Reviewerly/exHarmony huggingface dataset:
QuGIT: A numerical toolbox for Gaussian quantum states

Simulating quantum states on a classical computer is hard, typically requiring prohibitive resources in terms of memory and computational power. Efficient simulation, however, can be achieved for certain classes of quantum states, in particular the so-called Gaussian quantum states of continuous variable systems. In this work we introduce QuGIT - a python numerical toolbox based on symplectic methods specialized in efficiently simulating multimode Gaussian states and operations. QuGIT is exact, requiring no truncation of Hilbert space, and provides a wide range of Gaussian operations on arbitrary Gaussian states, including unitaries, partial traces, tensor products, general-dyne measurements, conditional and unconditional dynamics. To illustrate the toolbox, several examples of usage relevant to quantum optics and optomechanics are described. Program Title: QuGIT: Quantum Gaussian Information Toolbox CPC Library link to program files: https://doi.org/10.17632/tp4s84yc5d.1 Developer's repository link: https://github.com/IgorBrandao42/Quantum-Gaussian-Information-Toolbox Licensing provisions: CC By 4.0 Programming language: Python External routines/libraries: NumPy, SciPy Nature of problem: Conditional and unconditional dynamics of Gaussian quantum systems. Solution method: Numerical simulation of symplectic representation of Gaussian states. Additional comments including restrictions and unusual features: Problems must deal with Gaussian states following Gaussian preserving dynamics.
Learning Typed Rules over Knowledge Graphs

Rule learning from large datasets has regained extensive interest as rules are useful for developing explainable approaches to many applications in knowledge graphs. However, existing methods for rule learning are still limited in terms of scalability and rule quality. This paper presents a new method for learning typed rules by employing entity class information. Our experimental evaluation shows the superiority of our system compared to state-of-the-art rule learners. In particular, we demonstrate the usefulness of typed rules in reasoning for link prediction.
Design and Flight Testing of a Two DOF Decoupled Pantograph Aerial Manipulator for Commercial UAVs

Aerial manipulation devices have been increasingly tailored to use at the limit of unmanned aerial vehicle (UAV) technology. The utility value of affordable off-the shelf UAVs for aerial manipulation is relatively unexplored as they often do not meet the performance requirements of coupled manipulator design systems. This paper presents the design and validation of a balanced pantograph manipulator prototype for decoupled use, evaluated with bench tests and and flight tests on a quadrotor UAV. The design exhibits two degrees of freedom (DOF) with a larger range of motion than most preceding decoupled manipulators. The paper also discusses an analytical model which maps the centre of gravity (CG) of all the links of the device in its operational range, allowing fine-tuning of a compact counter mass assembly which operates mechanically to achieve a net-zero stability impact on the UAV. This primary function is ideal for close quarters inspection and contact sensing tasks which engage passively with the environment. Results show that the decoupled pantograph manipulator has no measurable stability impact on the UAV, is appropriate for the use cases described and shows promise for development into a coupled design platform.
Bayesian framework selection for hyperspectral image denoising

• Mixture noise reduction as a key pre-process improves the HSI images’ quality • Mixture noise modeling is a challenge and should be close to the real noise • Proposed mixture noise model leads to Bayesian framework selection • Proposed method used low-rank factorization, total variation, L1, and nuclear norm • Devised problem is solved by augmented Lagrange multiplier and proximal gradient Mixture noise reduction is a key pre-process that improves the hyperspectral images’ (HSI) quality and prepares them for the next processes. Mixture noise should be modeled as similar as possible to the real HSI noise model, which is a challenge for noise removal methods. These use noise model simplification assumptions that can take the method performance away from real scenarios. This paper adopts a new general model for the mixture noise which leads to one model selection framework. The optimization problem of the proposed method is formed using Bayesian risk in five different models and promoted to total variation regularized low-rank matrix factorization. The devised optimization problem is solved using augmented Lagrange multiplier and proximal gradient algorithms. Also, we compared the proposed method with other state-of-the-art methods to reduce the mixture noise of HSI, which is a combination of Gaussian (i.i.d or non-i.i.d), and sparse (e.g., stripe, deadline, impulse) noises. The results obtained on both real and synthetic HSI data sets show the proposed method’s performance superiority to other competing methods in both visual comparisons and quantitative evaluations.
Automation of Asset Inventory for Cyber Security: Investigation of Event Correlation-Based Technique

Asset inventory is one of the essential steps in cyber security analysis and management. It is required for security risk identification. Current information systems are large-scale, heterogeneous, and dynamic. This complicates manual inventory of the assets as it requires a lot of time and human resources. At the same time, an asset inventory should be continuously repeated because continuous modifications of system objects and topology lead to changes in the cyber security situation. Thus, a technique for automated identification of system assets and connections between them is required. The paper proposes a technique for automated inventory of assets and connections between them in different organizations. The developed technique is constructed based on event correlation methods, namely linking the system events to each other. The essence of the technique consists of the investigation of event characteristics and identifying the characteristics that arise solely together. This allows determining system assets via assigning event characteristics to specific asset types. The security risks depend on the criticality of the assets; thus, a discussion of automated calculation of the outlined assets’ criticality is provided. Outlined system objects and topology can be further used for restoring possible attack paths and security assessment. The applicability of the developed technique to reveal object properties and types is demonstrated in the experiments.
Pipetting in virtual reality can predict real-life pipetting performance.

Can a virtual reality (VR) simulation promote acquisition of scientific skills with real-life practicability?In order to answer this question, we conducted (I) an online study (N = 126) and (II) a field study at a high school (N = 47).Study I focused on the instructional design of VR by comparing the effects of different pedagogical agents on acquiring pipetting skills.We found no significant differences between the conditions, that is, it did not seem to make a difference whether the pedagogical agent was present or not, or if it demonstrated the procedure or not.Study II focused on transfer of skills learned in VR to real-life with the addition of a control group who were taught by a real-life instructor.The results indicated that performance in VR can predict performance on a real-life transfer test.However, comparisons between the two groups showed that the students who received virtual training made more errors, experienced more extraneous cognitive load, and learned less compared to the students who were taught by the real-life instructor.Across both studies, all students experienced an increase in self-efficacy from prior to after the intervention, although the students taught by the real-life instructor experienced the largest increases in Study II.Hence, VR should not replace traditional ways of teaching scientific procedures.Rather, it can be a complement to traditional teaching that can increase accessibility.
Covert sensing using floodlight illumination

We present an active covert phase sensing scheme using floodlight illumination with an amplified spontaneous emission (ASE) source. We show that the performance of an ASE source is substantially superior compared to a laser source.
Development of Historical Learning Media Based on Virtual Reality of The National Awakening Museum

In history learning, visits to locations such as museums or other historical places in the pandemic Covid-19 situation are minimal. However, learning activities must be continued, and the existing conventional learning media in the pandemic scenario are deemed less to provide the experience. Based on previous learning experiences, an obligation to transfer knowledge from museum managers, and technological developments related to virtual reality, supports an idea to develop a museum national awakening virtual reality. This VR application is expected to become an alternative historical learning medium. This study aims to create a museum national awakening VR based on 360-degree images of the museum location. The multimedia development life cycle (MDLC) is chosen as the method used in developing the proposed VR application. The procedure consists of six steps: defining the concept based on on-site observation and interviews with the manager, creating the design, collecting the material, assembling materials and interactions with code, testing the proposed application, and distributing the system. The proposed virtual tour application has been successfully developed following the methodological principles chosen. It is also supported by a series of observations in the field using a cognitive walkthrough approach. The application was then reviewed by five experts: historians, multimedia, and learning experts. The location detailing is perfectly captured, including all the objects shown in each museum room. The quiz feature also strengthens the VR application as the learning medium, but it needs to be developed further in the evaluation section.
A subspace code of size &lt;inline-formula&gt;&lt;tex-math id="M1"&gt;\begin{document}$ \bf{333} $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt; in the setting of a binary &lt;inline-formula&gt;&lt;tex-math id="M2"&gt;\begin{document}$ \bf{q} $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt;-analog of the Fano plane

We show that there is a binary subspace code of constant dimension 3 in ambient dimension 7, having minimum subspace distance 4 and cardinality 333, i.e., $ 333 \le A_2(7, 4;3) $, which improves the previous best known lower bound of 329. Moreover, if a code with these parameters has at least 333 elements, its automorphism group is in one of 31 conjugacy classes.This is achieved by a more general technique for an exhaustive search in a finite group that does not depend on the enumeration of all subgroups.
Data Collection and Visualization Application of VMware Workstation Virtualization Technology in College Teaching Management

In this study of data collection and visualization application of VMware Workstation virtualization technology in university teaching management, data processing is performed by the SQLServer database processor using VMware Workstation virtualization technology, and finally the radial-based teaching comprehensive evaluation neural network index is used to calculate the quality of teaching management to China’s teaching team is gradually balanced and the comprehensive quality of teaching team is improving; the academic atmosphere is more open; the fields and perspectives of academic research are richer; the distribution of teaching at all levels in China is more balanced, the comprehensive quality of teaching in China is improving, the system of education and scientific research in China’s colleges and universities is improving, and the ecological management system of colleges and universities is improving, and so on. It is also proposed to improve the academic level and establish a strict institutional threshold for the teaching industry in colleges and universities; to effectively implement the policy of freedom and equality in academic and scholarly circles, and improve the system of teaching, principal appointment, and evaluation in higher education institutions; to insist on implementing the principle of academic fairness and optimizing the teaching evaluation system; to innovate and implement the incentive mechanism of teaching in colleges and universities according to the characteristics of talent training of teaching academic career ability; and to establish the access system of teaching career development in colleges and universities to promote academic career development and other suggestions of big data management of teaching in colleges and universities.
Q4EDA: A Novel Strategy for Textual Information Retrieval Based on User Interactions with Visual Representations of Time Series

Knowing how to construct text-based Search Queries (SQs) for use in Search Engines (SEs) such as Google or Wikipedia has become a fundamental skill. Though much data are available through such SEs, most structured datasets live outside their scope. Visualization tools aid in this limitation, but no such tools come close to the sheer amount of information available through general-purpose SEs. To fill this gap, this paper presents Q4EDA, a novel framework that converts users’ visual selection queries executed on top of time series visual representations, providing valid and stable SQs to be used in general-purpose SEs and suggestions of related information. The usefulness of Q4EDA is presented and validated by users through an application linking a Gapminder’s line-chart replica with a SE populated with Wikipedia documents, showing how Q4EDA supports and enhances exploratory analysis of United Nations world indicators. Despite some limitations, Q4EDA is unique in its proposal and represents a real advance towards providing solutions for querying textual information based on user interactions with visual representations.
The Prediction on the Students’ Graduation Timeliness Using Naive Bayes Classification and K-Nearest Neighbor

The college quality can be seen from the level of punctuality of student graduation. The Prediction on students’ graduation timelines can be used as one of the supporting decisions to evaluate students’ performance. Currently, the Medical Laboratory Technology study program of STIKES Guna Bangsa Yogyakarta does not have tools to predict the level of students’ graduation punctuality early yet. The purpose of this study is to evaluate the application of the Naive Bayes Classification and K-Nearest Neighbor algorithms with predictive modeling of student graduation period. This study applied the academic data from students of the Medical Laboratory Technology study program for the Academic Year (TA) 2015/2016 to 2018/2019. This study utilized an experimental approach by comparing the methods of the Naive Bayes Classification (NBC) and K-Nearest Neighbor (KNN) algorithms. The validation model uses 5-fold Cross Validation, while the evaluation model uses a Confusion Matrix. The results illustrated that the prediction with NBC in this case obtained an accuracy of 96.11%, precision of 82.11% and Recall of 100.00%. Meanwhile, predictions using KNN obtained accuracy of 97.68%, precision of 100.00% and Recall of 86.11%. Thus, KNN is an algorithm with an enhanced level of accuracy to solve the case of predicting the timeliness of students’ graduation of the Medical Laboratory Technology Study Program STIKES Guna Bangsa Yogyakarta
Speaker Anonymization for Machines using Sinusoidal Model

With the widespread use of speech technologies, speaker identity/voiceprint protection has become very important. Many methods have been proposed in the literature that protects the speaker’s identity either by modifying the voice or replacing it with another speaker’s identity. Both authentication systems and humans cannot recognize the speaker’s identity in those approaches. Changing the speaker identity of original speech cannot be used for the applications in which we want to conceal speaker identity from machine authentication and, at the same time, keep the speaker’s voice as it is. Noise addition methods have been proposed in the literature to address this issue. However, adding noise to the signal increases the irritation effect on speech perception. This paper proposes a sinusoidal model-based approach that solves this issue. The proposed method does not interfere with the originality of speech but, at the same time, protects the speaker’s identity for the automatic speaker verification (ASV) system by degrading its performance. The proposed approach’s anonymized speech is tested on the ASV system for TIMIT and IITG-MV datasets, and an equal error rate (EER) is reported. Intelligence tests like short-time objective intelligibility (STOI) and mean opinion score (MOS) is also done. By taking both EER and intelligibility tests together into consideration, it is shown that the proposed approach can solve the discussed issue.
Efficient aircraft object detection based on faster R-CNN in remote sensing images

Arming at the problems of large-scale remote sensing image, complex surface background and sparse aggregation of aircraft objects in remote sensing detection, we propose an efficient remote sensing aircraft object detection algorithm based on Faster R-CNN. Firstly, we embed a lightweight aircraft object recognition network in Faster R-CNN network to filter invalid remote sensing image slices. Then, we fuse the feature map of lightweight aircraft object recognition network (LAORNet) into Faster R-CNN to enhance the semantics of aircraft objects. In addition, we add an rotation parameter to the Faster R-CNN bordering regression, so that the Faster R-CNN can predict the rotation angle of the aircraft in the image. The experimental results show that our algorithm has 86.94% AP50 and 34 fps on UCAS-AOD dataset, which has achieved competitive results.
Scalable simulation of programmable photonic integrated waveguide meshs

Programmable photonic integrated waveguide mesh (PPIWM), which is similar to the field-programmable gate array (FPGA) in electronics, can be reconfigured to form a variety of mesh topologies to provide different signal processing functions flexibly. With the development of large-scale PPIWM, multipath routing is becoming more and more complex. Automatic and efficient calculation of the transmission characteristics between arbitrary input and output ports is very essential to realize the simulation and theoretical analysis task for the PPIWMs. In this paper, a simple and effective calculation method for the transmission characteristics of PPIWMs with any scale is proposed. Compared with the available technologies, this method can greatly reduce the time of solving the total scattering matrix in the large-scale PPIWM and has the advantage of easy programming. Typical simulation examples are given to verify the effectiveness of this method. The generality and high efficiency of this method can help us analyze increasingly large and complex reconfigurable photonic circuits with a wide variety of functions.
TrustFSDV: Framework for Building and Maintaining Trust in Self-Driving Vehicles

The deployment of fully self-sufficient, self-driving vehicles (SDVs) to city roads in a diverse range of benefit-driven concepts is on the horizon where the higher levels of automation with automated driving systems (ADSs) are being installed on vehicles. The acceptance of SDVs as safety-critical systems to successfully substitute for human drivers requires a high level of trustworthiness in numerous socio-technical aspects. By the time SDVs are available to the general public with challenges and opportunities, the trustworthiness of SDVs in rapidly changing, partially observable, multiagent, stochastic, sequential, dynamic, continuous and unknown environments shall be ensured from the perspective of the stakeholders. This paper analyses the dynamics of trust in SDVs from the perspectives of all stakeholders to result in how the &#x201C;trustworthiness&#x201D; of SDVs can be ensured with mitigated risks and dangers (i.e., non-maleficence) and maximised benefits (i.e., beneficence) as a holistic view of all the dimensions and dynamics of SDVs. Besides, a framework, namely, TrustFSDV, that designates a transitional roadmap with delegated responsibilities and liabilities along with the desired performance indicators, is proposed for the stakeholders to pursue in ensuring a sufficient level of trust within the concepts of human-vehicle integration and society-vehicle harmonisation &#x2014; inducing the advancement of SDVs and acceptance of them by the society &#x2014; while the future is being revolutionised by SDVs. To conclude the key findings in this research, i) although instant trust can be gained through hard work, it can be lost readily without forward-thinking during the life-cycle of SDVs, ii) a high level of trustworthiness cannot only be achieved by manufacturers, it requires the strict collaboration of all the stakeholders with distributed responsibilities and liabilities while moulding the requirements of the stakeholders in a system under the supervision of related disciplines, iii) the manufacturers along with the press shall not ignore the real technical limitations of the SDV technology when communicating about benefits to the potential customers, and iv) drastic steps are needed to be taken concerning ethical and legal perspectives, which is perceived as the primary concern beyond technology to build and maintain a high level of trustworthiness.
Session Fidelity for ElixirST: A Session-Based Type System for Elixir Modules

This paper builds on prior work investigating the adaptation of session types to provide behavioural information about Elixir modules.A type system called ElixirST has been constructed to statically determine whether functions in an Elixir module observe their endpoint specifications, expressed as session types; a corresponding tool automating this typechecking has also been constructed.In this paper we formally validate this type system.An LTS-based operational semantics for the language fragment supported by the type system is developed, modelling its runtime behaviour when invoked by the module client.This operational semantics is then used to prove session fidelity for ElixirST.
PSTMonitor: Monitor synthesis from probabilistic session types

We present PSTMonitor, a tool for the run-time verification of quantitative specifications of message-passing applications, based on probabilistic session types. The key element of PSTMonitor is the detection of executions that deviate from expected probabilistic behaviour. Besides presenting PSTMonitor and its operation, the paper analyses its feasibility in terms of the runtime overheads it induces.
Data Science and Artificial Intelligence for Responsible Recommendations

With the advancement of data science and AI, more and more powerful and accurate recommender systems (RSs) have been developed. They provide recommendation services in various areas, including shopping, eating, travelling and entertainment. RSs have achieved a great success and benefted the society. However, most of the research on RS has focused on the improvement of the recommendation accuracy, while ignoring other important qualities, such as trustworthiness (robustness, fairness, explainability, privacy and security) and social impact (influence on users' recognition and behaviours) of the recommendations. These are important aspects and cannot be overlooked since they measure properties that determine whether the recommendation service is reliable, trustworthy and benefcial to individual users and society. In this work, responsible recommendations refer to trustworthy recommendation techniques and positive-social-impact recommendation results.