Reviewerly/exHarmony huggingface dataset:
Development of a Learning-Based Intention Detection Framework for Power-Assisted Manual Wheelchair Users

Pushrim-activated power-assisted wheels (PAPAWs) are assistive technologies that provide on-demand assistance to wheelchair users. PAPAWs operate based on a collaborative control scheme. Therefore, they rely on accurate interpretation of the user’s intent to provide effective propulsion assistance. This paper presents a learning-based approach to predict wheelchair users’ intention when performing a variety of wheelchair activities. We obtained kinematic and kinetic data from manual wheelchair users when performing standard wheelchair activities such as turns and ascents. Our measurements revealed variability in physical capabilities and propulsion habits of different users, therefore, highlighting the need for the development of personalized intention inference models. We used Gaussian Mixture models to label different phases of user-pushrim interactions based on individual user’s wheeling behaviour. Supervised classifiers were trained with each user’s data and these models were used to predict the user’s intentions during different propulsion activities. We found random forest classifiers had high accuracy (>92%) in predicting different states of individual-specific wheelchair propulsion and user intent for 2 participants. This proposed framework is computationally efficient and can be used for real-time prediction of wheelchair users’ intention. The outcome of this clustering-classification pipeline provides relevant information for designing user-specific and adaptive PAPAW controllers.
Improved Density Peaks Clustering Based on Natural Neighbor Expanded Group

Density peaks clustering (DPC) is an advanced clustering technique due to its multiple advantages of efficiently determining cluster centers, fewer arguments, no iterations, no border noise, etc. However, it does suffer from the following defects: (1) difficult to determine a suitable value of its crucial cutoff distance parameter, (2) the local density metric is too simple to find out the proper center(s) of the sparse cluster(s), and (3) it is not robust that parts of prominent density peaks are remotely assigned. This paper proposes improved density peaks clustering based on natural neighbor expanded group (DPC-NNEG). The cores of the proposed algorithm contain two parts: (1) define natural neighbor expanded (NNE) and natural neighbor expanded group (NNEG) and (2) divide all NNEGs into a goal number of sets as the final clustering result, according to the closeness degree of NNEGs. At the same time, the paper provides the measurement of the closeness degree. We compared the state of the art with our proposal in public datasets, including several complex and real datasets. Experiments show the effectiveness and robustness of the proposed algorithm.
Design of a Remote Emotional Requirement Elicitation Feedback Method

The emotions of users are often neglected in requirements engineering, despite their criticality to technology adoption. Furthermore, it is difficult for small organizations with low resource capability to elicit and evaluate user-feedback to requirements when building socio-technical applications. The current pandemic has hampered the ability of face-to-face interaction, presenting further barriers for requirements engineering. Although requirement elicitation and feedback techniques are well-established, there is a gap in the state of the art pertaining to effective methods that integrate both emotionally-driven development of features and the remote capturing of feedback to these features. Using a cancer peer-support prototype as its use-case, this paper presents the Remote Emotional Requirements Elicitation Feedback Method (REREFM) for integrating emotionality into the requirements building process through emotional goal-modelling and applying low-cost or free-to-use multimedia tools for remotely garnering user-feedback. Ultimately, the method emphasizes providing a positive user-experience for interviewees.
Scale-Aware Attention-Based PillarsNet (SAPN) Based 3D Object Detection for Point Cloud

Three-dimensional object detection can provide precise positions of objects, which can be beneficial to many robotics applications, such as self-driving cars, housekeeping robots, and autonomous navigation. In this work, we focus on accurate object detection in 3D point clouds and propose a new detection pipeline called scale-aware attention-based PillarsNet (SAPN). SAPN is a one-stage 3D object detection approach similar to PointPillar. However, SAPN achieves better performance than PointPillar by introducing the following strategies. First, we extract multiresolution pillar-level features from the point clouds to make the detection approach more scale-aware. Second, a spatial-attention mechanism is used to highlight the object activations in the feature maps, which can improve detection performance. Finally, SE-attention is employed to reweight the features fed into the detection head, which performs 3D object detection in a multitask learning manner. Experiments on the KITTI benchmark show that SAPN achieved similar or better performance compared with several state-of-the-art LiDAR-based 3D detection methods. The ablation study reveals the effectiveness of each proposed strategy. Furthermore, strategies used in this work can be embedded easily into other LiDAR-based 3D detection approaches, which improve their detection performance with slight modifications.
Sentiment analysis of preschool teachers’ perceptions on ICT use for young children

Sentiment analysis in gaining more attention as it is increasingly used in multiple domains, including in interpreting educational data. The article uses sentiment analysis technique to understand the early childhood educators reported beliefs (perception) on young children's ICT use. The dataset was obtained from a comparative study of early childhood educators from two countries, Australia and Malaysia. The result shows a similar outcome where most teachers agreed upon the benefits of ICT use and conclude more positive sentiment polarity.This paper summarizes the findings using sentiment analysis as well as comparing it to the quantitative data obtained from the survey.
Why is That a Background Article

News background linking is the problem of finding online resources that can provide valuable context and background information to help the reader comprehend a given news article. While the problem has recently attracted several researchers, however, the notion of background relevance is not well-studied and it requires better understanding to ensure effective system performance. In this paper, we conduct a qualitative analysis on a sample of 25 query news articles and 152 of their corresponding background articles, in addition to 50 of non-background ones. The goal of the study is to shed some light on the relationship between the query articles and the background articles, and provide informative insights for developing more effective background retrieval models. For instance, our analysis shows that event-driven query articles are, on average, harder to process than others, hence they should be handled differently. It also shows that discussing subtopics in detail and adding new informative topics are both essential factors for highly-relevant background articles. Moreover, it shows that a high lexical similarity between a query article and a background one is neither sufficient nor necessary.
AUV Path Planning Based on Improved Genetic Algorithm

The path planning model of AUV is established based on the improved genetic algorithm. In the model, the shortest path length is taken as the main criterion, and the boundary conditions are established with the range of collision avoidance and turning angle as constraints. The reasonable crossover and mutation adaptive probability models are designed. Then, the simulation results show that the effectiveness of AUV path planning is verified by a specific example The algorithm can converge quickly, which proves the correctness and feasibility of the path planning model.
CloudJoin: Experimenting at scale with Hybrid Cloud Computing

To continue innovating in an age of at-scale computer systems research, the academic computing and networking systems research community must explore new approaches to addressing growing researcher demands to support larger size experiments. CloudJoin explores a transformational approach to scaling out successful Computing Research Infrastructures (CRI) into larger testbeds by creating hybrid cloud computing systems. We describe how to create a seamless, scalable, single experiment testbed that spans CloudLab and the Google Cloud Platform (GCP), while requiring no infrastructure changes. In addition to added elastic computing capacity, CloudJoin experiments benefit from easy access specialized hardware and cloud services and APIs to leverage world class data analytics and experimental infrastructure monitoring. In this work-in-progress, we show how to integrate the infrastructures by creating a Virtual Private Network between a CloudLab experiment and a GCP Virtual Private Cloud (VPC). To simplify understanding of large-scale experiment behavior, problem diagnosing and debugging, we also demonstrate how to use scalable, single dashboard cloud monitoring and logging tools across the hybrid testbed infrastructure. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> This material is based upon work supported by the National Science Foundation under Grant No. CNS-1923692
AURORA

Information extraction is a well-known topic that plays a critical role in many NLP applications as its outputs can be considered as an entrance step for digital transformation. However, there still exist gaps when applying research results to actual business cases. This paper introduces AURORA, an information extraction for domain-specific business documents. The intuition of AURORA is to use transfer learning for extraction. To do that, it utilizes the power of transformers for dealing with the limitation of training data in business cases and stacks additional layers for domain adaptation. We demonstrate AURORA in the context of actual scenarios where users are invited to experience two functions: fine-grained and whole paragraph extraction of Japanese business documents. A video of the system is available at http://y2u.be/xHQpYE41Tqw.
Preliminary Investigation on Using Semi-Supervised Contextual Word Sense Disambiguation for Data Augmentation

Recently, neural architectures play a significant role in the task of Word Sense Disambiguation (WSD). Supervised methods seem to be ahead of its rivals and their performance mostly depends on the size of training data. A numerous number of human-annotated data available for WSD task have been constructed for English. However, low-resource languages (LRLs) still face difficulty in finding suitable data resources. Gathering and annotating a sufficient amount of training data is a time-consuming and labor-expensive work. To address and overcome this problem, in this paper we investigate the possibility of using a semi-supervised context based WSD approach for data augmentation (in order to be later used for supervised learning). Since, it is even difficult to find WSD evaluation datasets for LRLs, in this study, we use English datasets to build a proof-of-concept and to evaluate their applicability onto LRLs. Our semi-supervised approach uses a seed set and context embeddings. We test with 9 different context based language models (including ELMo, BERT, RoBERTa etc.) and investigate their impacts on WSD. We increased our baseline results up to 28 percentage point improvements (baseline with ELMo 50.39% and ELMo Sense Seed Based Average Similarity Model 78.06%) in terms of accuracy. Our initial findings reveal that the proposed approach is very promising for the augmentation of WSD datasets of LRLs.
Are You a Favorite Target For Cryptojacking? A Case-Control Study On The Cryptojacking Ecosystem

Illicitly hijacking visitors' computational resources for mining cryptocurrency via compromised websites is a consolidated activity.Previous works mainly focused on large-scale analysis of the cryptojacking ecosystem, technical means to detect browser-based mining as well as economic incentives of cryptojacking. So far, no one has studied if certain technical characteristics of a website can increase (decrease) the likelihood of being compromised for cryptojacking campaigns.In this paper, we propose to address this unanswered question by conducting a case-control study with cryptojacking websites obtained crawling the web using Minesweeper. Our preliminary analysis shows some association for certain website characteristics, however, the results obtained are not statistically significant. Thus, more data must be collected and further analysis must be conducted to obtain a better insight into the impact of these relations.
GENERAL REMARKS ON BASIC ACTUALITIES WITHIN OUR LIFE AND EDUCATION DURING FIRST 20 YEARS OF 21st CENTURY

Development of Human personal as well as social Life means continuing physical and spiritual (mental) changes all around them and within them. Corresponding changes traditionally reflected by humans as evolution (reformational) or revolution (transformational) processes, that are realizing corresponding quantitative or qualitative steps within the development of people’s Life Activities. First 20 years of 21-st Century presented a real start of serious qualitative changes as a process of implementation up to date achievements of modern Science and Technologies. These are transformational changes not only within physical background of our traditional Life, but fast developing of corresponding changes also within our spiritual (or mental) Life (Broks, 2016). The central phenomenon within the mentioned processes above today is the development of overall computerization of physical as well as mental Life Activities of Humans. Global, regional, local explosion of information as an artefact of revolution within Computer Science and Information Technology marks the above mentioned beginning of the 21-st century. So, our life today is becoming more and more complex by means of increasing diversity and speed of corresponding changes. Virus COVID-19 attack has just only accelerated these changes. Global turnover of Biological as well as Computer viruses is part of modern Life today for tomorrow.
A Densely Connected Encoder Stack Approach for Multi-type Legal Machine Reading Comprehension

Legal machine reading comprehension (MRC) is becoming increasingly important as the number of legal documents rapidly grows. Currently, the main approach of MRC is the deep neural network based model which learns multi-level semantic information with different granularities layer by layer, and it converts the original data from shallow features into abstract features. Owing to excessive abstract semantic features learned by the model at the top of layers and the large loss of shallow features, the current approach still can be strengthened when applying to the legal field. In order to solve the problem, this paper proposes a Densely Connected Encoder Stack Approach for Multi-type Legal MRC. It can easily get multi-scale semantic features. A novel loss function named multi-type loss is designed to enhance the legal MRC performance. In addition, our approach includes a bidirectional recurrent convolutional layer to learn local features and assist in answering general questions. And several fully connected layers are used to keep position features and make predictions. Both extensive experiments and ablation studies in the biggest Chinese legal dataset demonstrate the effectiveness of our approach. Finally, our approach achieves 0.817 in terms of F1 in CJRC dataset and 83.4 in the SQuAD2.0 dev.
Role Switching in Task-Oriented Multimodal Human-Robot Collaboration

In a collaborative task and the interaction that accompanies it, the participants often take on distinct roles, and dynamically switch the roles as the task requires. A domestic assistive robot thus needs to have similar capabilities. Using our previously proposed Multimodal Interaction Manager (MIM) framework, this paper investigates how role switching for a robot can be implemented. It identifies a set of primitive subtasks that encode common interaction patterns observed in our data corpus and that can be used to easily construct complex task models. It also describes an implementation on the NAO robot that, together with our original work, demonstrates that the robot can take on different roles. We provide a detailed analysis of the performance of the system and discuss the challenges that arise when switching roles in human-robot interactions.
Delay‐aware privacy‐preserving location‐based services under spatiotemporal constraints

Summary The ubiquitous use of location‐based services (LBS) through smart devices produces massive amounts of location data. An attacker, with an access to such data, can reveal sensitive information about users. In this paper, we study location inference attacks based on the probability distribution of historical location data, travel time information between locations using knowledge of a map, and short and long‐term observation of privacy‐preserving queries. We show that existing privacy‐preserving approaches are vulnerable to such attacks. In this context, we propose a novel location privacy‐preserving approach, called KLAP, based on the three fundamental obfuscation requirements: minimum k ‐locations, l ‐diversity, and privacy a rea p reservation. KLAP adopts a personalized privacy preference for sporadic, frequent, and continuous LBS use cases. Specifically, it generates a secure concealing region (CR) to obfuscate the user's location and directs that CR to the service provider. The main contribution of this work is twofold. First, a CR pruning technique is devised to establish a balance between privacy and delay in LBS usage. Second, a new attack model called a long‐term obfuscated location tracking attack, and its countermeasure is proposed and evaluated both theoretically and empirically. We assess KLAP with two real‐world datasets. Experimental results show that it can achieve better privacy, reduced delay, and lower communication costs than existing state‐of‐the‐art methods.
Positioning-Based Domain-Specific Modelling through Mobile Devices

Modelling is a central activity in many disciplines. It is typically performed with the support of modelling tools that run on desktop computers or laptops, i.e., in static settings. How-ever, some modelling scenarios require a faithful representation of the position of the model elements in the physical world. Such scenarios would benefit from the ability to model in mobility and exploit the data obtained from the sensors embedded in mobile devices. For this purpose, we propose a conceptual approach to positioning-based modelling based on the combination of a physical dimension (as provided by the sensors of mobile devices) and an ontological one (as provided by domain meta-models). We showcase different scenarios for these ideas, and present a prototype app - called METAPHORE - that runs on iOS devices and realizes these concepts.
A probability density function model describing height estimation uncertainty due to image pixel intensity noise in digital fringe projection measurements

Digital fringe projection is a surface-profiling technique used for highly accurate non-contact measurements. As with any measurement technique, a variety of sources degrade to the measurement accuracy of the method. This paper presents an analytically-derived probability density function that explicitly models the surface height measurement error due to inevitable phase measurement error, and it includes the specific case of pixel noise inducing the phase measurement error that ultimately leads to the height estimation error. The accuracy of the model was validated through Monte-Carlo simulations of resultant height distributions subject to arbitrarily correlated pixel intensity noise and experimental digital fringe projection measurements where the pixel-by-pixel height uncertainty estimations were compared to the predictions of the derived model.
Entity Recognition of Chinese Medical Literature Based on BiLSTM-CRF and Fusion Features

The entity recognition status of Chinese medical literature is introduced in the paper. On the foundation, we propose a method based on fusion-feature to solve problems of medical named entity recognition. Then, the BiLSTM-CRF model is constructed by combining the Conditional Random Field (CRF) of Bidirectional Long Short-Term Memory (BiLSTM) network. It is used to obtain long-distance context information and label entity types. We use Word2vec to build the embedding layer. The characters and words in the sentence are converted into dense vectors with fusing external dictionary features. The results show that compared with using traditional CRF model, the method based on fusion-feature BiLSTM-CRF has better effect, and the F-measure is increased by 7.51%.
Multi-Criteria Scheduling of Complex Workloads on Distributed Resources

The emergence of large-scale distributed platforms, such as cloud and fog environments, as well as the increasing complexity of the workloads processed on such resources, have made imperative the utilization of effective scheduling techniques. In this paper, we study and analyze multi-criteria scheduling of complex workloads on distributed resources. The workloads consist of multiple-task applications with different characteristics. Simulation is employed in order to evaluate the system performance. The simulation results reveal that the performance of the utilized scheduling techniques is depended on the employed routing strategies and the variability of the processor service times.
Hierarchical Intelligent-Geometric Control Architecture for Unmanned Aerial Vehicles Operating in Uncertain Environments

This paper considers the cutting-edge scientific problem of controlling unmanned aerial vehicles (UAVs) in unstable conditions based on intelligent-geometric theory that combines geometric control methods (methods of optimal control, complex motion control and stabilization, trajectory tracking, differential pursuit-evasion games, etc.) with intelligent control methods using tools of artificial intelligence (productions, semantic networks, fuzzy logic, frame-based behavioral microprograms and operations, methods of knowledge acquisition, etc.). Such integration provides reliable and high-performance control techniques for operating in uncertain environments under wind disturbances. Hierarchical architecture of intelligent-geometric control system is proposed, designed for joint application of precise geometric and adaptive intelligent control methods as parts of a single robotic system. In accordance with the proposed architecture, the solution to the problem of controlling a UAV group taking into account mathematical models of an aircraft and wind loads was simulated in MATLAB Simulink system.