Free Reinforcement Learning under the Condition of Conditional Transfer Function with Coupling Factors

Dynamic systems are ubiquitous in nature. The analysis of the stability and performance of dynamic systems has been a research hotspot in control science and operations research for a long time. In this paper, we construct and analyze an actual sequential decision-making problem of dynamic system. The Model-Free reinforcement learning algorithms are used to optimize this problem. The problem is analyzed in detail through adaptive control theory and information theory, also the extreme performance of the algorithm is pointed out. In this paper, we select three classic Model-Free reinforcement learning algorithms, DQN, DQN-PER, and PPO, to compare and analyze their performance on the timing series decision problem we construct.
2D/3D Visualization of Large-Scale Wind Field based on WebGL

With the increasing amount of data in the marine environment and the user's affluent requirements for visualization, traditional CPU-based visualization is becoming more and more difficult to complete specific tasks. To visualize the large-scale marine vector data, the particle system method is one of the most popular methods, but the data scale and complex calculations heavily restrict the efficiency of these CPU-based algorithms. This paper proposes a frame buffer visualization method with two strategies based on WebGL (FBO-W). To reduce the dense particle resulted by the increased visualization resolution, we dynamically set the number of particles based on different resolutions (PN-R); then, to alleviate the problem of unbalanced particle distribution during visualization, the strategy of dynamically adjusting particle drop rate based on particle velocity (DR-V) is incorporated. Experimental results show that the efficiency of data loading and rendering is greatly improved with higher visualization quality.
On the Influence of Surface Topography on the Electric Double Layer Structure and Differential Capacitance of Graphite/Ionic Liquid Interfaces

Molecular simulations reveal that the shape of differential capacitance (DC) versus the electrode potential can change qualitatively with the structure of the electrode surface. Whereas the atomically flat basal plane of graphite in contact with a room-temperature ionic liquid generates camel-shaped DC, the atomically corrugated prismatic face of graphite with the same electrolyte exhibits bell-shaped behavior and much larger DCs at low double-layer potentials. The observed bell-shaped and camel-shaped DC behavior was correlated with the structural changes occurring in the double layer as a function of applied potential. Therefore, the surface topography clearly influences DC behavior, suggesting that attention should be paid to the electrode surface topography characterization in the studies of DC to ensure reproducibility and unambiguous interpretation of experimental results. Furthermore, our results suggest that controlling the electrode roughness/structure could be a route to improving the energy den.
Improving the Barrier Properties of Poly(Lactic Acid) by Blending with Poly(Ethylene-Co-Vinyl Alcohol)

Poly(lactic acid) (PLA)/poly(ethylene-co-vinyl alcohol) (EVOH) blends were prepared via melt blending to improve the barrier properties of PLA. The phase morphologies and final properties (rheological behavior, thermal and dynamical-mechanical features, barrier properties, and mechanical behaviors) of the blends were investigated as a function of the EVOH content. The results indicated that hydroxyl groups of EVOH promoted the degradation of PLA, and thus affected the viscosities and morphologies of the resulting blends. The intrinsic viscosities of PLA in the blends decreased with the content of EVOH. The PLA and EVOH presented typical phase-separated morphologies, with a relatively small domain size of the EVOH phase. The EVOH enhanced the cold-crystallization behavior of PLA. The barrier properties to water vapor and oxygen increased linearly with increasing EVOH content.
Software Quality is Multidimensional: Let's play with Tensors

In the face of the dynamic nature of modern software systems, the quality of software products must be monitored and assured from different perspectives at the same time and throughout the development cycle. Although different characteristics and subcharacteristics can represent a software product quality, they have been historically studied and observed in a unidimensional way, which restricts the ability to observe the multiple relationships among quality characteristics. There is empirical evidence indicating the multidimensionality of this phenomenon. Therefore, an n-dimensional software quality base model is presented. The fundamentals of tensor algebra provide its basis, which explores the combination of different dimensions and relations of quality (internal, external, and in use) to suggest the level of quality of a software product. Preliminary results of its operationalization are presented to support this proposal.
Analysis and Simulation of crowd in Airport Multiple Transport Modes

This paper presents the analysis and simulation of crowd in airport multiple transport modes. We analyze the different transportation modes in the airport and crowd distribution. A pedestrian behavior model in airport multiple transport modes based on the potential energy field is utilized to analyze the crowd flow. Based on the pedestrian behavior model of fine mesh and artificial potential field, a simulation system framework of crowd flow is proposed. Taking the real scene of an airport integrated hub as a scene, the process of crowd flow is simulated. The movement track and influencing factors of pedestrians are analyzed. On this basis, the flow time of different numbers of people is obtained. The results are basically consistent with the actual situation. In addition, a simulation-- optimization is presented for the crowd flow of airport integrated hub exit gates. The obtained results show that the optimized scenario shortens the crowd flow time within a certain number of people. This study would be helpful for architectural design, pedestrian organization, emergency plan formulation and so on.
From Dusk till Dawn: Reflections on the Impact of COVID-19 on the Development Practices of a R&D Project

Responding to change over following a plan is one of the Agile Manifesto's values. According to its original perspective, development teams should embrace changes due to a better understanding of the business domain or about the users' needs. This kind of change is well understood and explored by practitioners and researchers. However, the issue of how development teams embrace changes due to unexpected, severe, and atypical external events (like the ones we are facing during COVID-19) have not been fully discussed. In this paper, we present our experience in managing the expectations and changing the development methods to mitigate the risks of carrying out a research and development project (named Virtual Social Office-VSO) during a pandemic time. To this end, we report on an observational study that explains the main differences between the procedures we expected to follow before the pandemic and the procedures we are following. We also present the perceptions of both stakeholder and development teams about the impact of COVID-19 on VSO development. Although the development team quickly adapted itself to work from home, we observed some impact on the development plans. In particular, trying to reconcile the trade-offs between completeness and the tight schedule for the first release was a challenge, since we depended on different partners to collect the information that we need to make available in a mobile application (app) that corresponds to one of the VSO project outcomes. According to one stakeholder, the initial effort to design and prototype the VSO app (using the Design Thinking approach) before the start of the pandemic, has been considered essential to mitigate part of the threats that arose due to the COVID-19. We discuss part of these threats in this paper and the actions we have been taking to mitigate them. In addition, according to members of the development team, working remotely presents both challenges (e.g., adapt to a new routine, prepare a comfortable place, isolation feelings), and benefits (e.g., working hours flexibility, less distraction from co-workers).
HopsFS-S3: Extending Object Stores with POSIX-like Semantics and more (industry track)

Object stores have become the de-facto platform for storage in the cloud due to their scalability, high availability, and low cost. However, they provide weaker metadata semantics and lower performance compared to distributed hierarchical file systems. In this paper, we introduce HopsFS-S3, a hybrid distributed hierarchical file system backed by an object store while preserving the file system's strong consistency semantics. We base our implementation on HopsFS, a next-generation distribution of HDFS with distributed metadata. We redesigned HopsFS' block storage layer to transparently use an object store to store the file's blocks without sacrificing the file system's semantics. We also introduced a new block caching service to leverage faster NVMe storage for hot blocks. In our experiments, we show that HopsFS-S3 outperforms EMRFS for IO-bound workloads, with up to 20% higher performance and delivers up to 3 AX the aggregated read throughput of EMRFS. Moreover, we demonstrate that metadata operations on HopsFS-S3 (such as directory rename) are up to two orders of magnitude faster than EMRFS. Finally, HopsFS-S3 opens up the currently closed metadata in object stores, enabling correctly-ordered change notifications with HopsFS' change data capture (CDC) API and customized extensions to metadata.