Reviewerly/exHarmony huggingface dataset:
A Large-Scale Behavioral Analysis of the Open DNS Resolvers on the Internet

Open DNS resolvers are resolvers that perform recursive resolution on behalf of any user. They can be exploited by adversaries because they are open to the public and require no authorization to use. Therefore, it is important to understand the state of open resolvers to gauge their potentially negative impact on the security and stability of the Internet. In this study, we conducted a comprehensive probing over the entire IPv4 address space and found that more than 3 million IP addresses of open resolvers still exist in the wild. Moreover, we found that many of them work in a way that deviates from the standard. More importantly, we found that many open resolvers answer queries with incorrect, even malicious, responses. Contrasting to results obtained in 2013, we found that while the number of open resolvers has decreased significantly, the number of resolvers providing incorrect responses is almost the same, while the number of open resolvers providing malicious responses has increased, highlighting the prevalence of their threat. Through an extended analysis, we also empirically show that the use of forwarders in the open resolver ecosystem and the possibility that incorrect or malicious responses can be manipulated by these forwarders.
A Comparative Study of Two Nature-Inspired Algorithms for Routing Optimization

The paper presents and compares two algorithms inspired by nature - genetic and ant. They were applied to the problem of determining the optimal route. After the implementation of the algorithms, tests were carried out and the results were correct from the point of view of the solution sought. Then the results were analyzed and the advantages and disadvantages of both algorithms were indicated. The article helps to better understand which of the analyzed algorithms is better suited to specific tasks.
BYOD Security: A Study of Human Dimensions

The prevalence and maturity of Bring Your Own Device (BYOD) security along with subsequent frameworks and security mechanisms in Australian organisations is a growing phenomenon somewhat similar to other developed nations. During the COVID 19 pandemic, even organisations that were previously reluctant to embrace BYOD have been forced to accept it to facilitate remote work. The aim of this paper is to discover, through a study conducted using a survey questionnaire instrument, how employees practice and perceive the BYOD security mechanisms deployed by Australian businesses which can help guide the development of future BYOD security frameworks. Three research questions are answered by this study - What levels of awareness do Australian businesses have for BYOD security aspects? How are employees currently responding to the security mechanisms applied by their organisations for mobile devices? What are the potential weaknesses in businesses IT networks that have a direct effect on BYOD security? Overall, the aim of this research is to illuminate the findings of these research objectives so that they can be used as a basis for building new and strengthening existing BYOD security frameworks in order to enhance their effectiveness against an ever-growing list of attacks and threats targeting mobile devices in a virtually driven work force.
Tunable spin-glass optical simulator based on multiple light scattering

The race to heuristically solve non-deterministic polynomial-time (NP) problems through efficient methods is ongoing. Recently, optics was demonstrated as a promising tool to find the ground state of a spin-glass Ising Hamiltonian, which represents an archetypal NP problem. However, achieving completely programmable spin couplings in these large-scale optical Ising simulators remains an open challenge. Here, by exploiting the knowledge of the transmission matrix of a random medium, we experimentally demonstrate the possibility of controlling the couplings of a fully connected Ising spin system. By further tailoring the input wavefront we showcase the possibility of modifying the Ising Hamiltonian both by accounting for an external magnetic field and by controlling the number of degenerate ground states and their properties and probabilities. Our results represent a relevant step toward the realisation of fully-programmable Ising machines on thin optical platforms, capable of solving complex spin-glass Hamiltonians on a large scale.
Correcting Diacritics and Typos with a ByT5 Transformer Model

Due to the fast pace of life and online communications and the prevalence of English and the QWERTY keyboard, people tend to forgo using diacritics, make typographical errors (typos) when typing in other languages. Restoring diacritics and correcting spelling is important for proper language use and the disambiguation of texts for both humans and downstream algorithms. However, both of these problems are typically addressed separately: the state-of-the-art diacritics restoration methods do not tolerate other typos, but classical spellcheckers also cannot deal adequately with all the diacritics missing. In this work, we tackle both problems at once by employing the newly-developed universal ByT5 byte-level seq2seq transformer model that requires no language-specific model structures. For a comparison, we perform diacritics restoration on benchmark datasets of 12 languages, with the addition of Lithuanian. The experimental investigation proves that our approach is able to achieve results (> 98%) comparable to the previous state-of-the-art, despite being trained less and on fewer data. Our approach is also able to restore diacritics in words not seen during training with > 76% accuracy. Our simultaneous diacritics restoration and typos correction approach reaches > 94% alpha-word accuracy on the 13 languages. It has no direct competitors and strongly outperforms classical spell-checking or dictionary-based approaches. We also demonstrate all the accuracies to further improve with more training. Taken together, this shows the great real-world application potential of our suggested methods to more data, languages, and error classes.
Quantum-classical distance as a tool to design optimal chiral quantum walks

Continuous-time quantum walks (CTQWs) provide a valuable model for quantum transport, universal quantum computation and quantum spatial search, among others. Recently, the empowering role of new degrees of freedom in the Hamiltonian generator of CTQWs, which are the complex phases along the loops of the underlying graph, was acknowledged for its interest in optimizing or suppressing transport on specific topologies. We argue that the quantum-classical distance, a figure of merit which was introduced to capture the difference in dynamics between a CTQW and its classical, stochastic counterpart, guides the optimization of parameters of the Hamiltonian to achieve better quantum transport on cycle graphs and spatial search to the quantum speed limit without an oracle on complete graphs, the latter also implying fast uniform mixing. We compare the variations of this quantity with the 1-norm of coherence and the Inverse Participation Ratio, showing that the quantum-classical distance is linked to both, but in a topology-dependent relation, which is key to spot the most interesting quantum evolution in each case.
Time-dependent Hamiltonian Simulation of Highly Oscillatory Dynamics and Superconvergence for Schrödinger Equation

We propose a simple quantum algorithm for simulating highly oscillatory quantum dynamics, which does not require complicated quantum control logic for handling time-ordering operators. To our knowledge, this is the first quantum algorithm that is both insensitive to the rapid changes of the time-dependent Hamiltonian and exhibits commutator scaling. Our method can be used for efficient Hamiltonian simulation in the interaction picture. In particular, we demonstrate that for the simulation of the Schrödinger equation, our method exhibits superconvergence and achieves a surprising second order convergence rate, of which the proof rests on a careful application of pseudo-differential calculus. Numerical results verify the effectiveness and the superconvergence property of our method.
Quantum OPTICS and deep self-learning on swarm intelligence algorithms for Covid-19 emergency transportation

In this paper, the quantum technology is exploited to empower the OPTICS unsupervised learning algorithm, which is a density-based clustering algorithm with numerous applications in the real world. We design an algorithm called Quantum Ordering Points To Identify the Clustering Structure (QOPTICS) and demonstrate that its computational complexity outperforms that of its classical counterpart. On the other hand, we propose a Deep self-learning approach for modeling the improvement of two Swarm Intelligence Algorithms, namely Artificial Orca Algorithm (AOA) and Elephant Herding Optimization (EHO) in order to improve their effectiveness. The deep self-learning approach is based on two well-known dynamic mutation operators, namely Cauchy mutation operator and Gaussian mutation operator. And in order to improve the efficiency of these algorithms, they are hybridized with QOPTICS and executed on just one cluster it yields. This way, both effectiveness and efficiency are handled. To evaluate the proposed approaches, an intelligent application is developed to manage the dispatching of emergency vehicles in a large geographic region and in the context of Covid-19 crisis in order to avoid an important loss in human lives. A theoretical model is designed to describe the issue mathematically. Extensive experiments are then performed to validate the mathematical model and evaluate the performance of the proposed deep self-learning algorithms. Comparison with a state-of-the-art technique shows a significant positive impact of hybridizing Quantum Machine Learning (QML) with Deep Self Learning (DSL) on solving the Covid-19 EMS transportation.
Application of Pontryagin's maximum principle to quantum metrology in dissipative systems

Optimal control theory, also known as Pontryagin's Maximum Principle, is applied to the quantum parameter estimation in the presence of decoherence. An efficient procedure is devised to compute the gradient of quantum Fisher information with respect to the control parameters and is used to construct the optimal control protocol. The proposed procedure keeps the control problem in the time-invariant form so that both first-order and second-order optimality conditions derived from Pontryagin's Maximum Principle apply; the second-order condition turns out to be crucial when the optimal control contains singular arcs. Concretely we look for the optimal control that maximizes quantum Fisher information for "twist and turn" problem. We find that the optimal control is singular without dissipation but can become unbounded once the quantum decoherence is introduced. An amplitude constraint is needed to guarantee a bounded solution. With quantum decoherence, the maximum quantum Fisher information happens at a finite time due to the decoherence, and the asymptotic value depends on the specific decoherence channel and the control of consideration.
ReViVD: Exploration and Filtering of Trajectories in an Immersive Environment using 3D Shapes

We present ReViVD, a tool for exploring and filtering large trajectory-based datasets using virtual reality. ReViVD's novelty lies in using simple 3D shapes -- such as cuboids, spheres and cylinders -- as queries for users to select and filter groups of trajectories. Building on this simple paradigm, more complex queries can be created by combining previously made selection groups through a system of user-created Boolean operations. We demonstrate the use of ReViVD in different application domains, from GPS position tracking to simulated data (e.g., turbulent particle flows and traffic simulation). Our results show the ease of use and expressiveness of the 3D geometric shapes in a broad range of exploratory tasks. ReViVD was found to be particularly useful for progressively refining selections to isolate outlying behaviors. It also acts as a powerful communication tool for conveying the structure of normally abstract datasets to an audience.
Augmented reality application for laptop assembly with assembly complexity study

The assembly process increases complex and Augmented Reality (AR) is applied in the assembly process to improve the user’s experience by providing interactive instructions in real time. However, none of the previous studies developed AR application to guide assembly and investigated the complexity of the assembly. The research develops an AR-guided assembly application to provide instruction on the assembly of a laptop. The assembly complexity of the laptop was investigated. The application development involves the creation of model target, 3D models and animations, and the development of the user interface. The laptop consists of ten parts and ten assembly steps are described. Each step comprises animated 3D models and text to detail the assembly instructions comprehensively. Speech recognition has been used to navigate the assembly sequence. The result demonstrates the ability of AR application in assembly guidance with the assembly complexity up to 6.63. With the developed application, the performance of the laptop assembly can be accelerated.
Teaching Foreign Language in Transport University Using Massive Open Online Courses: Pilot Study

This study explores the issues of organizing foreign language training for future economists enrolled in undergraduate programs at transport university using massive open online courses. Modern labor market conditions and the current situation in the world require the introduction of innovations in the process of professional training and the study of foreign languages at transport university is no exception. As an innovative means of teaching a foreign language to students of Economics at transport university the author considers subject massive open online courses in a foreign language. The leading online-platforms for their relevance to the research problem were investigated. The selection of massive open online courses that meet the requirements stated in federal state educational standards, STU curricula and disciplines implemented at the department of “English Language” was carried out. The methodological model for organizing independent learning of students at transport university using massive open online courses was developed. It is suggested that the use of massive open online courses in teaching a foreign language to future economists at transport university can lead to higher educational results.
DADApy: Distance-based analysis of data-manifolds in Python

<h2>Summary</h2> DADApy is a Python software package for analyzing and characterizing high-dimensional data manifolds. It provides methods for estimating the intrinsic dimension and the probability density, for performing density-based clustering, and for comparing different distance metrics. We review the main functionalities of the package and exemplify its usage in a synthetic dataset and in a real-world application. DADApy is freely available under the open-source Apache 2.0 license.
Featherweight Scribble

This paper gives a formal definition of the protocol specification language Scribble. In collaboration with industry, Scribble has been developed as an engineering incarnation of the formal multiparty session types. In its ten years of development, Scribble has been applied and extended in manyfold ways as to verify and ensure correctness of concurrent and distributed systems, e.g. type checking, runtime monitoring, code generation, and synthesis. This paper introduces a core version of Scribble, Featherweight Scribble. We define the semantics of Scribble by translation to communicating automata and show a behavioural-preserving encoding of Scribble protocols to multiparty session type.
The counterfeit degree certificate: application of blockchain technology in higher education in India

Purpose In this work-in-progress, the application of blockchain technology (BT) in higher education in India – involving academic institutions, students and verifiers – in the wake of fake degrees (racket) being issued at a large scale from universities across the country is being investigated. This paper considers providing new methods of database system in education, and implementing the new BT in education can increase transparency and security in the education system. Design/methodology/approach This study used interview methodology (asking interview participants a series of informal questions to obtain knowledge). Findings Private and public sector organizations have many challenges to overcome in synchronizing and aligning their digital transformation efforts to enable the network effects to take hold. India’s policymakers, civil society leaders, senior business leaders and entrepreneurs are building strong clusters to help the country be the leader of the next era of the internet as a platform that helps transform human affairs for the benefit of the citizens. Executives can contribute to the digital ecosystem by creating open and collaborative cultures where knowledge and innovation are shared with the industry for the benefit of the masses and, more so, to establish quality and communication standards. They also can contribute by staying open to change, embracing digital adoption and transformation within their management models and infrastructure. It is time for institutions to rethink their processes and governance structures to become more agile and innovative players. The success of a harmonious digital identity ecosystem relies on staying ahead of the organization’s digital curve. As a first step to provide better quality in the provision of public services, Secure Key Technologies’ blockchain-based ecosystem (securekey.com) allows multiple partners to strengthen the authentication and provides identity attribute validation as a fabric of trust and as a solid foundation to embrace a new digital era. Secure Key Technologies’ vision for the future of digital identities redefines the ways both consumers and businesses approach identity verification and the sharing of key personal information. The challenges identified in the education system be it the difficulty in authenticating certifications and documents, tracking intellectual property and in accessibility can be better managed through blockchain implementation. Originality/value Digital certificates produced using blockchains are the most secure and efficient method being adopted by many countries across the globe (Grech and Camilleri, 2017). Tamper-proof blockchain records consist of three components or layers: the first contains the signed hash of the content, the second contains all the data and images and the third is the presentation and styling of the data. The verification process is relatively simple; the hash of the recipient is compared to the hash stored on the blockchain if they match the document is verified.
Ultrahyperbolic Knowledge Graph Embeddings

Recent knowledge graph (KG) embeddings have been advanced by hyperbolic geometry due to its superior capability for representing hierarchies. The topological structures of real-world KGs, however, are rather heterogeneous, i.e., a KG is composed of multiple distinct hierarchies and non-hierarchical graph structures. Therefore, a homogeneous (either Euclidean or hyperbolic) geometry is not sufficient for fairly representing such heterogeneous structures. To capture the topological heterogeneity of KGs, we present an ultrahyperbolic KG embedding (UltraE) in an ultrahyperbolic (or pseudo-Riemannian) manifold that seamlessly interleaves hyperbolic and spherical manifolds. In particular, we model each relation as a pseudo-orthogonal transformation that preserves the pseudo-Riemannian bilinear form. The pseudo-orthogonal transformation is decomposed into various operators (i.e., circular rotations, reflections and hyperbolic rotations), allowing for simultaneously modeling heterogeneous structures as well as complex relational patterns. Experimental results on three standard KGs show that UltraE outperforms previous Euclidean, hyperbolic, and mixed-curvature KG embedding approaches.
Tana, a Healthcare Chatbot to Help Patients During the COVID-19 Pandemic at a University Hospital in Argentina

A Chatbot or Conversational Agent is a computer application that simulates the conversation with a human person (by text or voice), giving automated responses to people's needs. In the healthcare domain, chatbots can be beneficial to help patients, as a complement to care by health personnel, especially in times of high demand or constrained resources such as the COVID-19 Pandemic. In this paper we share the design and implementation of a healthcare chatbot called Tana at the Hospital Italiano de Buenos Aires. Considering best practices and being aware of possible unintended consequences, we must take advantage of information and communication technologies, such as chatbots, to analyze and promote useful conversations for the health of all people.
MEGCF: Multimodal Entity Graph Collaborative Filtering for Personalized Recommendation

In most E-commerce platforms, whether the displayed items trigger the user's interest largely depends on their most eye-catching multimodal content. Consequently, increasing efforts focus on modeling multimodal user preference, and the pressing paradigm is to incorporate complete multimodal deep features of the items into the recommendation module. However, the existing studies ignore the mismatch problem between multimodal feature extraction (MFE) and user interest modeling (UIM). That is, MFE and UIM have different emphases. Specifically, MFE is migrated from and adapted to upstream tasks such as image classification. In addition, it is mainly a content-oriented and non-personalized process, while UIM, with its greater focus on understanding user interaction, is essentially a user-oriented and personalized process. Therefore, the direct incorporation of MFE into UIM for purely user-oriented tasks, tends to introduce a large number of preference-independent multimodal noise and contaminate the embedding representations in UIM. This paper aims at solving the mismatch problem between MFE and UIM, so as to generate high-quality embedding representations and better model multimodal user preferences. Towards this end, we develop a novel model, MEGCF. The UIM of the proposed model captures the semantic correlation between interactions and the features obtained from MFE, thus making a better match between MFE and UIM. More precisely, semantic-rich entities are first extracted from the multimodal data, since they are more relevant to user preferences than other multimodal information. These entities are then integrated into the user-item interaction graph. Afterwards, a symmetric linear Graph Convolution Network (GCN) module is constructed to perform message propagation over the graph, in order to capture both high-order semantic correlation and collaborative filtering signals.