Reviewerly/exHarmony huggingface dataset:
DiFNet: Densely High-Frequency Convolutional Neural Networks

Deep convolutional neural networks have achieved great success in many computer vision tasks. However, they can be attacked by adversarial examples which are input-data with small intentional feature perturbations to fool machine learning models. This vulnerability to adversarial examples poses a potential threat to their widespread application, especially in security-sensitive scenarios. In this paper, we revisit adversarial examples in the frequency domain referring to the computational theory of edge detection, and propose a novel <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">densely high-frequency convolution neural network</i> ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DiFNet</i> ) to effectively defend against adversarial attacks. DiFNet introduces classical edge detection operations into the network structure to enhance the detection ability for high-frequency components in the image. It works well to defend against the imperceptible perturbations attacks even without adversarial examples. Experiments demonstrate that DiFNet outperforms handcraft-designed CNNs in terms of prediction accuracy with improved robustness against state-of-the-art adversarial attacks (FGSM, PGD, etc.).
The Impact of Quality of Experience of Chinese College Students on Internet-Based Resources English Learning

Since Internet-based resources provides various and practical forms of English learning materials, Internet-based resources English learning is a common way for the younger generation. However, not like adult learning, university students need stronger motivation to learn English from Internet-based resources. This study surveyed Chinese college students in Central China to reveal the relationship between cultural intelligence, hedonic motivation, English self-efficacy, online experience quality, and willingness to continue learning online English. Using online media platforms and convenient sampling methods, a total of 385 questionnaires were collected. The data analysis was divided into three phases, descriptive analysis, measurement model evaluation, and structural equation model examination. The results showed Internet quality of experience significantly impacted English continuous learning intention. Cultural intelligence, English self-efficacy, and hedonic motivation all influenced significantly on Internet quality of experience and hedonic motivation had the strongest impact. In addition, the mediation effects of Internet quality of experience to these three factors and Internet-based resources English continuous learning intention all existed. Finally, the research results show cultural intelligence, English self-efficacy, and hedonic motivation were all examined significantly impacting Internet quality of experience statistically. English learning hedonic motivation is the most influencing factor. Therefore, English learning material should be attractive, fun, and enjoyable. This is what the teachers should think of and emphasize when to recommend learning material for students.
Automatic Webpage Briefing

We introduce the task of webpage briefing (WB) to provide a summary of a webpage in a hierarchical manner, from the broad topic of the webpage, to finer level key attributes. A straightforward approach for this task is to train a machine learning model for generating topics and extracting key attributes. However, such a model may not perform well on webpages that are from domains not seen in the training data. An ideal model should be able to adapt to unseen domains while preserving knowledge learned from the seen domains. Knowledge distillation (KD) offers a potential solution, in which a teacher pre-trained with specific domains can pass the knowledge to a student, while unseen domains can also be added to increase the robustness of the models. However, existing works usually assume the models have no access to seen domains during distillation and the knowledge on seen domains may be lost. In our setting, we have access to the generated topics, which contain representative knowledge of seen domains and can help preserve that knowledge during distillation. Moreover, a vanilla KD does not pass on the knowledge about the location patterns of the informative contents in webpages, which are essential for identifying the topics to be generated or the key attributes to be extracted. To preserve more knowledge of seen domains and to better utilize the location patterns, we propose a Dual Distillation model which consists of identification distillation (ID) and understanding distillation (UD); ID distills knowledge on the identification of informative contents under the guidance of the learned topics of seen domains, while UD distills knowledge on topic generation or key attribute extraction. Since topics and key attributes are distilled separately in two students in Dual Distillation, the inherent correlations between them are not utilized. To better exploit such correlations, we propose a Triple Distillation model which consists of a shared ID and two UDs, one for topic generation and the other for key attribute extraction. We further propose a joint model for WB with signal enhancement and exchange among a key attribute extractor, a topic generator, and an informative section predictor. Experiments on real-world webpages show that our models achieve high performances for WB, and validate the superiority of Dual Distillation and Triple Distillation in their target settings. Experiments also show that the proposed joint model outperforms single-task baselines and other joint models.
Kwame: A Bilingual AI Teaching Assistant for Online SuaCode Courses

Introductory hands-on courses such as our smartphone-based coding course, SuaCode require a lot of support for students to accomplish learning goals. Online environments make it even more difficult to get assistance especially more recently because of COVID-19. Given the multilingual context of SuaCode students—learners across 42 African countries that are mostly Anglophone or Francophone—in this work, we developed a bilingual Artificial Intelligence (AI) Teaching Assistant (TA)—Kwame—that provides answers to students’ coding questions from SuaCode courses in English and French. Kwame is a Sentence-BERT (SBERT)-based question-answering (QA) system that we trained and evaluated offline using question-answer pairs created from the course’s quizzes, lesson notes and students’ questions in past cohorts. Kwame finds the paragraph most semantically similar to the question via cosine similarity. We compared the system with TF-IDF and Universal Sentence Encoder. Our results showed that fine-tuning on the course data and returning the top 3 and 5 answers improved the accuracy results. Kwame will make it easy for students to get quick and accurate answers to questions in SuaCode courses.
Price dynamics of cryptocurrencies in parallel markets: evidence from Bitcoin exchanges in Brazil

Purpose This paper aims to study the dynamics of bitcoin prices in Brazil, a large emerging economy with an unregulated bitcoin market. Design/methodology/approach First, this study tests if the Law of One Price (LOOP) is valid for bitcoin prices in Brazil, conducting tests with data from three Brazilian exchanges. Next, this study documents bitcoin price dynamics in the short run by studying the price discovery mechanism in these exchanges. This study uses Information Share and Component Share, combining the two measures to obtain an Information Leadership Share (ILS) measure. Findings This study finds a common trend within bitcoin prices among a set of exchanges, with cointegration tests between the price series indicating that LOOP is valid in Brazilian markets in the long run. ILS indicated that, for closing prices, the most liquid exchange (Foxbit) leads discovery, whereas the least liquid (Local Bitcoin) lags, with Mercado Bitcoin in the middle both in terms of discovery and liquidity. Finally, this study provides evidence that the price variation in the market that leads price discovery can be used to construct an arbitrage in another exchange. Originality/value This research brings the first evidence of a price discovery mechanism for exchanges in Brazilian Reais. Although LOOP is valid in the long run, price leadership in bitcoin markets potentially create arbitrage opportunities in the short run. This study contributes to the growing literature of bitcoin prices with novel evidence from a large emerging economy.
Machine learning vulnerability in medical imaging

Recently, there has been increased interest in applying computer vision methodologies in medical imaging, mainly due to the outstanding performance of deep learning. However, evolution in computer vision systems has led to some matters of security. Adversarial computer vision and/or machine learning is the field that researches these matters, producing either adversarial attack proposals or defensive strategies and techniques against them. This chapter aims at capturing the status of medical computer vision threats and the defensive techniques that are being proposed by researchers. This chapter intends to shed light on the vulnerability of machine learning models in medical image analysis, e.g., disease diagnosis, and to serve as a guide for any researcher working in the field of medical image analysis towards the development of more secure machine learning-based computer-aided diagnosis systems.
Methodological analysis of the personnel training pedagogical space in the agro-industrial complex

The agro-industrial complex is undergoing changes, experiencing personnel shortage. The article presents the experience of analyzing the pedagogical space state of agricultural personnel training using the general scientific concept "space". The concept "pedagogical space" is based on the concept "teacher". The whole society as a self-developing and self-learning system is "pedagogical space" in which in the modern world there is a process associated with the formation of spiritual and moral values, principles and professional qualities of the individual. The social space of the agro-industrial complex coincides with the pedagogical space of agricultural personnel training. In the article the practice of functioning of agro-industrial complex in Russia is discussed, which reveals the problems of workers: a personnel shortage, training programs backlog from requirements to the specialists competence, the reluctance of those, who received and are receiving agricultural education to work in rural areas. The authors draw attention to the fact that the agro-industrial complex problems are always associated with more general society problems. Their analysis, in turn, entails the need to refer to the society as a whole, which leads to the consideration of other society subsystems. Referring to the concept "space" allows to enhance the effect of analyzing the problem.
Data Mining Based College English Teaching Assistant Expert System

To make up for the deficiency of CAI System in practical application, this paper presents a solution to English teaching by data mining algorithm and expert system. We first introduce the principle and application of expert system and ICAI. According to the actual needs of college English teaching, the basic model and structure of WBIETS system are designed. Then, the knowledge base and inference engine of the teaching expert system are discussed in detail from two levels of logical structure and physical structure, and the optimized Apriori algorithm is used to analyze the correlation of knowledge points. Finally, through Web Expert System and ASP.NET the scheme is run and tested. The results show that the system has good expansibility, portability and transparency. It enables students to practice and realize self diagnosis and provides effective help for college English teaching.
An Access Control Scheme Supporting Privacy Protection Based on Blockchain and Attribute

Abstract With the large-scale application of cloud storage systems, lots of attribute-based access control (ABAC) schemes have been introduced to protect data and user security in this insecure environment. ABAC can make data owners control their own data and protect data security and privacy. However, there are two serious privacy leakage problems, namely user attribute privacy and policy privacy, should be solved in the process of constructing ABAC schemes. In this paper, an ABAC scheme supporting privacy protection is constructed, which can solve privacy leakage problems of user attribute privacy and policy privacy in large universe attribute system. Furthermore, a framework is proposed. The framework combines our ABAC scheme, the Ethereum blockchain and blockchain-based storage system. In this framework, the security characteristics of blockchain technology are used to realize decentralization, tamper-resistant and avoiding single point of failure. Besides, the problems of attribute revocation and policy updating are solved by smart contract on the Ethereum blockchain. Finally, we established an initial implementation on Linux and Rinkeby test network, and the experimental results show that our scheme is feasible.
Lighthouses: A Warning System for Super-Spreader Events

Super-spreader events have been a driving force of the COVID-19 pandemic. Such events often take place indoors when many people come together. Various systems for automated contact tracing (ACT) have been proposed which warn users if they have come near an infected person. These generally fail to detect potential super-spreader events as only users who have come in close contact with the infected person, but not others who also visited the same location, are warned. Other ACT approaches allow users to check into locations, but this requires user interaction. We propose two designs how broadcast-based ACT systems can be enhanced by using location-specific information without the need for GPS traces or scanning of QR codes. This makes it possible to alert attendees of a potential super-spreader event while providing privacy. Our idea relies on cooperating "lighthouses" which cover a large area and send out pseudonyms. In our passive design the health authority (HA) publishes location pseudonyms collected by infected users. In the active design, lighthouses communicate with HAs. After retrospectively detecting an infected visitor the lighthouse notifies the HA which users' stay overlapped.
Security Risk Analysis of Multi-Stage Attacks based on Data Criticality

In recent years, it has become more challenging for organizations to assess the security risks of their assets properly, as more vulnerabilities are discovered, exploited, and weaponized. Further, attackers usually use complex multi-stage attack strategies to compromise a system and achieve their goals by exploiting several vulnerabilities. The number of affected assets and the strategy used to create the compromises by the threat actor will often dictate the costs and damages to the organization. When performing risk analysis, in addition to existing vulnerabilities, it is important, but often neglected, to consider the criticality of the data residing in the vulnerable asset. However, graphical threat modeling techniques often do not offer suitable tools for this type of analysis. In this paper, we propose a class of security risk metrics to estimate the cost of an attack that considers the criticality of data in addition to the dependencies among vulnerabilities. Our metrics are based on graphical modeling techniques in which we incorporate data criticality. We applied our approach to a real-life case study and obtained promising results.
VIAM-USV2000: An Unmanned Surface Vessel with Novel Autonomous Capabilities in Confined Riverine Environments

Unmanned Surface Vessels (USVs) have witnessed an increasing growth in demand for development due to their compactness, mobility and maneuverability, which make them well-suited for environmental monitoring on narrow water in Vietnam in particular and in general at several similar tropical regions. However, current surface vessels are limited to operation on open water only. In this paper, we design a USV, namely, VIAM-USV2000, equipped with advanced autonomous capabilities to satisfactorily carry out missions in confined riverine environments. More specifically, our prototype is designed to follow a smooth B-Spline path that is self-planned to meet the limiting curvature and avoid static obstacles. Moreover, the vessel is capable of avoiding dynamic obstacles by an advanced Set-based Guidance mechanism. Simulated and experimental results at a local lake prove the effectiveness of the proposed capabilities, thereby paving the way for the extensive deployment of USVs in many real-world applications.
Legibility and readability in Augmented Reality

Digital technology offers multimodal presentation of information, that can be used for translating foreign languages or for alleviating hearing impairment or deafness communication problems. Today, there exists various aids that can be used for speech-to text translations, but there are some challenges with these. One potential solution for this is to make use of a combination of Augmented Reality (AR) and speech-to-text systems, where speech is converted into text that is then presented in AR-glasses. In AR, one crucial problem is the legibility and readability of text under different environmental conditions. Different types of AR glasses have different ergonomic characteristics, which implies that certain glasses might be more suitable for such a system than others. In this investigation, two different AR-glasses were evaluated based on among other things their optical, visual, and ergonomic characteristics. User tests were conducted to evaluate the legibility and readability of text under different environmental contexts. The results indicate that legibility and readability are affected by factors such as ambient illuminance, background properties and how the text is presented with respect to polarity, opacity, size, and number of lines. The characteristics of the glasses impacts the user experience, but which glasses that is preferred depends on the individual.
Modus Operandi in Fake News : Invited Paper

With the proliferation of different forms of digital communication, the opportunities for spreading Fake News are increasing constantly. More often than not people inform themselves about news and current events in social networks or blogs. By doing so, they run the risk of falling for and also - often unconsciously - spreading false information from manipulative news sites. The distribution of fake news itself is not a new phenomenon, but thanks to the advances in digital communication their circulation accelerates, leading to major impacts for the whole society. This lecture will take a closer look at several aspects. (a) What exactly are fake news? Which legal and content-related criteria must be fulfilled in order to identify fake news as such? (b) Modus Operandi: What are the typical methods of committing a crime? Moreover, the typical motives for spreading fake news are going to be examined in more detail.
Gradient-Based Quantification of Epistemic Uncertainty for Deep Object Detectors

The majority of uncertainty quantification methods for deep object detectors are based on the network output, such as sampling strategies like Monte-Carlo dropout or deep ensembles with straight-forward transfers to object detection.Here, we study gradient-based uncertainty features for object detection.We show that they contain information orthogonal to that of common, output-based uncertainty approximation methods.Meta classification and meta regression are used to produce confidence estimates using gradient features and other methods which are applicable to numerous object detection architectures.Our results show that gradient uncertainty itself performs on par with stateof-the-art methods across different detectors and datasets.We find that combined meta classifiers outperform standalone models.This suggests that sampling strategies may be supplemented by gradient-based uncertainty to obtain improved confidences, contributing to the probabilistic reliability of object detectors in down-stream applications.
Introduction to the Special Issue on Explainable Robotic Systems

introduction Open AccessIntroduction to the Special Issue on Explainable Robotic Systems Share on Authors: Maartje M. A. De Graaf Utrecht University Utrecht UniversityView Profile , Anca Dragan University of California, Berkeley University of California, BerkeleyView Profile , Bertram F. Malle Brown University Brown UniversityView Profile , Tom Ziemke Linkoping University Linkoping UniversityView Profile Authors Info & Claims ACM Transactions on Human-Robot InteractionVolume 10Issue 3July 2021 Article No.: 22pp 1–4https://doi.org/10.1145/3461597Online:11 July 2021Publication History 0citation320DownloadsMetricsTotal Citations0Total Downloads320Last 12 Months320Last 6 weeks80 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteView all FormatsPDF
Goal-Aware Robocentric Mapping and Navigation of a Quadrotor Unmanned Aerial Vehicle

This paper proposes an autonomous navigation solution for an unmanned aerial vehicle (UAV) in an unknown and Global Navigation Satellite System (GNNS)-denied environment using a goal-aware robocentric reference frame. A combined Proportional Navigation (PN)-guidance and obstacle avoidance controller is developed to help navigate the UAV to a user-defined goal by successfully avoiding obstacles along its path. An extended Kalman filter is used to estimate the relative orientation of the UAV with respect to the goal and other objects of interest in the environment (called landmarks). This work uses a bearing-only measurement model based on the positions of the goal and landmarks provided by a visual tracker. A detailed two-dimensional observability analysis is performed and the proposed goal-aware solution is shown to provide more information about the system than a general robocentric approach. The problem formulation is corroborated through extensive simulation and hardware results.
Directional Supervised Learning Through Depth Functions: An Application to ECG Waves Analysis

The present work investigates arrhythmias which can be detected from Electrocardiography (ECG) waves. Detecting cardiac arrhythmia helps indeed to prevent sudden and untimely deaths. Here, directional depth-based classifiers are employed to predict the presence or absence of cardiac arrhythmia. A comparison of their performance with respect to the directional Bayes rule is also provided.