Reviewerly/exHarmony huggingface dataset:
Towards Configurable and Efficient Runtime Verification of Blockchain Based Smart Contracts at the Virtual Machine Level

Runtime Verification in both traditional systems and smart contracts has typically been implemented at the application level. Such systems very often run on Virtual Machines which execute their application logic. Conditional monitoring is often implemented to enable for certain monitors to be switched off in aim of reducing execution overheads once certain levels of assurance have been provided. Even when turned off such application level conditional monitoring still incurs added overheads to execute the conditional statement related operations. In this paper, we propose methods to support conditional runtime verification of applications at the virtual machine level. We demonstrate that such an approach can provide lower overheads in terms of both execution and gas.
A Hierarchical Fine-Tuning Approach Based on Joint Embedding of Words and Parent Categories for Hierarchical Multi-label Text Classification

Many important classification problems in real world consist of a large number of categories. Hierarchical multi-label text classification (HMTC) with higher accuracy over large sets of closely related categories organized in a hierarchical structure or taxonomy has become a challenging problem. In this paper, we present a hierarchical fine-tuning deep learning approach for HMTC, where a joint embedding of words and their parent categories is generated by leveraging the hierarchical relations in the hierarchical structure of categories and the textual data. A fine tuning technique is applied to the Ordered Neural LSTM (ONLSTM) neural network such that the text classification results in the upper levels are able to help the classification in the lower ones. The extensive experiments were made over two benchmark datasets, and the results show that the method proposed in this paper outperforms the state-of-the-art hierarchical and flat multi-label text classification approaches, in particular the aspect of reducing computational costs while achieving superior performance.
Parallel Particle Advection Bake-Off for Scientific Visualization Workloads

There are multiple algorithms for parallelizing particle advection for scientific visualization workloads. While many previous studies have contributed to the understanding of individual algorithms, our study aims to provide a holistic understanding of how algorithms perform relative to each other on various workloads. To accomplish this, we consider four popular parallelization algorithms and run a “bake-off” study (i.e., an empirical study) to identify the best matches for each. The study includes 216 tests, going to a concurrency of up to 8192 cores and considering data sets as large as 34 billion cells with 300 million particles. Overall, our study informs three important research questions: (1) which parallelization algorithms perform best for a given workload?, (2) why?, and (3) what are the unsolved problems in parallel particle advection? In terms of findings, we find that the seeding box is the most important factor in choosing the best algorithm, and also that there is a significant opportunity for improvement in execution time, scalability, and efficiency.
Person Re-identification Based on Pose-guided Generative Adversarial Network

Person re-identification (re-id) is a challenging and valuable research topic in the field of computer vision. It needs to match person images with the same identity in multiple camera systems. The change of pose is one of the key factors that affect the network to extract robust features. In order to mitigate the influence of pose variations on person re-id, the paper proposes a person re-id method based on a pose-guided generative adversarial network (PG-GAN), which can be used to learn identity-sensitive and pose-insensitive features. The algorithm is composed of a Siamese convolutional neural network (SCNN) and generative adversarial networks (GANs). SCNN is a symmetric structure with ResNet-50. GANs contain multiple pose discriminators and identity discriminators, as well as incorporate pose loss, which requires appearance of the generated image with same identity to be similar. The proposed method has been well performed on pedestrian reidentification datasets.
MS Excel-Based Application for Implementing the Cryptographic Algorithm Shamir's Secret Sharing

The paper presents the usage of spreadsheets, and more specifically, MS Excel, to build an application that illustrates graphically the secret-sharing algorithm of Israeli cryptographer Shamir. The application helps students learn some aspects of cryptographic information security by presenting this algorithm extremely clearly and making easier to understand the cryptography theory, which is generally difficult because of its mathematical foundations. Numerous examples of Shamir's Secret Sharing obtained by the application with details about the embedded functions used are presented in the paper. The application is used in the training in the course “Telecommunication Security”, included as compulsory in the curriculum of the specialty “Internet and Mobile Communications” for Bachelors at the University of Ruse.
Memory Scaling of Cloud-Based Big Data Systems: A Hybrid Approach

When deploying applications with dynamic and intensive memory footprint to big data systems on public clouds, one important yet challenging question to answer is how to select a specific instance type whose memory capacity is large enough to prevent out-of-memory errors while the cost is minimized without violating performance requirements. The state-of-the-practice solution is trial and error, causing both performance overhead and additional monetary cost. This article investigates two memory scaling mechanisms in public clouds: physical memory (good performance and high cost) and virtual memory (degraded performance and no additional cost). In order to analyze the trade-off between performance and cost of the two scaling options, a performance-cost model is developed that is driven by a lightweight analytic prediction approach through a compact representation of the memory footprint. In addition, for those scenarios when the footprint is unavailable, a meta-model-based prediction method is proposed using just-in-time migration mechanisms. The proposed techniques have been extensively evaluated with various benchmarks and real-world applications on Amazon Web Services: the performance-cost model is highly accurate and the proposed just-in-time migration approach reduces the monetary cost by up to 66 percent.
Implementation of Modified Talbi’s Quantum Inspired Genetic Algorithm for Travelling Salesman Problem on an IBM Quantum Computer

In this study, a Quantum-Inspired Genetic Algorithm is implemented on an IBM Quantum Computer and compared with a Classical Genetic Algorithm based on the Travelling Salesman Problem. Out of numerous Quantum-Inspired Algorithms, the ones which are designed based on the assumption that the qubit state can be duplicated to another variable without collapsing the state during its measurement are only designed for classical computers which is a blocker to be implemented on real Quantum devices. Also the majority of the previous studies have been simulated on classical computers. Therefore in this study a selected Quantum-Inspired Genetic Algorithm is initially executed on a high performance IBM quantum simulator and a modified version of the algorithm is implemented on an IBM Quantum Computer. Both the original and the modified algorithms demonstrated stability during the simulation and on real Quantum devices. However in any of the instances acceptable level of convergence was not seen when compared with a classical genetic algorithm. Yet this study is able to remove a blocker for Quantum-Inspired Algorithms to be implemented on real quantum computers. Also to the best of our knowledge it is the first time for a quantum-inspired algorithm which is constrained with this design limitation to be implemented on a Quantum Computer.
User-centred tooling for modelling of big data applications

We outline the key requirements for a Big Data modelling recommender tool. Our web-based tool is suitable for capturing system requirements in big data analytics applications involving diverse stakeholders. It promotes awareness of the datasets and algorithm implementations that are available to leverage in the design of the solution. We implement these ideas in BiDaML-web, a proof of concept recommender system for Big Data applications, and evaluate the tool using an empirical study with a group of 16 target end-users. Participants found the integrated recommender and technique suggestion tools helpful and highly rated the overall BiDaML web-based modelling experience. BiDaML-web is available at https://bidaml.web.app/ and the source code can be accessed at https://github.com/tarunverma23/bidaml.
A Stochastic Assessment of Attacks based on Continuous-Time Markov Chains

With the increasing interdependence of critical infrastructures, the probability of a specific infrastructure to experience a complex cyber-physical attack is increasing. Thus it is important to analyze the risk of an attack and the dynamics of its propagation in order to design and deploy appropriate countermeasures. The attack trees, commonly adopted to this aim, have inherent shortcomings in representing interdependent, concurrent and sequential attacks. To overcome this, the work presented here proposes a stochastic methodology using Petri Nets and Continuous Time Markov Chain (CTMC) to analyze the attacks, considering the individual attack occurrence probabilities and their stochastic propagation times. A procedure to convert a basic attack tree into an equivalent CTMC is presented. The proposed method is applied in a case study to calculate the different attack propagation characteristics. The characteristics are namely, the probability of reaching the root node & sub attack nodes, the mean time to reach the root node and the mean time spent in the sub attack nodes before reaching the root node. Additionally, the method quantifies the effectiveness of specific defenses in reducing the attack risk considering the efficiency of individual defenses.
Supervisory Control for Dynamic Feature Configuration in Product Lines

In this paper a method for engineering supervisory controllers for product lines with dynamic feature configuration is proposed. The variability in valid configurations is described by a feature model. Behavior of system components is achieved using (extended) finite automata and both behavioral and dynamic configuration constraints are expressed by means of requirements as is common in supervisory control theory. Supervisory control synthesis is applied to compute a behavioral model in which the requirements are adhered to. For the challenges that arise in this setting, multiple solutions are discussed. Some of these solutions are exemplified in the ClF tool set using a wiper system model.
Virtual Humans in AR: Evaluation of Presentation Concepts in an Industrial Assistance Use Case

Embedding virtual humans in educational settings enables the transfer of the approved concepts of learning by observation and imitation of experts to extended reality scenarios. Whilst various presentation concepts of virtual humans for learning have been investigated in sports and rehabilitation, little is known regarding industrial use cases. In prior work on manual assembly, Lampen et al. [21] show that three-dimensional (3D) registered virtual humans can provide assistance as effective as state-of-the-art HMD-based AR approaches. We extend this work by conducting a comparative user study (N=30) to verify implementation costs of assistive behavior features and 3D registration. The results reveal that the basic concept of a 3D registered virtual human is limited and comparable to a two-dimensional screen aligned presentation. However, by incorporating additional assistive behaviors, the 3D assistance concept is enhanced and shows significant advantages in terms of cognitive savings and reduced errors. Thus, it can be concluded, that this presentation concept is valuable in situations where time is less crucial, e.g. in learning scenarios or during complex tasks.
Leveraging Double Simulation to Efficiently Evaluate Hybrid Patterns on Data Graphs

Labeled graphs are used to represent entities and their relationships in a plethora of Web applications. Graph pattern matching is a fundamental operation for the analysis and exploration of data graphs. In this paper, we address the problem of efficiently finding homomorphic matches for hybrid patterns, where each edge may be mapped either to an edge or to a path, thus allowing for higher expressiveness and flexibility in query formulation. We design a novel holistic graph simulation-based algorithm, called GraphMatch-Sim, which leverages simulation to precisely identify, in advance, all the graph nodes that participate in the pattern matches returned. GraphMatch-Sim can flexibly employ any reachability index as a plug-in component. Unlike existing methods, it produces no redundant intermediate results, thus achieving worst-case optimality. An extensive experimental evaluation on both real and synthetic datasets shows that our method evaluates hybrid patterns orders of magnitude faster than existing algorithms and has much better scalability.
An Evaluation of Pie Menus for System Control in Virtual Reality

While natural interaction techniques in virtual reality (VR) seem suitable for most tasks such as navigation and manipulation, force-fitting natural metaphors for system control is often inconvenient for the user. Focusing on traditional 2D techniques like pie menus and exploiting their potential in VR offers a promising approach. Given that, we design and examine the four pie menus pick ray (PR), pick hand (PH), hand rotation (HR) and stick rotation (SR), addressing usability, user experience (UX), presence, error rate and selection time. In terms of UX and usability, PH was rated significantly better compared to HR and SR; PR was rated better compared to SR. Presence was not affected by menu design. Selection times for PH were significantly reduced compared to SR. PH and PR resulted in significantly decreased error rates compared to SR and HR respectively. Based on these findings, we eventually derive implications for developers of VR applications.
The Development of the Vital Signs Tele-monitoring System for the Elderly by Using ‘UML’ Language and the Interoperability Standard ‘Continua’

Aims: We aim to develope a system allowing the telemonitoring of the elderly health status. Medical telemonitoring can make life easy and safe for elderly. The goal of this project is the development of a medical telemonitoring application. Background: Medical telemonitoring can make life easy and safe for elderly. Objective: The goal of this project is the development of a medical telemonitoring application. Methods: In this paper we exposed the different steps of the developing of a medical telemonitoring system designed for the elderly. We studied the medical needs and the system specifications. We used the UML language. Then we detailed the designed system with a total respect to the standard for the interoperability of connected medical equipment, Continua. We presented printed screens of the realized interfaces. Results: We realized an application based on web development, more specifically development of a management application for medical telemonitoring. Conclusion: In terms of perspectives, we aim to integrate security protocols in the developed system, integrate the data sent from the sensors into an E, H &amp; R (HER) and send the patient file to a H, I &amp; S (HIS).
Integrating QR Code-Based Approach to University e-Class System for Managing Student Attendance

Deloitte Insight defines that the Industrial Revolution 4.0 era mainly consists of four domains, namely big data analytics (BDA), artificial intelligence (AI), automation/robotics and the Internet of things (IoT). This research presents one application of IoT system toward automating students’ class attendance capturing, while embedding it with the university’s Total Campus Information System. In university class environment, capturing students’ attendance is one of the fundamental factors in assessing overall student performance. Currently, with new trend of the IoT and the Industrial Revolution 4.0 (IR4.0), the application of class attendance system, namely e-Class system, is used in ensuring that students attend classes consistently, as well as keeping track of student academic performance. This newly automated QR code-based system is eliminating the time wasted to find a student name, signing and mistakenly signaturing at a wrong name. The development of the system comprises the Student Management Academic Performance (SMAP) online platform, Total Campus Information System (TCIS) and QR code as the newly developed interface for student login into class attendance using the school intranet. The objective of this paper is to improve the processes of capturing students’ attendance in class, promote systematic and more organized attendance recording and enhance the requirement of attendance compliance of the university academic guideline in which requiring 80% attendance. One significant impact of this e-Class system, compliance to class attendance has been found to be positively significant, students becoming more alert on their attendances and rate of attendance has been pushed to high level of 96%. The university top management has decided that the system has to be implemented across all teaching faculties in two of its main campuses due to its seamless approach, relatively low cost and compatibility with the existing technology.
An Evaluation on Investment of Research Funds with a Neural Network Algorithm in “Double First-Class” Universities

In the current context of the establishment of world-class universities and disciplines in China, this study examined the investment of research funds at universities. First, six variables were selected as evaluation indicators from the perspective of fixed assets, teaching configuration, research instruments, and the number of books in libraries. Seventy-two universities were investigated from 2013 to 2017. Second, an evaluation system was constructed using the BP (backpropagation) neural network method and its applicability was verified. Finally, by adjusting the six indicators, the investment of university research funds could be adjusted and predicted to provide a reference for the construction of “first-class” universities and disciplines.
A Note on Geometric Calibration of Multiple Cameras and Projectors

Geometric calibration of cameras and projectors is an essential step that must be performed before any imaging system can be used. There are many well-known geometric calibration methods for calibrating systems comprised of multiple cameras, but simultaneous geometric calibration of multiple projectors and cameras has received less attention. This leaves unresolved several practical issues which must be considered to achieve the simplicity of use required for real world applications. In this work we discuss several important components of a real-world geometric calibration procedure used in our laboratory to calibrate surface imaging systems comprised of many projectors and cameras. We specifically discuss the design of the calibration object and the image processing pipeline used to analyze it in the acquired images. We also provide quantitative calibration results in the form of reprojection errors and compare them to the classic approaches such as Zhang's calibration method.
Square‐root cubature Kalman filter‐based vector tracking algorithm in GPS signal harsh environments

In a vector tracking loop (VTL) architecture, non-linearities exist in discriminator functions and pseudo-range/pseudo-range rate measurement expressions. Generally, normalisation functions are used in discriminators to export the desired code phase or carrier frequency error and the extended Kalman filter is adopted to estimate receiver's states. This process could be accurate enough when the code phase or carrier frequency error approaches zero in the signal moderate environment but begins to distort due to non-linearity when the tracking errors become large in harsh situations. This finally narrows the applicable range of VTL. To overcome this issue, a square-root cubature Kalman filter (CKF)-based VTL is designed in this study. The discriminator functions are employed directly as measurements of navigation filter, and the non-linear expressions of discriminator functions in terms of the receiver's position, velocity, and time states are derived without normalisation. Then the CKF, which is competitive in high-dimensional non-linear systems, is employed in its square-root version to estimate the position, velocity, acceleration, and time states of the receiver. Comparison trial results between traditional and proposed VTL illustrate that the proposed algorithm can not only keep a superior tracking accuracy but also improves the tracking stability of VTL in <20 dB-Hz signal harsh circumstances.
Convergence Analysis of Multilayer BP Neural Network with Momentum Term

Abstract In this paper, we analyze the convergence of a back-propagation (BP) neural network with momentum term containing multiple hidden layers. When the learning rate is constant and the momentum coefficient is adaptively changed under certain conditions, we give both the weak and strong convergence results of the algorithm, and give corresponding theoretical proofs for both convergence results.
Containers Runtimes War: A Comparative Study

Docker has revolutionized the cloud by popularizing containerization, changing the habits of developers and their tools, introducing new paradigms such as microservices and serverless computing, facilitating CI/CD pipelines and packaging techniques, inspiring Infrastructure as Code (IaC) and pushing the Software Defined Everything (SDx. Docker is omnipresent, on our laptops, servers, storage arrays and even on IoT devices. Docker had also the merit of defining standards such as the OCI initiative. Despite all this, can we trust this technology and deploy it widely in our private or public clouds? Given the few vulnerabilities of Docker but badly impactful, many Cloud players (Google, Amazon, RedHat, IBM, Intel, VMware among others) have worked on containerized house technologies made public. Four types of releases resulted: containerization based on OS virtualization, containerization based on micro VMs, syscalls virtualization and finally the Unikernels approach. In this article we will try to briefly present the recent containerization technologies respecting the OCI standards with a comparative study of performance. The purpose of this paper is to provide elements that help in decision-making for the choice of the most suitable container engine.
Supporting Collaborative Modeling via Natural Language Processing

Engineering large-scale systems requires the collaboration among experts who use different modeling languages and create multiple models. Due to their independent creation and evolution, these models may exhibit discrepancies in terms of the domain concepts they represent. To help re-align the models without an explicit synchronization, we propose a technique that provides the modelers with suggested concepts that they may be interested in adding to their own models. The approach is modeling-language agnostic since it processes only the text in the models, such as the labels of elements and relationships. In this paper, we focus on determining the similarity of compound nouns, which are frequently used in conceptual models. We propose two algorithms, that make use of word embeddings and domain models, respectively. We report an early validation that assesses the effectiveness of our similarity algorithms against state-of-the-art machine learning algorithms with respect to human judgment.