Reviewerly/exHarmony huggingface dataset:
Managing COVID-19 Test Certificates Using Blockchain Platform

In early 2020, World Health Organization (WHO) declared that the world is facing the COVID-19 pandemic. This pandemic is caused by the emergence of a new type of coronavirus which spreads very quickly. Because of this pandemic, the government has issued health protocols that use a lot of COVID-19 tests. However, cases of forgery of test certificates often occur in paper-based test certificate. To overcome this problem, we propose a COVID-19 test certificate management system using blockchain technology. By using blockchain technology, the system can be used by multiple entities, even though the entities that use it do not have trust relationship beforehand. This is possible because blockchain is a technology that uses a decentralized network that can store its data with a secure consensus mechanism. The system built using the Ethereum platform, which is a public non-permissioned blockchain that supports decentralized application development through smart contracts that executed via EVM (Ethereum Virtual Machine). The system has been developed using 3-layers architecture, which contains network, software, and application layer. The evaluation shows that the system successfully fulfills the COVID-19 test certificate management requirement. The system can be used for tester registration, and create, revoke, display certificates. Furthermore, by using encryption and digital signature mechanism, the system can protect patient privacy and data integrity. The system has also succeeded in fulfilling the availability and security requirements. However, due to the nature of Ethereum consensus mechanism, the system has much lower response time, compared to the centralized counterpart.
On-line and Off-line Educational Means of the Industrial Region Engineers and Academic Staff Training

Regional well-being depends on the quality of engineering qualifications. Universities deal with a twofold task: training students to work in high-tech industries and the employee qualifications continuous development at industrial enterprises. Rapid technological changes lead to a negative synergetic effect and a problem of high-quality education: lack of teaching expertise leads to insufficient employee readiness to work in new conditions. To solve this problem, we suggest introducing a combination of online and offline means to train engineers and academic staff of technical departments. These means include: online monitoring of job functions update along with offline discussions with company management; project and innovation activities training; providing online information concerning advanced programs of additional professional education; creating electronic analogs storing educational content of distance learning programs; organizing education using project-based learning technologies and mutual learning.
The Secret to Better AI and Better Software (Is Requirements Engineering)

Much has been written about the algorithmic role that AI plays for automation in SE.But what about the role of AI, augmented by human knowledge?Can we make a profound advance by combining human and artificial intelligence?Researchers in requirements engineering think so, arguing that requirement engineering is the secret weapon for better AI and better software.
Up-to-Down Network: Fusing Multi-Scale Context for 3D Semantic Scene Completion

An efficient 3D scene perception algorithm is a vital component for autonomous driving and robotics systems. In this paper, we focus on semantic scene completion, which is a task of jointly estimating the volumetric occupancy and semantic labels of objects. Since the real-world data is sparse and occluded, this is an extremely challenging task. We propose a novel framework, named Up-to-Down network (UDNet), to achieve the large-scale semantic scene completion with an encoder-decoder architecture for voxel grids. The novel up-to-down block can effectively aggregate multi-scale context information to improve labeling coherence, and the atrous spatial pyramid pooling module is leveraged to expand the receptive field while preserving detailed geometric information. Besides, the proposed multi-scale fusion mechanism efficiently aggregates global background information and improves the semantic completion accuracy. Moreover, to further satisfy the needs of different tasks, our UDNet can accomplish the multi-resolution semantic completion, achieving faster but coarser completion. Detailed experiments in the semantic scene completion benchmark of SemanticKITTI illustrate that our proposed framework surpasses the state-of-the-art methods with remarkable margins and a real-time inference speed by using only voxel grids as input.
Topic modelling of racist and xenophobic YouTube comments. Analyzing hate speech against migrants and refugees spread through YouTube in Spanish

Racist and xenophobic hate speech transmitted online is of increasing concern, as social media provide the most suitable channel for its rapid, uncontrolled and massive spread. However, although this type of hate has been studied on diverse social platforms, the academy has not yet focused the attention on the speech generated on YouTube. This work aims to fill this gap in the literature, analyzing the racist and xenophobic hate speech that is spread through YouTube comments in Spanish. Specifically, a manual classification has been carried out to detect hate messages, as well as latent sentiments in comments. On the other hand, a topic modelling was carried out exclusively on hate comments. The results show the characteristics of the racist/xenophobic discourse that spreads through YouTube in Spanish, which, in general terms, represents migration as a burden and a threat, and calls for a government that carries out tougher and more restrictive immigration measures.
A Generalized Method for Sentiment Analysis across Different Sources

Sentiment analysis is widely used in a variety of applications such as online opinion gathering for policy directives in government, monitoring of customers, and staff satisfactions in corporate bodies, in politics and security structures for public tension monitoring, and so on. In recent times, the field met with new set of challenges where new algorithms have to contend with highly unstructured sources for sentiment expressions emanating from online social media fora. In this study, a rule and lexical-based procedure is proposed together with unsupervised machine learning to implement sentiment analysis with an improved generalization ability across different sources. To deal with sources devoid of syntactic and grammatical structure, the approach incorporates a ruled-based technique for emoticon detection, word contraction expansion, noise removal, and lexicon-based text preprocessing using lexical features such as part of speech (POS), stop words, and lemmatization for local context analysis. A text is broken into number of tokens with each representing a sentence and then lexicon-dependent features are extracted from each token. The features are merged together using a combining function for a given text before being used to train a machine learning classifier. The proposed combining functions leverage on averaging and information gain concepts. Experimental results with different machine leaning classifiers indicate that improved performance with great deal of generalization capacity across both structured and nonstructured sources can be realized. The finding shows that carefully designed lexical features reinforce learning process in unsupervised learning more than using word embeddings alone as the features. Obtained experimental results from movie review dataset (recall = 74.9%, precision = 70.9%, F1-score = 72.9%, and accuracy = 72.0%) and twitter samples’ datasets (recall = 93.4%, precision = 89.5%, F1-score = 91.4%, and accuracy = 91.1%) show the efficacy of the proposed approach in comparison with other state-of-the-art research studies.
sknet: A Python framework for Machine Learning in Complex Networks

Recent advances in Machine Learning, an area that leverages data to identify patterns, and in Complex Networks, an area which leverages connections between entities to identify complex phenomena and can be considered as an extension to graph theory, are changing society.Both of those areas can be related to the task of 'learning' from data (Silva & Zhao, 2016).There seems, however, to be a gap between those two research areas.It has already been shown that one can leverage both, using complex networks to improve machine learning methods, and using machine learning to exploit the information on complex networks to achieve better results.However, little to no implementation of methods that can be used on both areas has been open-sourced.And for those who did, it was not done in any unified way.sknet exists as a library to solve this gap.
Assessment of Online Learning Effectiveness by Students of Engineering and IT Degree Programs

In 2020, traditional education will face a struggle in replacing full-time education with distance teaching and learning. The findings of a study on the effectiveness of distance teaching and learning are presented in this article. The article's content is based on research conducted at the St. Petersburg Electrotechnical University “LETI.” The primary focus of the study was on full-time students of bachelor's and master's engineering and IT degree programs of the university. The results of a survey of students' satisfaction with distance learning in 2020 are described in this article. The survey's main finding is a firm determination of the most successful techniques of teaching theoretical content and practical classes over the internet. The usefulness of several remote learning monitoring strategies is also examined. The results of a comparison of senior and junior students' perceptions of distance learning practice are also presented in the paper.
Considerations on the region of interest in the ROC space

Receiver Operating Characteristic curves have been widely used to represent the performance of diagnostic tests. The corresponding area under the curve, widely used to evaluate their performance quantitatively, has been criticized in several respects. Several proposals have been introduced to improve area under the curve by taking into account only specific regions of the Receiver Operating Characteristic space, that is, the plane to which Receiver Operating Characteristic curves belong. For instance, a region of interest can be delimited by setting specific thresholds for the true positive rate or the false positive rate. Different ways of setting the borders of the region of interest may result in completely different, even opposing, evaluations. In this paper, we present a method to define a region of interest in a rigorous and objective way, and compute a partial area under the curve that can be used to evaluate the performance of diagnostic tests. The method was originally conceived in the Software Engineering domain to evaluate the performance of methods that estimate the defectiveness of software modules. We compare this method with previous proposals. Our method allows the definition of regions of interest by setting acceptability thresholds on any kind of performance metric, and not just false positive rate and true positive rate: for instance, the region of interest can be determined by imposing that ϕ (also known as the Matthews Correlation Coefficient) is above a given threshold. We also show how to delimit the region of interest corresponding to acceptable costs, whenever the individual cost of false positives and false negatives is known. Finally, we demonstrate the effectiveness of the method by applying it to the Wisconsin Breast Cancer Data. We provide Python and R packages supporting the presented method.
Hierarchical Multilabel Text Classification via Multitask Learning

Hierarchical multilabel classification is a variant of classification where instances might belong to multiple labels and these labels come from a hierarchy. In this paper, we solve the hierarchical multilabel text classification problem of professionally-generated content via multitask learning. More specifically, we focus on (1) how to build models that can share features well in multitask learning, (2) how to incorporate the label dependence into the training procedure of the models, and (3) how to combine the predicted labels of different levels in the hierarchy. To make the experiments simple and comparable, we bring in the state-of-art BERT model as the base model in our work. Experiment results show that the multitask models we build are competitive, the penalty loss we propose is able to improve the performance, and the union operation is the best choice to handle prediction contradiction. In other words, the time cost is reduced but performance is improved via our multitask learning approach.
Hybrid Recommendation Scheme Based on Deep Learning

Big data has been developed for nearly a decade, and the information data on the network is exploding. Facing the complex and massive data, it is difficult for people to get the demanded information quickly, and the recommendation algorithm with its characteristics becomes one of the important methods to solve the massive data overload problem at this stage. In particular, the rise of the e-commerce industry has promoted the development of recommendation algorithms. Traditional, single recommendation algorithms often have problems such as cold start, data sparsity, and long-tail items. The hybrid recommendation algorithms at this stage can effectively avoid some of the drawbacks caused by a single algorithm. To address the current problems, this paper makes up for the shortcomings of a single collaborative model by proposing a hybrid recommendation algorithm based on deep learning IA-CN. The algorithm first uses an integrated strategy to fuse user-based and item-based collaborative filtering algorithms to generalize and classify the output results. Then deeper and more abstract nonlinear interactions between users and items are captured by improved deep learning techniques. Finally, we designed experiments to validate the algorithm. The experiments are compared with the benchmark algorithm on (Amazon item rating dataset), and the results show that the IA-CN algorithm proposed in this paper has better performance in rating prediction on the test dataset.
A Dynamically Weighted Multi-Objective Optimization Approach to Positional Interactions in Remote-Local Augmented/Mixed Reality

With the rise in remote work culture and increased computing capabilities of head-mounted displays (HMDs), more immersive, collaborative experiences are desired in remote-local mixed/augmented (MR/AR) reality. Photorealistic full-body avatar representations of users in remote workspace interactions have shown to have increased social presence, non-verbal behavior, and engagement. However, a direct mapping of the body pose angles from local to the remote workspace will, in most cases, result in positional errors during human-object interaction, caused by the dissimilarity between remote and local workspaces. Hence the interaction must be retargeted, but it should be retargeted in such a way that the original intent of the body pose should be preserved. However, these two objectives sometimes contradict each other. As a result, a multi-objective optimization (MO) problem can be formulated where the primary objective is to minimize positional errors and the secondary objective is to preserve the original interaction body pose. The current state-of-the-art solution uses an evolutionary computation-based inverse kinematic (IK) approach to solve the MO problem where the weights between the objectives must be set by the user based on trial and error, leading to a suboptimal solution. In this paper, we present a new dynamic weight allocation approach to this problem, where a user has the flexibility to set a chosen minimum error tolerance, and the weights will be distributed between the objectives based on a dynamic allocation algorithm. We have tested the adaptability and robustness of this mechanism on motion captured human animations of varying levels of speeds, error tolerances, and redirections. Compared to the static weighting, the dynamic weighted mechanism shows a net (primary + secondary objective) decrease in error ranging from 20.5 % to 34.4% across varying animation speeds and redirections resulting in decreased positional errors and better pose preservation across interactions.
Corpus Design and Automatic Speech Recognition for Deaf and Hard-of-Hearing People

This study describes automatic speech recognition (ASR) for the deaf and hard-of-hearing people. In the relevant literature, ASR for the deaf has been studied in a manner similar to the recognition of speech by people with dysarthria. However, prior studies have been conducted over a small number of deaf speakers. Therefore, to date, it remains unclear how the performance of ASR varies with different speakers. We conducted phoneme recognition experiments using speech from 12 deaf students to obtain analytical results from the perspective of ASR performance.
Identifying nonconformities in contributions to programming projects: from an engagement perspective in improving code quality

Project-based learning is among the most common learning approaches aimed at conveying professional standards and best practices to students in programming courses. However, team projects commonly impose problems related to responsibility sharing, such as low effort or inequality in contributions. This paper presents a collaborative programming assessment system featuring a code quality assessment function with specific metrics to measure individual contributions. Student engagement data is used to detect nonconformities in collaboration using a learning analytical approach. Latent profile analysis was used to detect four theoretical team profiles differentiated by team effort (2 levels) and within-team collaboration (2 levels). We demonstrated the efficacy of assessing code to evaluate team dynamics and student behaviour, wherein efforts to resolve coding style failures can be used as a proxy by which to estimate the taskwork awareness of team members. Submission data from 146 students in 41 web-programming projects revealed four behavioural patterns that could potentially hinder the effective functioning of programming teams: free-riding, social loafing, the bystander effect, and lone wolves. We also demonstrated the applicability of automated programming assessment systems to the monitoring of learning progress, thereby facilitating timely interventions to correct difficulties at the team level.
ORIC: A Self-Adjusting Blockchain Protocol with High Throughput

This paper presents a simple and self-adjusting blockchain protocol called ORIC to make full use of the available bandwidth as far as possible. ORIC follows the Nakamoto protocol and allows many more blocks to be produced during the block-time so that the bandwidth can be exploited to its maximum extent. Recorded blocks are organized into a Directed Acyclic Graph(DAG) sequence from which a transaction total order can be deterministically derived as the blockchain ledger. We give a theoretical analysis of ORIC&#x2019;s properties, such as security and liveness, and evaluate its performance on a distributed system simulator with 1000 full nodes designed. The simulation shows that ORIC can achieve bandwidth utilization close to 50% while confirming transactions in 5-7 minutes under the bandwidth of 20Mbps available. More importantly, our protocol can achieve an optimal throughput by a self-adjusting mechanism when the available bandwidth changes in a peer-to-peer network while hardly affecting security.
Generalizable and Interpretable Deep Learning for Network Congestion Prediction

While recent years have witnessed a steady trend of applying Deep Learning (DL) to networking systems, most of the underlying Deep Neural Networks (DNNs) suffer two major limitations. First, they fail to generalize to topologies unseen during training. This lack of generalizability hampers the ability of the DNNs to make good decisions every time the topology of the networking system changes. Second, existing DNNs commonly operate as "blackboxes" that are difficult to interpret by network operators, and hinder their deployment in practice. In this paper, we propose to rely on a recently developed family of graph-based DNNs to address the aforementioned limitations. More specifically, we focus on a network congestion prediction application and apply Graph Attention (GAT) models to make congestion predictions per link using the graph topology and time series of link loads as inputs. Evaluations on three real backbone networks demonstrate the benefits of our proposed approach in terms of prediction accuracy, generalizability, and interpretability.
Implementation of Human in The Loop on the TurtleBot using Reinforced Learning methods and Robot Operating System (ROS)

In this paper, an implementation of a human in the loop (HITL) technique for robot navigation in an indoor environment is described. The HITL technique is integrated into the reinforcement learning algorithms for mobile robot navigation. Reinforcement algorithms, specifically Q-learning and SARSA, are used combined with HITL since these algorithms are good in exploration and navigation. Turtlebot3 has been used as the robot for validating the algorithms by implementing the system using Robot Operating System and Gazebo. The robot-assisted with human feedback was found to be better in navigation task execution when compared to standard algorithms without using human in the loop. This is a work in progress and the next step of this research is exploring other reinforced learning methods and implementing them on a physical robot.
Artificial Neural Network Based Forecasting of Power Under Real Time Monitoring Environment

While photovoltaic (PV) has grown in popularity as a viable alternative to traditional energy sources in recent decades, it still has to improve in some areas to become the preferred energy source. The degradation of output power owing to soiling is one of the areas that require attention as it results in inefficient operation. The performance of clean and soiled solar panels is investigated in this work. The prediction algorithm is designed to forecast the output power of a solar panel based on the input parameters. Forecasting PV power is essential as it reduces uncertainty and assists in developing an effective PV technology. This work utilized three phases of development and validation to achieve these goals. Firstly, a data acquisition system to collect solar panel parameter data is designed, built, data stored on the ThingsSentral™ cloud platform. Secondly, a prediction algorithm based on machine learning ANN is created to estimate the output power of a solar panel, and thirdly the algorithm is simulated and tested. Measurements from two solar panels were used to test and analyze the performance of the proposed technique. Clean solar panels have an RMSE of 1.328, while dusty solar panels have an RMSE of 1.272, indicating this system can reliably forecast based on the training and test datasets provided. Both solar panel conditions have an R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> of 0.999, indicating that the solar panel dataset used in this project precisely matches the ANN model and accuracy. In conclusion, the data acquisition system and prediction algorithm employed in this work successfully met the project objectives based on the results.