Reviewerly/exHarmony huggingface dataset:
Open-Source Drone Programming Course for Distance Engineering Education

This article presents a full course for autonomous aerial robotics inside the RoboticsAcademy framework. This “drone programming” course is open-access and ready-to-use for any teacher/student to teach/learn drone programming with it for free. The students may program diverse drones on their computers without a physical presence in this course. Unmanned aerial vehicles (UAV) applications are essentially practical, as their intelligence resides in the software part. Therefore, the proposed course emphasizes drone programming through practical learning. It comprises a collection of exercises resembling drone applications in real life, such as following a road, visual landing, and people search and rescue, including their corresponding background theory. The course has been successfully taught for five years to students from several university engineering degrees. Some exercises from the course have also been validated in three aerial robotics competitions, including an international one. RoboticsAcademy is also briefly presented in the paper. It is an open framework for distance robotics learning in engineering degrees. It has been designed as a practical complement to the typical online videos of massive open online courses (MOOCs). Its educational contents are built upon robot operating system (ROS) middleware (de facto standard in robot programming), the powerful 3D Gazebo simulator, and the widely used Python programming language. Additionally, RoboticsAcademy is a suitable tool for gamified learning and online robotics competitions, as it includes several competitive exercises and automatic assessment tools.
Exploring Amharic Sentiment Analysis from Social Media Texts: Building Annotation Tools and Classification Models

This paper presents the study of sentiment analysis for Amharic social media texts.As the number of social media users is ever-increasing, social media platforms would like to understand the latent meaning and sentiments of a text to enhance decision-making procedures.However, lowresource languages such as Amharic have received less attention due to several reasons such as lack of well-annotated datasets, unavailability of computing resources, and fewer or no expert researchers in the area.This research addresses three main research questions.We first explore the suitability of existing tools for the sentiment analysis task.Annotation tools are scarce to support large-scale annotation tasks in Amharic.Also, the existing crowdsourcing platforms do not support Amharic text annotation.Hence, we build a social-network-friendly annotation tool called 'ASAB' using the Telegram bot.We collect 9.4k tweets, where each tweet is annotated by three Telegram users.Moreover, we explore the suitability of machine learning approaches for Amharic sentiment analysis.The FLAIR deep learning text classifier, based on network embeddings that are computed from a distributional thesaurus, outperforms other supervised classifiers.We further investigate the challenges in building a sentiment analysis system for Amharic and we found that the widespread usage of sarcasm and figurative speech are the main issues in dealing with the problem.To advance the sentiment analysis research in Amharic and other related lowresource languages, we release the dataset, the annotation tool, source code, and models publicly under a permissive.
Do-Care: A dynamic ontology reasoning based healthcare monitoring system

Healthcare remote monitoring applications dominate the market of new technologies due to their valuable aid to patients, families, and medical staff. They provide ubiquitous remote health services for patients with chronic diseases or specific conditions and can provide ubiquitous communication between patients and caregiver(s). This paper presents an ontology reasoning-based healthcare monitoring system called Do-Care. The proposed system supports the supervision and follow-up of outdoor and indoor patients suffering from chronic diseases. Collected data, from wearable1, nearable2 or usable3 devices forms the instances for entities from the proposed Do-Care ontology used by the reasoner when applying a set of SWRL4 rules to determinate the health situation of a patient as Normal, Abnormal or Wrong. The main contribution in this paper is a modular and dynamic ontology composed of FOAF5, SSN6/SOSA7 and ICNP8 ontologies with a scalable set of inference rules. The proposed rule based methodology is dynamic and adjustable to meet possible changes in the medication market, medical discoveries, and personal users’ profiles. The presented experimental results show the efficiency of the proposed DO-Care system.
Who’s There? Characterizing Interaction in Virtual Classrooms

The Covid-19 crisis changed the educational landscape. In Sweden, as in many other countries, school leaders, teachers, and students faced a completely new situation, as teaching would immediately be conducted remotely. It offered an opportunity to continue teaching in a crisis, while giving rise to new questions and dilemmas. This study aims to explore aspects of interaction in the virtual classroom. The context is four high schools in Sweden. Data includes a teacher survey and ten workshops with teachers and school leaders. The interaction order framework is used as an analytic lens. The results draw a multifaceted picture of interaction that involves both increased and reduced contact with, and control over, the students and their activities. Some students find a place in the virtual classroom that they previously lacked, while others “disappear behind the screen.” Contributions include unpacking the complex role of interaction in the virtual classroom and providing implications for teachers and school leaders.
Sparse discrete linear canonical transform and its applications

The linear canonical transform (LCT) is a powerful tool for non-stationary signals in signal processing. In this paper, we propose a sparse discrete linear canonical transform (SDLCT) algorithm to solve the high sampling rate and large data calculation of non-stationary signals. In detail, we introduce permutation of spectra and window function to ensure the sparsity of the frequency domain. To get rid of the redundant data of the signal and improve the processing efficiency, we compress the high-dimensional signal into the low-dimensional signal by down-sampling. Then the low dimensional signal is mapped to a linear canonical domain. The above processes effectively reduce the amount of data and computation for signal processing. Then, we apply the SDLCT to the pulse compression of the linear frequency modulation (LFM) signal. The comparison between SDLCT and LCT shows that SDLCT is hardly affected by noise, it can accurately obtain the target positions at a low signal-to-noise ratio (SNR). Furthermore, we apply this algorithm to moving target detection for synthetic aperture radar. The comparison between LCT, sparse Fourier transform (SFT), sparse discrete fraction Fourier transform (SDFrFT), and SDLCT shows that SDLCT has stronger signal detection performance and can detect all the signals that affect each other.
Yolo-v4 Deep Learning Model for Medical Face Mask Detection

The flora and fauna is facing a social disaster owing to the fast transfer of (Corona Virus). The disease with COVID-19 is mainly transmitted by respiratory droplets that are inhaled when people smell, talk, hack or sting. Wearing a veil is an incredible, powerful and easy way to prevent 82% of all respiratory infections. In this way, a number of cover protection structures and recognition structures have been put in place to provide compulsory provision for emergency clinics, air terminals, courier transport, sports offices, and retail outlets. Upper and lower tests have shown unusual strengths in specific real-world projects. Some of this may have been highlighted in the article. Ongoing revelations of articles relying on higher and lower reading models have yielded promising results by finding something found in the pictures. This paper focuses on a response to help to support a larger social request and to wear public covers using the revelation of the YOLO c4 object continuously engraved with images. The proposed Yolo v4 learning model has been pre-programmed with good program limitations. The organization ensures fast access that can deliver consistent results without resolving accuracy, or in complex arrangements. The proposed strategy will be divided into three categories: No dress, cover, and no veil. The model has outdone some of the proposed strategies in the past by gaining 99.98% accuracy during the preparation / testing.
Hardware realization of the multiply and accumulate operation on radio-frequency signals with magnetic tunnel junctions

Abstract Artificial neural networks are a valuable tool for radio-frequency (RF) signal classification in many applications, but the digitization of analog signals and the use of general purpose hardware non-optimized for training make the process slow and energetically costly. Recent theoretical work has proposed to use nano-devices called magnetic tunnel junctions, which exhibit intrinsic RF dynamics, to implement in hardware the multiply and accumulate (MAC) operation—a key building block of neural networks—directly using analog RF signals. In this article, we experimentally demonstrate that a magnetic tunnel junction can perform a multiplication of RF powers, with tunable positive and negative synaptic weights. Using two magnetic tunnel junctions connected in series, we demonstrate the MAC operation and use it for classification of RF signals. These results open a path to embedded systems capable of analyzing RF signals with neural networks directly after the antenna, at low power cost and high speed.
Fast Fuzzy Clustering Based on Anchor Graph

Fuzzy clustering is one of the most popular clustering approaches and has attracted considerable attention in many fields. However, high computational cost has become a bottleneck which limits its applications in large-scale problems. Moreover, most fuzzy clustering algorithms are sensitive to noise. To address these issues, a novel fuzzy clustering algorithm, called fast fuzzy clustering based on anchor graph (FFCAG), is proposed. The FFCAG algorithm integrates anchor-based similarity graph construction and membership matrix learning into a unified framework, such that the prior knowledge of anchors can be further utilized to improve clustering performance. Specifically, FFCAG first constructs an anchor-based similarity graph with a parameter-free neighbor assignment strategy. Then, it designs a quadratic programming model to learn the membership matrix of anchors, which is very different from traditional fuzzy clustering algorithms. More importantly, a novel balanced regularization term is introduced into the objective function to produce more accurate clustering results. Finally, we adopt an alternating optimization algorithm with guaranteed convergence to solve the proposed method. Experimental results performed on synthetic and real-world datasets demonstrate the proposed FFCAG can significantly reduce the computational time with comparable, even superior, clustering performance, compared with state-of-the-art algorithms.
Secure Automatic Speaker Verification (SASV) System Through sm-ALTP Features and Asymmetric Bagging

The growing number of voice-enabled devices and applications consider automatic speaker verification (ASV) a fundamental component. However, maximum outreach for ASV in critical domains e.g., financial services and health care, is not possible unless we overcome security breaches caused by voice cloning algorithms and replayed audios. Therefore, to overcome these vulnerabilities, a secure ASV (SASV) system based on the novel sign modified acoustic local ternary pattern (sm-ALTP) features and asymmetric bagging-based classifier-ensemble with enhanced attack vector is presented. The proposed audio representation approach clusters the high and low frequency components in audio frames by normally distributing frequency components against a convex function. Then, the neighborhood statistics are applied to capture the user specific vocal tract information. The proposed SASV system simultaneously verifies the bonafide speakers and detects the voice cloning attack, cloning algorithm used to synthesize cloned audio (in the defined settings), and voice-replay attacks over the ASVspoof 2019 dataset. In addition, the proposed method detects the voice replay and cloned voice replay attacks over the VSDC dataset. Both the voice cloning algorithm detection and cloned-replay attack detection are novel concepts introduced in this paper. The voice cloning algorithm detection module determines the voice cloning algorithm used to generate the fake audios. Whereas, the cloned voice replay attack detection is performed to determine the SASV behavior when audio samples are simultaneously contemplated with cloning and replay artifacts.
Defense-Resistant Backdoor Attacks Against Deep Neural Networks in Outsourced Cloud Environment

The time and monetary costs of training sophisticated deep neural networks are exorbitant, which motivates resource-limited users to outsource the training process to the cloud. Concerning that an untrustworthy cloud service provider may inject backdoors to the returned model, the user can leverage state-of-the-art defense strategies to examine the model. In this paper, we aim to develop robust backdoor attacks (named RobNet) that can evade existing defense strategies from the standpoint of malicious cloud providers. The key rationale is to diversify the triggers and strengthen the model structure so that the backdoor is hard to be detected or removed. To attain this objective, we refine the trigger generation algorithm by selecting the neuron(s) with large weights and activations and then computing the triggers via gradient descent to maximize the value of the selected neuron(s). In stark contrast to existing works that fix the trigger location, we design a multi-location patching method to make the model less sensitive to mild displacement of triggers in real attacks. Furthermore, we extend the attack space by proposing multi-trigger backdoor attacks that can misclassify inputs with different triggers into the same or different target label(s). We evaluate the performance of RobNet on MNIST, GTSRB, and CIFAR-10 datasets, against four representative defense strategies Pruning, NeuralCleanse, Strip, and ABS. The comparison with two state-of-the-art baselines BadNets and Hidden Backdoors demonstrates that RobNet achieves higher attack success rate and is more resistant to potential defenses.
Paragraph-level Simplification of Medical Texts

We consider the problem of learning to simplify medical texts. This is important because most reliable, up-to-date information in biomedicine is dense with jargon and thus practically inaccessible to the lay audience. Furthermore, manual simplification does not scale to the rapidly growing body of biomedical literature, motivating the need for automated approaches. Unfortunately, there are no large-scale resources available for this task. In this work we introduce a new corpus of parallel texts in English comprising technical and lay summaries of all published evidence pertaining to different clinical topics. We then propose a new metric based on likelihood scores from a masked language model pretrained on scientific texts. We show that this automated measure better differentiates between technical and lay summaries than existing heuristics. We introduce and evaluate baseline encoder-decoder Transformer models for simplification and propose a novel augmentation to these in which we explicitly penalize the decoder for producing 'jargon' terms; we find that this yields improvements over baselines in terms of readability.
A Closer Look at Few-Shot Crosslingual Transfer: The Choice of Shots Matters

Few-shot crosslingual transfer has been shown to outperform its zero-shot counterpart with pretrained encoders like multilingual BERT.Despite its growing popularity, little to no attention has been paid to standardizing and analyzing the design of few-shot experiments.In this work, we highlight a fundamental risk posed by this shortcoming, illustrating that the model exhibits a high degree of sensitivity to the selection of few shots.We conduct a largescale experimental study on 40 sets of sampled few shots for six diverse NLP tasks across up to 40 languages.We provide an analysis of success and failure cases of few-shot transfer, which highlights the role of lexical features.Additionally, we show that a straightforward full model finetuning approach is quite effective for few-shot transfer, outperforming several state-of-the-art few-shot approaches.As a step towards standardizing few-shot crosslingual experimental designs, we make our sampled few shots publicly available.
Reinforcement learning-based sensitive semantic location privacy protection for VANETs

Location-based services (LBS) in vehicular ad hoc networks (VANETs) must protect users' privacy and address the threat of the exposure of sensitive locations during LBS requests. Users release not only their geographical but also semantic information of the visited places (e.g., hospital). This sensitive information enables the inference attacker to exploit the users' preferences and life patterns. In this paper we propose a reinforcement learning (RL) based sensitive semantic location privacy protection scheme. This scheme uses the idea of differential privacy to randomize the released vehicle locations and adaptively selects the perturbation policy based on the sensitivity of the semantic location and the attack history. This scheme enables a vehicle to optimize the perturbation policy in terms of the privacy and the quality of service (QoS) loss without being aware of the current inference attack model in a dynamic privacy protection process. To solve the location protection problem with high-dimensional and continuous-valued perturbation policy variables, a deep deterministic policy gradient-based semantic location perturbation scheme (DSLP) is developed. The actor part is used to generate continuous privacy budget and perturbation angle, and the critic part is used to estimate the performance of the policy. Simulations demonstrate the DSLP-based scheme outperforms the benchmark schemes, which increases the privacy, reduces the QoS loss, and increases the utility of the vehicle.
Graph Signal Processing for Geometric Data and Beyond: Theory and Applications

Geometric data acquired from real-world scenes, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g.</i> , 2D depth images, 3D point clouds, and 4D dynamic point clouds, have found a wide range of applications including immersive telepresence, autonomous driving, surveillance, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">etc</i> . Due to irregular sampling patterns of most geometric data, traditional image/video processing methodologies are limited, while Graph Signal Processing (GSP)—a fast-developing field in the signal processing community—enables processing signals that reside on irregular domains and plays a critical role in numerous applications of geometric data from low-level processing to high-level analysis. To further advance the research in this field, we provide the first timely and comprehensive overview of GSP methodologies for geometric data in a unified manner by bridging the connections between geometric data and graphs, among the various geometric data modalities, and with spectral/nodal graph filtering techniques. We also discuss the recently developed Graph Neural Networks (GNNs) and interpret the operation of these networks from the perspective of GSP. We conclude with a brief discussion of open problems and challenges.
Fairness, integrity, and privacy in a scalable blockchain-based federated learning system

Federated machine learning (FL) allows to collectively train models on sensitive data as only the clients’ models and not their training data need to be shared. However, despite the attention that research on FL has drawn, the concept still lacks broad adoption in practice. One of the key reasons is the great challenge to implement FL systems that simultaneously achieve fairness, integrity, and privacy preservation for all participating clients. To contribute to solving this issue, our paper suggests a FL system that incorporates blockchain technology, local differential privacy, and zero-knowledge proofs. Our implementation of a proof-of-concept with multiple linear regressions illustrates that these state-of-the-art technologies can be combined to a FL system that aligns economic incentives, trust, and confidentiality requirements in a scalable and transparent system.
Efficient active contour model for medical image segmentation and correction based on edge and region information

The inhomogeneity of images is always a challenge in the field of image segmentation. Aiming at the problem of segmentation and correction of inhomogeneous images, this paper proposes an improved active contour model based on previous models. Our model inherits the advantage of that, which is robust to images with irregular intensity distribution. Meanwhile, our model can segment and correct images with inhomogeneous intensity at the same time, performing better when segmenting images with inhomogeneous intensity. Moreover, it saves a lot of time because our model has fewer parameters to be tuned. We extend our two-phase model to the multi-phase model and the vector-valued model and prove the convergence of our algorithm. Through a lot of qualitative analysis and quantitative comparison, we find that our model can accurately segment medical images and get homogeneous corrected images. In addition, compared to other models, our model has higher accuracy with fewer iterations.
Ontology Engineering

Ontologies have become increasingly important as the use of knowledge graphs, machine learning, natural language processing (NLP), and the amount of data generated on a daily basis has exploded. As of
Graph convolutional network with multiple weight mechanisms for aspect-based sentiment analysis

Aspect-based sentiment analysis (ABSA) aims at determining the sentiment polarity of the given aspect term in a sentence. Recently, graph convolution network (GCN) has been used in the ABSA task and obtained promising results. Despite the proliferation of the methods and their success, prevailing models based on GCN lack a powerful constraint mechanism for the message passing to aspect terms, introducing heavy noise during graph convolution. Further, they simply average the subword vectors from BERT to form word-level embeddings, failing to fully exploit the potentials of BERT. To overcome these downsides, a graph convolutional network with multiple weight mechanisms is proposed for aspect-based sentiment analysis in the paper. Specifically, a dynamic weight alignment mechanism is proposed to encourage our model to make full use of BERT. Then an aspect-aware weight mechanism is designed to control message propagation to aspect during graph convolution operation. Afterwards, an aspect-oriented loading layer is presented to further reduce adverse effects of words irrelevant with aspect term. Finally, the multi-head self attention is used to fuse high order semantic and syntax information. Hence, the model can obtain the premium aspect-specific representations for prediction. Experiments demonstrate that the proposed model can achieve state-of-the-art results compared to other models.