Reviewerly/exHarmony huggingface dataset:
Edge-Preserving Stereo Matching Using LiDAR Points and Image Line Features

This letter proposes a LiDAR and image line-guided stereo matching method (L2GSM), which combines sparse but high-accuracy LiDAR points and sharp object edges of images to generate accurate and fine-structure point clouds. After extracting depth discontinuity lines on the image by using LiDAR depth information, we propose a trilateral update of cost volume and depth discontinuity lines-aware semi-global matching (SGM) strategies to integrate LiDAR data and depth discontinuity lines into the dense matching algorithm. The experimental results for the indoor and aerial datasets show that our method significantly improves the results of the original SGM and outperforms two state-of-the-art LiDAR constraints’ SGM methods, especially in recovering the 3-D structure of low-textured and depth discontinuity regions. In addition, the 3-D point clouds generated by our proposed method outperform the LiDAR data and dense matching point clouds generated by Metashape and SURE aerial in terms of completeness and edge accuracy.
Overworld: Assessing the Geometry of the World for Human-Robot Interaction

For a robot to interact with humans in a given environment, a key need is to understand its environment in terms of the objects composing it, the other agents acting in it, and the relations between all of them. This capability is often called the geometrical situation assessment and is mainly related to spatial reasoning in time. In this letter, we present Overworld, a novel lightweight and open-source framework, merging the key features of a decade of research in the domain. It permanently maintains a geometric state of the world from the point of view of the robot by aggregating perceptual information from several sources and reasoning on them to create a coherent world. Furthermore, Overworld implements perspective-taking by emulating the humans' ability to perceive to estimate the state of the world from their perspective. Finally, thanks to a strong link with an ontology framework, it ensures knowledge coherence in the whole robotic architecture. This work is part of a broader effort to develop a complete, stable, and shareable decisional robotic architecture for Human-Robot Interaction.
EMSim: A Fast Layout Level Electromagnetic Emanation Simulation Framework for High Accuracy Pre-Silicon Verification

Electromagnetic (EM) emanation measurement and evaluation is one important testing for modern integrated circuits (ICs). Severe electromagnetic interference may degrade the performance of electronic devices or even cause system crashes. As a result, modern ICs need to follow strict electromagnetic compatibility (EMC) requirements. Moreover, EM emanations offer a covert channel for adversaries to steal secret information from fabricated ICs, causing side channel attacks. Due to the lack of fast and high-accuracy EM simulation tools, existing EM measurements often happen at the post-silicon stage. Any identification of side channel vulnerability or EM incompatibility may lead to high cost and delay the time-to-market. As a result, design-time EM simulation tools with fast simulation speed and high accuracy for pre-silicon designs are urgently needed. To this end, we propose EMSIM, a layout-level EM simulation framework that significantly speeds up the EM simulation process while maintaining high accuracy of the simulated EM emanations. To achieve this goal, we provide the theoretical explanation for the root cause of EM emanations from ICs. Guiding by this, EMSIM leverages techniques of parasitic network reduction and device model approximation to reduce the computation complexities while still ensuring high simulation accuracy. EMSIM further leverages Graphics Processing Unit (GPU) resources to solve equations for EM simulation. The efficiency and effectiveness of EMSIM are validated by showing the consistency between simulation results and physical measurements obtained from fabricated circuit designs.
Machine Learning-Based Ransomware Classification of Bitcoin Transactions

Ransomware attacks are one of the most dangerous related crimes in the coin market. To increase the challenge of fighting the attack, early detection of ransomware seems necessary. In this article, we propose a high-performance Bitcoin transaction predictive system that investigates Bitcoin payment transactions to learn data patterns that can recognize and classify ransomware payments for heterogeneous bitcoin networks into malicious or benign transactions. The proposed approach makes use of three supervised machine learning methods to learn the distinctive patterns in Bitcoin payment transactions, namely, logistic regression (LR), random forest (RF), and Extreme Gradient Boosting (XGBoost). We evaluate these ML-based predictive models on the BitcoinHeist ransomware dataset in terms of classification accuracy and other evaluation measures such as confusion matrix, recall, and F1-score. It turned out that the experimental results recorded by the XGBoost model achieved an accuracy of 99.08%. As a result, the resulting model accuracy is higher than many recent state-of-the-art models developed to detect ransomware payments in Bitcoin transactions.
Robust Video-Text Retrieval Via Noisy Pair Calibration

Video-text retrieval is a fundamental task in managing the emerging massive amounts of video data. The main challenge focuses on learning a common representation space for videos and queries where the similarity measurement can reflect the semantic closeness. However, existing video-text retrieval models may suffer from the following noise in the common space learning procedure: First, the video-text correspondences in positive pairs may not be exact matches. The crowdsourcing annotation for existing datasets leads to inevitable tagging noise for non-expert annotators. Second, the learning of video-text representation is based on the negative samples randomly sampled. Instances that are semantically similar to the query may be incorrectly categorized as negative samples. To alleviate the adverse impact of these noisy pairs, we propose a novel robust video-text retrieval method that protects the model from noisy positive and negative pairs by identifying and calibrating noisy pairs with their uncertainty score. In particular, we propose a noisy pair identifier, which divides the training dataset into noisy and clean subsets based on the estimated uncertainty of each pair. Then, with the help of uncertainties, we calibrate the two types of noisy pairs with an adaptive margin triplet loss and a weighted triplet loss function, respectively. To verify the effectiveness of our methods, we conduct extensive experiments on three widely used datasets. Experimental results show that the proposed robust video-text retrieval methods successfully identify and calibrate the noisy pairs and improve retrieval performance.
Exploring the Application of Design Thinking Methodology in Cellular Communications Network Planning and Deployment

Cellular network planning and deployment represent a set of activities that operators have been performing since the advent of mobile wireless communication systems. Decisions along this route traditionally have been based on engineering or commercial criteria, focusing on providing the best service to users. Although operator companies gather information regarding perceived quality of service among their subscribers, mainly using surveys, engineers work with numerical data more than with people’s interests, feelings, perceptions, or hopes. In other sectors, Design Thinking has arisen as a methodology that allows designers to involve real-world users in implementing new products or services. Thus, we are introducing this methodology for cellular networking, exploring its application in a process that could be designed in a more human-centered way. With this aim, we analyzed the different steps that could be included in the network planning and deployment. From this analysis, we detected which actions have chances to be explored in a human-centered view and thus, can be improved with some Design Thinking tips. Finally, we developed an experience of limited potential, comparing the insights obtained by an empathic interview to those given by traditional surveys, to show what Design Thinking could provide.
A Toolkit for Detecting Fallacious Calls for Papers from Potential Predatory Journals

Flattering emails are crucial in tempting authors to submit papers to predatory journals. Although there is ample literature regarding the questionable practices of predatory journals, the nature and detection of spam emails need more attention. Current research provides insight into fallacious calls for papers from potential predatory journals and develops a toolkit in this regard.In this study, we analyzed three datasets of calls for papers from potential predatory journals and legitimate journals using a text mining approach and R programming language.Overall, most potential predatory journals use similar language and templates in their calls for papers. Importantly, these journals praise themselves in glorious terms involving positive words that may be rarely seen in emails from legitimate journals. Based on these findings, we developed a lexicon for detecting unsolicited calls for papers from potential predatory journals.We conclude that calls for papers from potential predatory journals and legitimate journals are different, and it can help to distinguish them. By providing an educational plan and easily usable tools, we can deal with predatory journals better than previously.
Accurate Encrypted Malicious Traffic Identification via Traffic Interaction Pattern Using Graph Convolutional Network

Telecommuting and telelearning have gradually become mainstream lifestyles in the post-epidemic era. The extensive interconnection of massive terminals gives attackers more opportunities, which brings more significant challenges to network traffic security analysis. The existing attacks, often using encryption technology and distributed attack methods, increase the number and complexity of attacks. However, the traditional methods need more analysis of encrypted malicious traffic interaction patterns and cannot explore the potential correlations of interaction patterns in a macroscopic and comprehensive manner. Anyway, the changes in interaction patterns caused by attacks also need further study. Therefore, to achieve accurate and effective identification of attacks, it is essential to comprehensively describe the interaction patterns of malicious traffic and portray the relations of interaction patterns with the appearance of attacks. We propose a method for classifying attacks based on the traffic interaction attribute graph, named G-TIAG. At first, the G-TIAG studies interaction patterns of traffic describes the construction rule of the graphs and selects the attributive features of nodes in each graph. Then, it uses a convolutional graph network with a GRU and self-attention to classify benign data and different attacks. Our approach achieved the best classification results, with 89% accuracy and F1-Score, 88% recall, respectively, on publicly available datasets. The improvement is about 7% compared to traditional machine learning classification results and about 6% compared to deep learning classification results, which finally successfully achieved the classification of attacks.
An attack volume metric

Abstract For more than a decade, the notion of attack surface has been used to define the set of vulnerable assets that an adversary may exploit to penetrate a system, and various metrics have been developed to quantify the extent of a system's attack surface. However, most approaches to tackle this problem have failed to consider the complex interdependencies that exist between the many components of a distributed system, its vulnerabilities, and its configuration parameters. In our work, building upon previous research on vulnerability metrics and on graphical models to capture such interdependencies, we propose a novel approach to evaluate the potential risk associated with exposed vulnerabilities by studying how the effect of each vulnerability exploit propagates through chains of dependencies. Our analysis goes beyond the scope of traditional attack surface metrics, and considers the depth and implications of potential attacks, leading to the definition of a new family of metrics, which we refer to as attack volume metrics. We present experimental results illustrating how the proposed metric scales for graphs of realistic sizes, and illustrate its application to real‐world testbeds.
Research on the promotion of digital teaching and learning toward achieving China’s dual-carbon strategy

The dual-carbon strategy of China affects all walks of life, all of which will be affected in realizing these goals. The same is true of the education sector, and China has the world’s largest education population. The education sector has a large potential to reduce carbon emissions due to its high carbon consumption. Based on the requirements of carbon emission reduction in education, this paper studied the contribution of digital teaching and learning and the education sector toward reducing carbon emissions and discusses how to promote the mechanism construction of carbon emission reduction in the education sector. The study used a comparative analysis and questionnaire research method to analyze the impact of digital teaching and learning on energy saving and emission reduction in the education sector. Research shows that using the Internet and fully utilizing the advantages of digital teaching and learning could improve the quality of teaching, reduce carbon emissions, and help achieve the goal of dual-carbon.
A Bayesian Uncertainty Quantification Approach for Agent-Based Modeling of Networked Anagram Games

In group anagram games, players cooperate to form words by sharing letters that they are initially given. The aim is to form as many words as possible as a group, within five minutes. Players take several different actions: requesting letters from their neighbors, replying to letter requests, and forming words. Agent-based models (ABMs) for the game compute likelihoods of each player's next action, which contain uncertainty, as they are estimated from experimental data. We adopt a Bayesian approach as a natural means of quantifying uncertainty, to enhance the ABM for the group anagram game. Specifically, a Bayesian nonparametric clustering method is used to group player behaviors into different clusters without pre-specifying the number of clusters. Bayesian multi-nominal regression is adopted to model the transition probabilities among different actions of the players in the ABM. We describe the methodology and the benefits of it, and perform agent-based simulations of the game.
HDF5eis: A storage and input/output solution for big multidimensional time series data from environmental sensors

Modern high-performance computing (HPC) tasks overwhelm conventional geophysical data formats. We describe a new data schema called HDF5eis (read H-D-F-size) for handling big multidimensional time series data from environmental sensors in HPC applications and implement a freely available Python application programming interface (API) for building and processing HDF5eis files. HDF5eis augments the popular Hierarchical Data Format 5 with a minimal set of additional conventions that facilitate fast and flexible data input and output protocols for regularly sampled (in time) data with any number of dimensions. HDF5eis supports arbitrary ancillary data (e.g., metadata) storage in columnar format or as UTF-8 encoded byte streams alongside time series data. Our HDF5eis API enables simple and efficient access to big data sets distributed across a potentially large number of small heterogeneous files through a single point of access. HDF5eis outperforms conventional seismic data formats by up to two orders of magnitude in terms of random read access times. We contribute HDF5eis as an operational tool and an experimental draft proposal that will help establish the next generation of data standards in the earth sciences.
Stabilization of Stochastic Dynamical Systems of a Random Structure with Markov Switches and Poisson Perturbations

An optimal control for a dynamical system optimizes a certain objective function. Here, we consider the construction of an optimal control for a stochastic dynamical system with a random structure, Poisson perturbations and random jumps, which makes the system stable in probability. Sufficient conditions of the stability in probability are obtained, using the second Lyapunov method, in which the construction of the corresponding functions plays an important role. Here, we provide a solution to the problem of optimal stabilization in a general case. For a linear system with a quadratic quality function, we give a method of synthesis of optimal control based on the solution of Riccati equations. Finally, in an autonomous case, a system of differential equations was constructed to obtain unknown matrices that are used for the construction of an optimal control. The method using a small parameter is justified for the algorithmic search of an optimal control. This approach brings a novel solution to the problem of optimal stabilization for a stochastic dynamical system with a random structure, Markov switches and Poisson perturbations.
A Bayesian decision support system for counteracting activities of terrorist groups

Abstract We present an integrating decision support system designed to aid security analysts’ monitoring of terrorist groups. The system comprises of (i) a dynamic network model of the level of bilateral communications between individuals and (ii) dynamic graphical models of those individual’s latent threat states. These component models are combined in a statistically coherent manner to provide measures of the imminence of an attack by the terrorist group. Domain knowledge provides the structures of the models, values of parameters and prior distributions over latent variables. Inference of the values is performed using time-series of observed data and the statistical dependencies assumed between said data and model variables. The work draws on social network and graphical models used in sociological, military, and medical fields.
Saliency detection for large-scale mesh decimation

Highly complex and dense models of 3D objects have recently become indispensable in digital industries. Mesh decimation then plays a crucial role in the production pipeline to efficiently get visually convincing yet compact expressions of complex meshes. However, the current pipeline typically does not allow artists control the decimation process, just a simplification rate. Thus a preferred approach in production settings splits the process into a first pass of saliency detection highlighting areas of greater detail, and allowing artists to iterate until satisfied before simplifying the model. We propose a novel, efficient multi-scale method to compute mesh saliency at coarse and finer scales, based on fast mesh entropy of local surface measurements. Unlike previous approaches, we ensure a robust and straightforward calculation of mesh saliency even for densely tessellated models with millions of polygons. Moreover, we introduce a new adaptive subsampling and interpolation algorithm for saliency estimation. Our implementation achieves speedups of up to three orders of magnitude over prior approaches. Experimental results showcase its resilience to problem scenarios that efficiently scales up to process multi-million vertex meshes. Our evaluation with artists in the entertainment industry also demonstrates its applicability to real use-case scenarios.
Semantic programming for AI and Robotics

The paper presents a new high-level object-oriented programming language L <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∗</sup> which is a conservative extension of the p-complete logical programming language L. The p-complete language L was developed by us earlier within the framework of semantic programming theory based on PAG-theorem, p-iterative and conditional terms. Thus, L <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∗</sup> -programs can be used to implement any algorithms of polynomial complexity in such areas as artificial intelligence, robotics and smart contracts. This is especially true of the so-called explainable artificial intelligence (or briefly XAI), where it is necessary not only to give out the result based on the obtained data, but also to explain this result in a human-understandable language. This requires a programming language based on the basic constructions of mathematical logic such as logical formulas and terms. Moreover, the syntax of language L <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∗</sup> is as close as possible to the most popular programming languages such as C++, PHP, JavaScript. Which provides programmers with a quick entry into development.
Visual Collaboration - An Approach for Visual Analytical Collaborative Research

Studies have shown that collaboration in scientific fields is rising and considered enormously important. However, collaboration has proved to be challenging for various reasons, among others, the requirements for human-machine workflows. The importance of scientific collaboration lies in the complexity of the challenges that are faced today. The more complex the challenge, the more scientists should work together. The current form of collaboration in the scientific community is not as intelligent as it should be. Scientists have to multitask with various applications, often losing cognitive focus. Collaboration itself is very nearsighted as it is usually conducted not solely based on expertise but instead on social or local networks. We introduce a single-source visual collaboration approach based on learning methods in this work. We use machine learning and natural language processing approaches to improve the traditional research and development process and create a system that facilitates and encourages collaboration based on expertise, enhancing the research collaboration process in many ways. Our approach combines collaborative Visual Analytics with enhanced collaboration techniques to support researchers from different disciplines.
Big Data in 3D: Design guidelines for an immersive 3 dimensional approach to Big Data interaction design

The aim of this paper is to inspire the development of Big Data interaction in a 3 dimensional form to make Big Data Visualization more immersive, intuitive, visually engaging and user friendly. This paper initially discusses the current approaches to Big Data interaction and their limitation therein. It then proceeds to review the latest trends and developments in the technology sphere to explore how Big Data interaction could be technologically enhanced. Following which, the paper articulates design guidelines that could be adopted in the design of 3 dimensional Big Data interaction, based on literature research. These guidelines contribute towards visual formatting, information organization and interaction design of Big Data, to make it more human centered. Through proposing the interaction design of Big Data in alignment with the natural orientation of human perception of the world, the paper intends to make the field of data science accessible to all. Lastly, the paper concludes with a discussion of future vision for Big Data interaction.