Reviewerly/exHarmony huggingface dataset:
Primary Education and Augmented Reality. Other Form to Learn

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Automatically recognizing the semantic elements from UML class diagram images

Design models are essential for multiple tasks in software engineering, such as consistency checking, code generation, and design-to-code tracing. Almost all of these works need a semantically analyzable model to represent the software architecture design, e.g., a UML class diagram. Unfortunately, many design models are stored as images and embedded in text-based documentations, impeding the usage and evolution of these models. Thus, identifying the semantic elements of design models from images is important. However, there are lots of design models with different elements in diverse representations, which ask for different approaches for semantic elements extraction. In order to grasp an overview of the commonly used design model types, we conduct a survey on both open-source communities and industry. We find that design model diagrams are usually embedded in documents as pictures (73.72%), and UML class diagrams are the most used type (55.43%). Considering that there are limited studies on automatically recognizing the semantic elements from class diagram images, we propose an approach, which we call ReSECDI. ReSECDI includes our customized design for extracting UML class diagram elements based on image processing technologies. We design a rectangle clustering method for class recognition, to address the challenge that the presentation of classes may vary due to the UML constraints and tools' styles. We design a polygonal line merging method and double-recognition-approximation method for relationship recognition to deal with the impact of low resolution on the detection. We evaluate the applicability of ReSECDI on 30 images drawn by three popular UML tools and 50 diagrams collected from the open-source communities, and get promising performances. ReSECDI can recognize all types of semantic elements commonly used. It has well applicability and can be used to process the images drawn by the mainstream tools and stored in different resolutions. Editor's note: Open Science material was validated by the Journal of Systems and Software Open Science Board.
On the design and analysis of near-term quantum network protocols using Markov decision processes

The quantum internet is one of the frontiers of quantum information science. It will revolutionize the way we communicate and do other tasks, and it will allow for tasks that are not possible using the current, classical internet. The backbone of a quantum internet is entanglement distributed globally in order to allow for such novel applications to be performed over long distances. Experimental progress is currently being made to realize quantum networks on a small scale, but much theoretical work is still needed in order to understand how best to distribute entanglement, especially with the limitations of near-term quantum technologies taken into account. This work provides an initial step toward this goal. In this work, we lay out a theory of near-term quantum networks based on Markov decision processes (MDPs), and we show that MDPs provide a precise and systematic mathematical framework to model protocols for near-term quantum networks that is agnostic to the specific implementation platform. We start by simplifying the MDP for elementary links introduced in prior work and by providing new results on policies for elementary links in the steady-state (infinite-time) limit. Then, we show how the elementary link MDP can be used to analyze a complete quantum network protocol. We then provide an extension of the MDP formalism to two elementary links. Here, as new results, we derive linear programing relaxations that allow us to obtain optimal steady-state policies with respect to the expected fidelity and waiting time of the end-to-end link.
Double-branch U-Net for multi-scale organ segmentation

U-Net has achieved great success in the task of medical image segmentation. It encodes and extracts information from several convolution blocks, and then decodes the feature maps to get the segmentation results. Our experiments show that in a multi-scale medical segmentation task, excessive downsampling will cause the model to ignore the small segmentation objects and thus fail to complete the segmentation task. In this work, we propose a more complete method Double-branch U-Net (2BUNet) to solve the multi-scale organ segmentation challenge. Our model is divided into four parts: main branch, tributary branch, information exchange module and classification module. The main advantages of the new model consist of: (1) Extracting information to improve model decoding capabilities using the complete encoding structure. (2) The information exchange module is added to the main branch and tributaries to provide regularization for the model, so as to avoid the large gap between the two paths. (3) Main branch structure for extracting major features of large organ. (4) The tributary structure is used to enlarge the image to extract the microscopic characteristics of small organ. (5) A classification assistant module is proposed to increase the class constraint for the output tensor. The comparative experiments show that our method achieves state-of-the-art performances in real scenes.
Enhancing Foreign Language Learning Outcomes and Mitigating Cultural Attributes Inherent in Asian Culture in a Mobile-Assisted Language Learning Environment

To achieve sustainable language learning, we designed a foreign language learning activity that extended traditional lectures in classrooms to informal learning in the real world using mobile technology. Chinese undergraduate students participated in our learning activity. We tested whether our learning activity could enhance their language learning outcomes through employing a single group pretest post-test design. We explored how the participants perceived the usefulness of the activity to foster language learning by administering a questionnaire survey. Finally, we investigated whether our learning activity could mitigate the cultural attributes inherent in Asian culture through interviews with the participants and their instructors. Our results demonstrated that participant learning outcomes improved during the learning activity. The results also showed that the perceptions of most participants of the value of the exercise were high. In the interviews, the participants and the instructors revealed that they felt the learning activity was useful and interesting. They also claimed that the learning activity helped mitigate cultural attributes, such as shyness, reservedness, passiveness, inhibition, lack of confidence, risk-avoidance, test-oriented learning, reliance on teachers for knowledge, and memorization. Based on these results, we provide some implications and useful suggestions for educators and researchers.
An Accurate and Efficient Quaternion-Based Visualization Approach to 2D/3D Vector Data for the Mobile Augmented Reality Map

Increasingly complex vector map applications and growing multi-source spatial data pose a serious challenge to the accuracy and efficiency of vector map visualization. It is true especially for real-time and dynamic scene visualization in mobile augmented reality, with the dramatic development of spatial data sensing and the emergence of AR-GIS. Such issues can be decomposed into three issues: accurate pose representation, fast and precise topological relationships computation and high-performance acceleration methods. To solve these issues, a novel quaternion-based real-time vector map visualization approach is proposed in this paper. It focuses on precise position and orientation representation, accurate and efficient spatial relationships calculation and acceleration parallel rendering in mobile AR. First, a quaternion-based pose processing method for multi-source spatial data is developed. Then, the complex processing of spatial relationships is mapped into simple and efficient quaternion-based operations. With these mapping methods, spatial relationship operations with large computational volumes can be converted into efficient quaternion calculations, and then the results are returned to respond to the interaction. Finally, an asynchronous rendering acceleration mechanism is also presented in this paper. Experiments demonstrated that the method proposed in this paper can significantly improve vector visualization of the AR map. The new approach, when compared to conventional visualization methods, provides more stable and accurate rendering results, especially when the AR map has strenuous movements and high frequency variations. The smoothness of the user interaction experience is also significantly improved.
Parameterized Quantum Circuits for Learning Cooperative Quantum Teleportation

Abstract Quantum teleportation is an elemental process in quantum communication and many variants have been widely investigated theoretically and experimentally. Motivated by cooperation in classical communications, cooperative quantum teleportation (CQT) are developed with features of the cooperation referring to allocation of resources and operations among participants. Parameterized quantum circuit (PQC) are employed to learn the CQT protocols on account of different training scenarios with controls of gate parameters among shared entangled states, measurement, and recovery operations. Numerical results show the CQT protocols can be discovered with unit fidelity regardless of ideal or nonideal channels by training the PQCs with hybrid quantum‐classical (HQC) optimization under full cooperations of the participants. Further, the influence of quantum channel noise on the teleportation performance is explored by parameterizing the entangled states with noise and entanglement parameters under adjustable recovery operations. Simulation results indicate the trained PQC can improve the system performance, which implies the potential denoising capability of the HQC algorithms. The suggested CQT protocols satisfy the underlying properties of universality, randomness, locality and cooperation, and discussions indicate the designed quantum circuits can be optimized in view of quantum and circuit costs by appropriate optimization algorithms for achieving higher state fidelity in the future work.
An Efficient Heap Based Optimizer Algorithm for Feature Selection

The heap-based optimizer (HBO) is an innovative meta-heuristic inspired by human social behavior. In this research, binary adaptations of the heap-based optimizer B_HBO are presented and used to determine the optimal features for classifications in wrapping form. In addition, HBO balances exploration and exploitation by employing self-adaptive parameters that can adaptively search the solution domain for the optimal solution. In the feature selection domain, the presented algorithms for the binary Heap-based optimizer B_HBO are used to find feature subsets that maximize classification performance while lowering the number of selected features. The textitk-nearest neighbor (textitk-NN) classifier ensures that the selected features are significant. The new binary methods are compared to eight common optimization methods recently employed in this field, including Ant Lion Optimization (ALO), Archimedes Optimization Algorithm (AOA), Backtracking Search Algorithm (BSA), Crow Search Algorithm (CSA), Levy flight distribution (LFD), Particle Swarm Optimization (PSO), Slime Mold Algorithm (SMA), and Tree Seed Algorithm (TSA) in terms of fitness, accuracy, precision, sensitivity, F-score, the number of selected features, and statistical tests. Twenty datasets from the UCI repository are evaluated and compared using a set of evaluation indicators. The non-parametric Wilcoxon rank-sum test was used to determine whether the proposed algorithms’ results varied statistically significantly from those of the other compared methods. The comparison analysis demonstrates that B_HBO is superior or equivalent to the other algorithms used in the literature.
Optimal and Bounded-Suboptimal Multi-Goal Task Assignment and Path Finding

We formalize and study the multi-goal task assignment and path finding (MG-TAPF) problem from theoretical and algorithmic perspectives. The MG-TAPF problem is to compute an assignment of tasks to agents, where each task consists of a sequence of goal locations, and collision-free paths for the agents that visit all goal locations of their assigned tasks in sequence. Theoretically, we prove that the MG-TAPF problem is NP-hard to solve optimally. We present algorithms that build upon algorithmic techniques for the multi-agent path finding problem and solve the MG-TAPF problem optimally and bounded-suboptimally. We experimentally compare these algorithms on a variety of different benchmark domains.
MCoM: A Semi-Supervised Method for Imbalanced Tabular Security Data

MCoM (Mixup Contrastive Mixup) is a new semi-supervised learning methodology that innovates a triplet mixup data augmentation approach to address the imbalanced data problem in tabular security data sets. Tabular data sets in cybersecurity domains are widely known to pose challenges for machine learning because of their heavily imbalanced data (e.g., a small number of labeled attack samples buried in a sea of mostly benign, unlabeled data). Semi-supervised learning leverages a small subset of labeled data and a large subset of unlabeled data to train a learning model. While semi-supervised methods have been well studied in image and language domains, in security domains they remain underutilized, especially on tabular security data sets which pose especially difficult contextual information loss and balance challenges for machine learning. Experiments applying MCoM to collected security data sets show promise for addressing these challenges, achieving state-of-the-art performance compared with other methods.
Ring-ExpLWE: A High-Performance and Lightweight Post-Quantum Encryption Scheme for Resource-Constrained IoT Devices

As the Internet of Things (IoT) expands explosively existing network connections, the transmission and processing of private data is facing more serious threats of leakage and theft. Classical public key encryption schemes are difficult to guarantee strong security protection, because the mathematically hard problems they rely on are no longer difficult to solve under the rapid development of quantum computing. Therefore, a more high-performance and quantum-resistant encryption scheme Ring-ExpLWE is proposed, in which the error vector is sampled in the exponential distribution instead of the binary distribution in the previous Ring-BinLWE. We evaluate the Ring-ExpLWE’s security level by analyzing the runtime under quantum hybrid attack and comparing the standard deviation of the noise polynomial coefficients. Compared with Ring-BinLWE, the proposed Ring-ExpLWE requires larger runtime for quantum hybrid attack and has a more discrete noise distribution. Therefore, Ring-ExpLWE can provide a higher security level under the same parameter set. Moreover, the high-performance software and hardware implementations for the Ring-ExpLWE scheme are proposed, respectively. Based on the Cortex-M3 microprocessor platform, encryption, and decryption only require 35.6 and 17.8 ms in our software implementation, respectively. Compared with the previous Ring-BinLWE schemes, while significantly improving the security level, the Area <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> Time (AT) of our high-performance and lightweight hardware implementations is reduced by 49.2% and 49.5%, respectively, when the FPGA platform is Spartan 6.
Real-time gaze prediction in virtual reality

Gaze is an important indicator of visual attention and knowledge of gaze location can be used to improve and augment Virtual Reality (VR) experiences. This has led to the development of VR Head Mounted Displays (HMD) with inbuilt gaze trackers. Given the latency constraints of VR, foreknowledge of gaze, i.e., before it is reported by the gaze tracker, can similarly be leveraged to preemptively apply gaze-based improvements and augmentations to a VR experience, especially in distributed VR architectures. In this paper, we propose a light weight neural network based method utilizing only past HMD pose and gaze data to predict future gaze locations, forgoing computationally heavy saliency computation. Most work in this domain has focused on either 360°or ego-centric video or synthetic VR content with rather naive interaction dynamics like free viewing or supervised visual search tasks. Our solution considers data from the exhaustive OpenNEEDs dataset which contains 6 Degrees of Freedom (6DoF) data captured in VR experiences with subjects given the freedom to explore the VR scene and/or to engage in tasks. Our solution outperforms the very strict baseline: current gaze to predict gaze in real-time for sub 150ms prediction horizons for VR use-cases.
Hardware-assisted mechanisms to enforce control flow integrity: A comprehensive survey

Today, a vast amount of sensitive data worth millions of dollars is processed in untrusted data centers; hence, the confidentiality and integrity of the code and data are of paramount importance. Given the high incentive of mounting a successful attack, the complexity of attack methods has grown rapidly over the years. The attack methods rely on vulnerabilities present in the system to hijack the control flow of a process and use it to either steal sensitive information or degrade the quality of service. To thwart these attacks, the complexity of the defense methods has also increased in tandem. Researchers have explored different methods to ensure the secure execution of an application. The defense methods range from software-only to hardware-only to hybrid defense methods. In this survey, we focus on the relatively new hybrid form of defense methods where software and hardware work in tandem to protect the control flow of applications. We present a novel three-level taxonomy of these defense mechanisms based on first principles and use them to classify existing defense methods. After presenting the taxonomy, we critically analyze the proposed defense methods, study the evolution of the field and outline the challenges for future work.
A Neuro-Symbolic ASP Pipeline for Visual Question Answering

Abstract We present a neuro-symbolic visual question answering (VQA) pipeline for CLEVR, which is a well-known dataset that consists of pictures showing scenes with objects and questions related to them. Our pipeline covers (i) training neural networks for object classification and bounding-box prediction of the CLEVR scenes, (ii) statistical analysis on the distribution of prediction values of the neural networks to determine a threshold for high-confidence predictions, and (iii) a translation of CLEVR questions and network predictions that pass confidence thresholds into logic programmes so that we can compute the answers using an answer-set programming solver. By exploiting choice rules, we consider deterministic and non-deterministic scene encodings. Our experiments show that the non-deterministic scene encoding achieves good results even if the neural networks are trained rather poorly in comparison with the deterministic approach. This is important for building robust VQA systems if network predictions are less-than perfect. Furthermore, we show that restricting non-determinism to reasonable choices allows for more efficient implementations in comparison with related neuro-symbolic approaches without losing much accuracy.
Learning Stable Dynamical Systems for Visual Servoing

This work presents the dual benefit of integrating imitation learning techniques, based on the dynamical systems formalism, with the visual servoing paradigm. On the one hand, dynamical systems allow to program additional skills without explicitly coding them in the visual servoing law, but leveraging few demonstrations of the full desired behavior. On the other, visual servoing allows to consider exteroception into the dynam-ical system architecture and be able to adapt to unexpected environment changes. The beneficial combination of the two concepts is proven by applying three existing dynamical systems methods to the visual servoing case. Simulations validate and compare the methods; experiments with a robot manipulator show the validity of the approach in a real-world scenario.
MAGIC: A Method for Assessing Cyber Incidents Occurrence

The assessment of cyber risk plays a crucial role for cybersecurity management, and has become a compulsory task for certain types of companies and organizations. This makes the demand for reliable cyber risk assessment tools continuously increasing, especially concerning quantitative tools based on statistical approaches. Probabilistic cyber risk assessment methods, however, follow the general paradigm of probabilistic risk assessment, which requires the magnitude and the likelihood of incidents as inputs. Unfortunately, for cyber incidents, the likelihood of occurrence is hard to estimate based on historical and publicly available data; so, expert evaluations are commonly used, which however leave space to subjectivity. In this paper, we propose a novel probabilistic model, called MAGIC (Method for AssessinG cyber Incidents oCcurrence), to compute the likelihood of occurrence of a cyber incident, based on the evaluation of the cyber posture of the target organization. This allows deriving tailor-made inputs for probabilistic risk assessment methods, like HTMA (How To Measure Anything in cybersecurity risk), FAIR (Factor Analysis of Information Risk) and others, thus considerably reducing the margin of subjectivity in the assessment of cyber risk. We corroborate our approach through a qualitative and a quantitative comparison with several classical methods.
Assessing Differentially Private Variational Autoencoders Under Membership Inference

We present an approach to quantify and compare the privacy-accuracy trade-off for differentially private Variational Autoencoders. Our work complements previous work in two aspects. First, we evaluate the strong reconstruction MI attack against Variational Autoencoders under differential privacy. Second, we address the data scientist's challenge of setting privacy parameter $$\epsilon $$ , which steers the differential privacy strength and thus also the privacy-accuracy trade-off. In our experimental study we consider image and time series data, and three local and central differential privacy mechanisms. We find that the privacy-accuracy trade-offs strongly depend on the dataset and model architecture. We do rarely observe favorable privacy-accuracy trade-offs for Variational Autoencoders, and identify a case where LDP outperforms CDP.