Reviewerly/exHarmony huggingface dataset:
SPD Data Dimensionality Reduction based on SPD Manifold Tangent Space and Local LDA

The bilinear transformation of symmetric positive definite (SPD) matrix is currently the most common SPD data dimensionality reduction framework. The transformation matrix can be determined according to different criteria to produce different SPD data dimensionality reduction algorithms. The bilinear transformation of SPD matrix is essentially a transformation between two Riemannian manifolds. Since Riemannian manifolds are not linear spaces, most SPD data bilinear transformation dimensionality reduction algorithms now avoid linear operations between SPD matrices. This paper proposes a SPD data dimensionality reduction algorithm based on SPD manifold tangent space and local LDA. The main contributions are as follows: (1) This paper adopts the affine invariant Riemann metric, the tangent space of the unit matrix and the log transformation to transform the SPD The data is transformed from the Riemannian manifold to the unit matrix tangent space, so that the geodesic distance between the SPD and the unit matrix and the Euclidean distance transformation between the tangent vector and the origin of the tangent space after the transformation remain unchanged; (2) The so-called local discriminant difference of SPD is to first The SPD data is decomposed into one part, and then the local discriminant difference of the SPD data is calculated according to the category (label) of the SPD data. The local discriminant difference of SPD data better realizes the combination of internal and external attributes of SPD data.
Evaluating User Experience in Human-Data Interaction with Information Visualization

It is necessary to further understand the reasons that lead users to prefer one system to another to develop successful solutions. The preference may be the result of aspects ranging from functionality and usability to aspects such as emotions aroused, pleasure, and aesthetics. Providing a good User Experience (UX) is the goal of several user-centered design activities. Assessing the UX of applications based on interaction with data is challenging due to the diversity of parameters involved. It requires evaluating the experience offered by the system, involved data, and the analysis process supported. Existing UX measurement tools do not take all of these aspects into account. This article proposes a UX evaluation instrument for applications based on Human-data Interaction (HDI). We adapted a general-purpose questionnaire to support evaluating HDI-specific aspects of UX. Six prototypes were evaluated with the proposed instrument and the results obtained were close to the global attractiveness perceived by participants and their preferences.
Sunlight Glinting on Clouds: Deception and Autonomous Weapons Systems

Abstract The art of deception has played a significant role in military conflict for centuries and has been discussed extensively. Yet there has been an absence in the literature of any scrutiny of the risks posed by the deception of Autonomous Weapons Systems (AWS). After explaining the nature of AWS, we overview reasons given in their favour and arguments against them. Examples of military deceptive strategies are considered, together with reflections on the nature of deception. The core of the paper is a technical examination of some of the ways that AWS could be deceived and the potential humanitarian consequences. Since AWS have, by definition, an absence of meaningful human control, any deception could remain hidden until too late. We conclude that awareness of the vulnerability of sensing and image processing systems of AWS to deception reinforces and strengthens the case against their development and use.
DataPoste: a mobile application based on Understandable Deep Learning to characterise electrical equipment in secondary distribution substation

Gathering electrical reference information of equipment is a time-consuming task done by technicians on site. To make the task easier, we develop DataPoste, a mobile application performing image recognition with deep learning, to provide this information from a taken picture. Due to its black-box nature, we wanted to ensure the model performance in real-world condition and provide trust and acceptance for the domain experts. To do so, we investigate explainable artificial intelligence methods to better understand the model decision-making logic. Accordingly, we identified misbehaviour from the model and corrected it. Providing these results also help the domain experts to trust the model.
Information Flow Optimization for Practice of University Mathematics Interaction Platform Construction

Information flow optimization for the practice of the university mathematics interaction platform construction is studied in this paper. To construct the efficient model, data access signals are sent to the two systems on the same client, and both systems receive the access request at the same time and apply relevant methods to process the data access request. The WAN information access control system based on the information flow model is a core public system, and the operator needs to have an independent module to operate the WAN information access control. With these ideas, the platform is conducted for the implementations. The experimental results show the efficiency of the designed model.
Research of Artificial Intelligence Computer Vision Application

With the continuous development of Internet technology, Internet users are no longer satisfied with obtaining information only through text. More and more users like to take photos or screenshots through smart phones to save and share their life information. Subsequently, using computer to recognize and process image information becomes more and more important. Image recognition technology is an important research field of artificial intelligence. This technology can enable computers to process a large amount of information instead of human beings, so as to free human beings from cumbersome workload. Image recognition technology [3-5] meets the relevant needs of actual users through the steps of relevant information acquisition, preprocessing and model training. Image recognition technology first defines the characteristics of related objects, then memory image matching, and finally image recognition. For the definition of the features of related objects, we can not only use the predefined features, but also train the related features through convolutional neural network.
Multi-objective optimization for fuzzy workflow scheduling

A fuzzy workflow scheduling problem is investigated with fuzzy temporal parameters, such as the fuzzy task processing times, fuzzy data transmission times and the fuzzy due dates. In the considered problem, the resources are elastic cloud resources with multiple price structures. Due to the fuzzy temporal parameters, the deadline constraint cannot be ensured. Therefore, we formulate a triangle fuzzy number-based workflow scheduling problem model with the soft deadline constraint. A greedy local search method is proposed. The objectives are minimizing the total rental cost and the average dissatisfaction degree. The Pareto-based metrics are introduced to evaluate the performance of the compared algorithms. Experimental results show that the proposal is more effective and robust than the two existing algorithms.
Analyzing online programming communities to enhance visualization languages

column Share on Analyzing online programming communities to enhance visualization languages Author: Leilani Battle University of Washington University of WashingtonView Profile Authors Info & Claims InteractionsVolume 29Issue 1January - February 2022 pp 27–29https://doi.org/10.1145/3503490Online:06 January 2022Publication History 0citation2,376DownloadsMetricsTotal Citations0Total Downloads2,376Last 12 Months2,376Last 6 weeks37 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 SiteGet Access
Revdbscan and Flexscan—$$O(n\log n)$$ Clustering Algorithms

The goal of this paper is to present two new algorithms conceptually close to density-based clustering. Both algorithms deal with problems no worse than the dbscan algorithm, and additionally, flexscan deals with nonuniform distributions of data. The complexity of both algorithms is \(O(n \log n)\) in contrary to the well-known dbscan algorithm which complexity is \(O(n^2)\). Additionally, we show that the complexity of dbscan cannot be reduced to \(O(n \log n)\) just by using locality sensitive hashing trees (or either r-trees or kd-trees).In the final part of the paper, we present results on benchmark datasets. Results clearly show the superiority of the proposed algorithms.
SoccerKicks: a Dataset of 3D dead ball kicks reference movements for humanoid robots

The possibility of robots imitating reference movements performed by experts recently emerged in the Machine Learning context. Based on Deep Reinforcement Learning (DRL), this process focuses on observing a reference movement policy and its adaptation to a robot with a similar body scheme. In the humanoid robots domain, the massive availability of videos on the internet holds the potential to provide reference movements for virtually any task performed by humans. However, 3D pose estimation algorithms based on videos are currently subject to failure due to several practical situations (poor image framing, low video quality, joints occlusions and mismatch, and so on) and typically require applying a complex methodology. This paper presents SoccerKicks, a new dataset that provides 3D reference movements of humans performing dead ball kicks (penalty and foul) obtained from reference videos suitable for use in the robotics soccer domain. In this work we describe: i) the methodology adopted for the videos selection; ii) the algorithms chosen to perform the 2D and 3D pose estimation based on the videos; iii) the evaluation of the algorithms performance; iv) the annotation on these videos and the reference movements provided. Our dataset is publicly available at https://github.com/larocs/SoccerKicks.
OrchestraBox: RFID Music Box for Musical Education at Schools

There are many different educational applications for schools that use technologies like augmented reality (AR) and virtual reality (VR) in order to make the learning process more intuitive and to motivate the students. However, most of these applications are designed to be used by only one person, making it difficult for teachers to use such applications during their lessons. This paper presents a multiuser AR application for musical education at schools that allows the teachers to involve the whole class in the learning process. The application compounds of a music box with RFID antennas and 3D printed figurines of musicians with an RFID chip. The music box can recognize if some figurine is currently placed on the music box and starts playing the corresponding music part.
Towards Fully Automated Inspection of Large Components with UAVs: Offline Path Planning and View Angle Dependent Optimization Strategies

Automation mechanisms are increasingly established in the field of visual quality control. UAVs can be used for particularly large components, such as those used in aircraft or ship production, but also for critical infrastructures. This paper concentrates on the problem of visual quality control in the field of perspective-dependent route planning. It is shown how the requirements for such a system can be implemented and elaborated. Furthermore we investigate how sensor positions can be calculated offline, based on optical- and geometrical requirements and how a trajectory can be planned which contains the found sensor positions for each given area on the component. It is shown how the systems architecture can be designed in order to be able to adapt it to different requirements for the planning of sensor positions and trajectory. The implementation was tested in a simulation environment, evaluated using a benchmark data set and it was shown how above-average results can be achieved on this data set.
Conditional Independence-based Causal Discovery Algorithm for Few-Shot Feature Selection

Feature selection can select the optimal subset of features by eliminating the irrelevant or redundant features, which has been an important challenge in machine learning tasks. Existing neural network methods for data processing often requires large number of features to be filtered, extracted, and transformed. However, the characteristics of few-shot are usually accompanied by high dimensionality and sparse sample size, which making it arduous for neural network methods to locate the truly relevant features. Consequently, a causal feature selection layer based on conditional independence is proposed to embed the traditional neural network. In particular, effective features can be screened out by mining the causal relationships through conditional independence, and it makes the data modeling with certain interpretability. Experimental results show the feasibility of our theory on both simulated and real dataset.
The Performance of Cryptocurrencies under a Sentiment Analysis Approach in the Time of COVID-19

This paper examines the performance of the most popular and highly capitalized cryptocurrencies during the COVID-19 era under a sentiment analysis view. We investigate Bitcoin (BTC), Ethereum (ETH), Binance (BNB) and Cardano (ADA) during the period 1/1/2020-30/9/2021. Using Google trends indices, we test the impact of COVID-19 fear to the performance of the cryptocurrencies, and the empirical evidence show that when the health risk increases the BTC, BNB and ADA prices fall (and vice versa). Moreover, we generate two additional indices based on Google searches that reveal the users’ intention to buy and intention to sell cryptocurrencies. The empirical evidence shows that only the Google index which reveals the users’ intention to buy a cryptocurrency could be a useful and statistically significant factor for the crypto performance. Thus, the sentiment analysis based on the Google searches could be useful for the internet based market of cryptocurrencies and further study for on this direction should be done.
Breast Cancer Detection with Revamped Dataset Using Machine Learning Techniques

Machine learning is a current topic of interest in research and industry, with the implementation of novel strategies all the time. The main purpose of this research activity is to determine the efficiency of machine learning techniques in the detection research of breast cancer. The incidence and mortality of breast cancer in women are increasing day by day. Worldwide, researchers have worked hard to help clinicians provide the best model for detecting diagnosis and breast cancer. In this work, learning UCI machine Wisconsin breast cancer data from a set of databases, model, and analyze the performance of existing work use, compared to the same data set. The dataset is analyzed, and the revamped dataset is constructed by eliminating redundant features and appending new features essential for prediction. Logistic regression, K nearest neighbors (KNN), support vector machine (SVM), decision trees, random forest, XGBoost, using a machine learning algorithm, such as re-organized data set of artificial neural network AdaBoost, 8 one of prediction build the model application (ANN). Standard to analyze the accuracy rate. In the experiment, these classifications have been shown to work for breast cancer with &gt;97% accuracy. Logistic regression, XGBoost and Adaboost, stand on top with 99.28 percent accuracy. The experiment also, the balanced data set of removal outliers and balance, shows that have a significant impact on the model’s prediction performance.
LightDCVO: Lightweight Neural Network Based Visual Odometry Using a Downward-facing Monocular Camera

Generally, wheel odometry accuracy tends to drop significantly in situations such as running on soft ground or when the robot is turning. Visual odometry (VO) is considered a promising alternative for calculating the trajectory of autonomous robots. However, the conventional VO methods have the issues that the accuracy decreases in the environment where in the crowds or have few feature points. In this paper, we propose a real-time and highly accurate VO method that uses a downward-facing monocular camera. The proposed method has advantages in the accuracy and calculation performance by estimating and matching the feature of the ground surface using a lightweight neural network. Besides, the pose of the camera is automatically estimated to prevent the decrease of odometry accuracy due to the error of the camera posture.
Effectiveness of Graphic Training Technologies in Today's Environment

Today the higher education system should guarantee a confident transition to the digital era, provide training for professionals in demand in the labor market, who have digital literacy and thoroughly know the Internet technologies. In this paper, the authors investigate the problem of subject learning management and digital transformation of the educational process on the example of basic geometric-graphic training of junior students in a technical university. The experience of implementation of the GGT distance course on the Moodle platform and the system of automated control of educational results, as well as their use in various forms of education: full-time, distance and mixed, is described. Positive and negative aspects of distance learning are discussed. The paper presents the results of a survey of students, as well the study of the quality of knowledge gained in the discipline in various forms of education based on automated control of current educational tests. The study analyzes the learning outcomes shown by the students of the Mining Faculty of PNRPU over the past several years with various forms of organizing the course of GGT. The effectiveness of using relevant educational technologies in the framework of geometric and graphic training at PNRPU is assessed. The data of the survey of students, showing their attitude to distance learning, are given. It is concluded that in order to achieve the best results in teaching, a mixed approach is optimal, combining the use of distance learning with traditional classroom studies of students under the guidance of a teacher.
Sleeping with the Enemy: An Economic and Security Analysis of Bug Bounty Programs

In a bug bounty program, the firm uses a reward (the "bug bounty prize") to attract the public to submit vulnerabilities that could otherwise be exploited to harm its system. In this paper, we analyze the economic, security, and social welfare implications of bug bounty programs. We show that the bug bounty program is economically beneficial to the firm as long as it is not too costly for the firm to entice the strategic hacker. The firm enjoys two benefits from a bug bounty program: Attack diversion and protection delegation. Yet, even when the bug bounty program is economically beneficial, it need not enhance security. It enhances security when law enforcement protects the system better in the absence of a bug bounty program, the exogenous threat is small, or the protection cost is large. We show that the commonly-used bug bounty program is inefficient. This inefficiency can be addressed if the firm's effort is observable or via the presence of an altruistic third party. We propose a more practicable solution that requires only partially observing the strategic hacker's effort and the presence of a profit making third party. We draw related implications especially with respect to research and practice in crowdsourcing and security services outsourcing.
Feature Fusion based Re-voting for 3D Object Detection

3D object detection based on point cloud is a challenging visual task, which is helpful to the realization of various 3D visual applications. A few recent works based votenet recognize objects by using hough voting. However, the voting strategy in votenet can only obtain some sampling points from incomplete surfaces and chaotic backgrounds, without considering the features and position relation of the original cloud points. In this work, we introduce a new 3D object detection method called feature fusion based revoting network (FFRNet). Our method adds a supervision mechanism to the simple voting mechanism and fuses the feature of seed points and voting points to increase the richness of information in the re-voting module. The feature fusion operation enhances the acquisition of effective information of the original surface points, So as to achieve more reliable and flexible object positioning and category prediction results. We validate our model on the challenging ScanNet V2 dataset, advancing votenet results by 3.6 [email protected]
Compressed Video Quality Metric Based on Just-Noticeable-Difference and Saliency-aware Blocking Detection

Block-based prediction-quantization hybrid coding framework is widely used in video compression, which results in visually annoying artifacts, i.e., blocking artifacts, on encoded videos. They significantly degrade the Quality-of-Experience (QoE). To improve the QoE, it is crucial to develop Video Quality Assessment (VQA) methods that can identify and quantify various artifacts, among which the negative effect introduced by blocking artifacts is of guiding significance. To evaluate and monitor video quality effectively, a novel Blocking-based Compressed Video Quality Assessment (BCVQA) is proposed in this paper. The key idea of the BCVQA method is a blocking detection model based on Just-Noticeable-Difference (JND) mechanism, which is aimed to improve detection performance, since it only focuses on the observable blocking artifacts. Furthermore, the saliency model is utilized in quality prediction procedure after blocking detection, considering the visual attention mechanism. With the fusion of the above two visual mechanisms, quality prediction of compressed videos becomes more effective, since the useful information is refined in our framework. Experimental results show that the proposed BCVQA is consistent with perceived quality and can effectively evaluate video quality.
The Triad Construction Path of Gold Course Based on Information Technology

In 2018,"eliminating water course and creating gold course "was formally written into the document of the Ministry of Education. Since then, how to create gold course has become one of the key work in colleges and universities. Based on the current information technology background, this paper puts forward the triad construction path of gold course with teachers as the core, in order to change the teaching mode of teachers' unilateral irrigation and improve the students' comprehensive quality.
What Is Bad Online Behavior, and What Can We Do About It?

Explores the ways we can regulate online behavior. Larry Lessig divides regulation into laws, social norms, markets, and technology. As we’ll see, ideas about “free speech” vary around the world, and laws about hate speech are quite different in the United States compared to other nations. Where we draw the line between free speech and illegal speech is the most hotly contested issue about the internet. What is at stake is not just what we all read or watch, but who we are. At its worst, the internet can make insane and hateful ideas seem normal, and make it easy for new people to be radicalized. On the other hand, if some speech is not allowed, who decides where to draw the line? How do we make balanced decisions about what content to allow?