Reviewerly/exHarmony huggingface dataset:
Data Usage Control

Abstract Data-driven business models are based on sharing and exchanging data. However, to establish a trustworthy and secure data exchange between different organizations, we have to tackle several challenges. Data sovereignty, for instance, is an essential prerequisite to empower data-driven business models across different organizations. The International Data Spaces provide solutions for data sovereignty to implement a secure and trustworthy data economy. In this chapter, we focus on data usage control and data provenance as building blocks to solve data sovereignty challenges. We introduce concepts and technology for realizing usage control and describe the differences between usage control and access control as well as other related concepts such as digital rights management or user managed access. We present the implementation of data sovereignty in the International Data Spaces starting from the formalization of data usage restrictions as policies (i.e., the policy specification) to the technical compliance and adherence of the data usage restrictions (i.e., the policy enforcement). In doing so, we present the transformation of data usage restrictions to machine-readable policies that can be enforced by the systems. Different technologies, such as the MY DATA Control Technologies can be used to implement the enforcement of data sovereignty in a technical manner and discuss future expansion stages of implementing data sovereignty.
Quantum Computing and Quantum Blockchain: Recent Advancements, Analysis and Future Directions

Quantum computers will make it possible to solve problems faster and more effectively than conventional computers currently can. Commercial quantum computers might be available in the next several years. Performing quantum operations on quantum registers is crucial to the building blocks of a quantum computing system. When these events occur in a system, they result in something fundamentally different from what you expect from a computer system. Using quantum physics, it is feasible that some computer jobs may be done more rapidly than they would be if the tasks were conducted using conventional Boolean logic and classical computers. This work discussed the recent developments and in-depth state of the art literature in Quantum Computing (QC) and related areas. This work primarily focuses on core concepts of Quantum computing such as Quantum mechanics, quantum gates, and quantum circuits, which serve as pillars for Quantum Computing and Quantum Information. Further, the QC-based concepts are implemented using a case study. The case study explores quantum computing techniques to address a real-life financial problem QAOA on IBM's Quantum Computer using Qiskit.
Understanding Phishing in Mobile Instant Messaging: A Study into User Behaviour Toward Shared Links

In recent years, users of Mobile Instant Messaging (MIM) apps like WhatsApp and Telegram are being targeted by phishing attacks. While user susceptibility to phishing in other media is well studied, the literature currently lacks studies on phishing susceptibility in MIM apps. This paper presents a study that offers the first insights into the susceptibility of users of MIM apps to phishing by investigating their behaviour towards shared links. Using an online survey, we collected data from 111 users of MIM apps and found that participants frequently click and forward links during instant messaging, while factors such as the user's relationship with the sender and the group context of the communication influence these behaviours. The results show that behaviours of most users towards shared links try to reduce their risk to phishing by trusting their friends, family and colleagues to protect them. This raises some interesting questions for further research on the effectiveness and reliability of their strategy.
Concreteness ratings for 62,000 English multiword expressions

Concreteness describes the degree to which a word's meaning is understood through perception and action. Many studies use the Brysbaert et al. (2014) concreteness ratings to investigate language processing and text analysis. However, these ratings are limited to English single words and a few two-word expressions. Increasingly, attention is focused on the importance of multiword expressions, given their centrality in everyday language use and language acquisition. We present concreteness ratings for 62,889 multiword expressions and examine their relationship to the existing concreteness ratings for single words and two-word expressions. These new ratings represent the first big dataset of multiword expressions, and will be useful for researchers interested in language acquisition and language processing, as well as natural language processing and text analysis.
ARCoA: Using the AR-Assisted Cooperative Assembly System to Visualize Key Information about the Occluded Partner

During component assembly, some operations must be completed by two or more workers due to the size or assembly mode. For instance, in manual riveting, two workers are positioned on either side of a steel plate, which blocks the view. Traditional collaborative approaches limit assembly efficiency and is difficult to ensure accurate and rapid interaction between workers. In this study, we developed an AR-Assisted Cooperative Assembly System (ARCoA) to address the issue. ARCoA allows users to view their partner’s key information that is occluded, including tools, gestures, orientations, and shared markers. Besides, we presented a user experiment that compared this method with the traditional approach. The results indicated that the new system could significantly improve assembly efficiency, system availability, and sense of social presence. Moreover, most users we surveyed preferred ARCoA. In the future, we will incorporate more functions and improve the accuracy of the system to solve complex multi-person collaborative problems.
Local and Public DNS Resolvers: do you trade off performance against security?

The Domain Name System (DNS) is a vital component of the Internet, used for all the operations performed over the network and, recently, also for protecting users from malicious activities. In this work, we analyze the behavior of DNS resolvers provided by three main Italian ISPs and contrast them with open, public resolvers provided by Google and Cisco. We consider two aspects. The first one is the time spent to perform a query and obtain a response from the resolvers, which has a considerable impact on the performance of most applications on the Internet. The second one is the capability to recognize domains associated with malicious activities, blocking related requests to protect users. The DNS response time is generally shorter for local resolvers since they are closer to the users. On the other hand, public resolvers are typically considered more efficient in detecting malicious domains. We performed a large number of DNS queries towards the different resolvers, both local and public, using different sets of domain names and different Internet access networks from main Italian providers. Our results confirm that the response time of local resolvers is shorter than the public ones. However, they also show that, unexpectedly, the protection level of local resolvers is largely comparable with the one of public resolvers. Consequently, you do not have to trade off security against performance. In addition, we study the impact of DNS over HTTPs, we unveil the different mechanisms implemented to block users from accessing malicious domains and assess the impact of caching on the obtained results.
Situation Awareness in Multi-User Wearable Computing Systems

The use of wearable devices in daily activities is continuously and rapidly growing. Wearable technology provides seamless sensing, monitoring, multimodal interaction, without continuous manual intervention and effort for users. These devices support the realization of novel applications in many domains, from healthcare to security and entertainment, improving the quality of life of users. The situation awareness paradigm allows wearable computing systems to be aware of what is happening to users and in the surrounding environment, supporting automatic smart adaptive behaviors based on the identified situation. Although situation-aware wearable devices have recently attracted a lot of attention, there is still a lack of methodological approaches and references models for defining such systems. In this paper, we propose a reference architecture for situation-aware wearable computing systems grounded on Endsley's SA model. A specialization of the architecture in the context of multi-user wearable computing systems is also proposed to support team situation awareness. An illustrative example shows a practical instantiation of the architecture in the context of contact tracing using smart sensorized face masks.
On Nxt Proof of Stake Algorithm: A Simulation Study

In this paper, we present a simulation study of a Proof of Stake (PoS) consensus algorithm used in a public blockchain called Nxt. We first provide an overview of Nxt and its PoS consensus algorithm design, and introduce a concise mathematical analysis of the Nxt PoS algorithm. We then present an experimental study on the Nxt PoS behavior in a simulated environment and in a small network running Nxt private nodes. We show that the fractions of the blocks generated by forgers in the system are generally proportional to their weight in stakes provided that the weight is relatively small, which is consistent with the mathematical analysis. We consider two scenarios of double-spending attacks: (1) a single dominating forger with large stakes; and (2) a group of colluding forgers that collectively control large stakes. The simulation results show that the single-forger attack is more advantageous over using a pool of forgers in launching successful double-spending attacks. Finally, we propose a quota-based mechanism to limit the fraction of blocks that any forger could generate. We show that the mechanism is highly effective in mitigating the single-forger attack, but has limited success in preventing the double-spending attacks based on forging pools.
Improving extractive document summarization with sentence centrality

Extractive document summarization (EDS) is usually seen as a sequence labeling task, which extracts sentences from a document one by one to form a summary. However, extracting sentences separately ignores the relationship between the sentences and documents. One solution is to use sentence position information to enhance sentence representation, but this will cause the sentence-leading bias problem, especially in news datasets. In this paper, we propose a novel sentence centrality for the EDS task to address these two problems. The sentence centrality is based on directed graphs, while reflecting the sentence-document relationship, it also reflects the sentence position information in the document. We implicitly strengthen the relevance of sentences and documents by using sentence centrality to enhance sentence representation. Notably, we replaced the sentence position information with sentence centrality to reduce sentence-leading bias without causing model performance degradation. Experiments on the CNN/Daily Mail dataset showed that EDS models with sentence centrality significantly improved compared with baseline models.
DCT-net

This paper introduces DCT-Net, a novel image translation architecture for few-shot portrait stylization. Given limited style exemplars (~100), the new architecture can produce high-quality style transfer results with advanced ability to synthesize high-fidelity contents and strong generality to handle complicated scenes (e.g., occlusions and accessories). Moreover, it enables full-body image translation via one elegant evaluation network trained by partial observations (i.e., stylized heads). Few-shot learning based style transfer is challenging since the learned model can easily become overfitted in the target domain, due to the biased distribution formed by only a few training examples. This paper aims to handle the challenge by adopting the key idea of "calibration first, translation later" and exploring the augmented global structure with locally-focused translation. Specifically, the proposed DCT-Net consists of three modules: a content adapter borrowing the powerful prior from source photos to calibrate the content distribution of target samples; a geometry expansion module using affine transformations to release spatially semantic constraints; and a texture translation module leveraging samples produced by the calibrated distribution to learn a fine-grained conversion. Experimental results demonstrate the proposed method's superiority over the state of the art in head stylization and its effectiveness on full image translation with adaptive deformations. Our code is publicly available at https://github.com/menyifang/DCT-Net.
Disentangling random and cyclic effects in time-lapse sequences

Time-lapse image sequences offer visually compelling insights into dynamic processes that are too slow to observe in real time. However, playing a long time-lapse sequence back as a video often results in distracting flicker due to random effects, such as weather, as well as cyclic effects, such as the day-night cycle. We introduce the problem of disentangling time-lapse sequences in a way that allows separate, after-the-fact control of overall trends, cyclic effects, and random effects in the images, and describe a technique based on data-driven generative models that achieves this goal. This enables us to "re-render" the sequences in ways that would not be possible with the input images alone. For example, we can stabilize a long sequence to focus on plant growth over many months, under selectable, consistent weather. Our approach is based on Generative Adversarial Networks (GAN) that are conditioned with the time coordinate of the time-lapse sequence. Our architecture and training procedure are designed so that the networks learn to model random variations, such as weather, using the GAN's latent space, and to disentangle overall trends and cyclic variations by feeding the conditioning time label to the model using Fourier features with specific frequencies. We show that our models are robust to defects in the training data, enabling us to amend some of the practical difficulties in capturing long time-lapse sequences, such as temporary occlusions, uneven frame spacing, and missing frames.
Measurement-Device-Independent Quantum Key Agreement against Collective Noisy Channel

Quantum key agreement (QKA) permits participants to constitute a shared key on a quantum channel, while no participants can independently determine the shared key. However, existing Measurement-device-independent (MDI) protocols cannot resist channel noise, and noise-resistant QKA protocols cannot resist side-channel attacks caused by equipment defects. In this paper, we design a MDI-QKA protocol against collective-dephasing noise based on GHZ states. First, in our protocol, Alice and Bob prepare a certain number of GHZ states respectively, and then send two particles of each GHZ state to Charlie for bell measurement. Results are that Alice and Bob can obtain Bell states through entanglement exchange with the help of dishonest Charlie. Meanwhile our protocol can ensure the transmission process noise-resisted. Then, Alice and Bob encode their key components to the particle in their hands and construct logical quantum states against collective noise through additional particles and CNOT operation to implement MDI-QKA. Compared with existing MDI-QKA protocols, our protocol uses logical quantum states during particle transmission, which makes the protocol immune to collective-dephasing noise and thus improves the final key rate. Security analysis shows that our protocol can resist common insider and outsider attacks.
Law, political stability, tourism management and economic development in sub-Saharan Africa

This study complements the extant literature by assessing how the rule of law and political stability modulate tourism development dynamics (i.e. tourism receipts and tourism expenditure) to affect economic development in terms of gross domestic product (GDP) per capita. The study focuses on 47 countries in sub-Saharan Africa (SSA) with data for the period 2002–2018 and the empirical evidence is based on the Generalized Method of Moments. The study finds that: (i) the rule of law modulates both tourism receipts and tourism expenditure for overall positive effects on economic development and (ii) political stability modulates tourism receipts for an overall positive impact on economic development. Policy implications are discussed.
A Blockchain Approach for Exchanging Machine Learning Solutions Over Smart Contracts

Blockchain technology enables us to create ‘Smart’ contracts, capable of offering a reward in exchange for the services of skilled contributors, by contributing a trained machine learning solution for a particular dataset or specific code packages for aiding development in large projects. Leveraging the opportunities presented with this technology, in this paper, we present a proposal to deploy a system of Smart Contracts to facilitate the creation and fulfillment of collaborative agreements. The smart contract is used for automatically validating the solution, by evaluating the submissions in order of their arrival, and whether the quality requirements specified are met. The most critical advantage would be the impartial and fair evaluation of the work submitted by the prospective collaborators, with assurance for fair compensation for their efforts, wherein their payment would not be subject to subjective and manipulable factors and incentivize data contributors to refine the solution.
Solving Monocular Sensors Depth Prediction Using MLP-Based Architecture and Multi-Scale Inverse Attention

Monocular sensors depth prediction has received continuous attention in recent years because of its wide application in autonomous driving, intelligent system navigation and other fields. Convolutional neural networks have dominated monocular depth prediction for a long time, and the recent introduction of Transformer-based and MLP-based architectures in the field of computer vision has provided some new ideas for monocular depth prediction. However, they all have a series of problems such as high computational complexity and excessive parameters. In this paper, we propose MLP-Depth, which is a lightweight monocular depth prediction method based on hierarchical multi-stage MLP, and utilizes depth-wise convolution to improve local modeling capabilities and reduce parameters and computational costs. In addition, we also design a multi-scale inverse attention mechanism to implicitly improve the global expressiveness of MLP-Depth. Our method effectively reduces the number of parameters of monocular depth prediction network using transformer-like architectures, and extensive experiments show that MLP-Depth can achieve competitive results with fewer parameters in challenging outdoor and indoor datasets.
Script Identification in Natural Scene Text Images by Learning Local and Global Features on Inception Net

In a multi script environment Script identification is essential prior to text recognition. Compared to document images, Script identification in natural scene images becomes a more challenging task due to complex backgrounds, intricate font styles, poor image quality etc. All this is in addition to the common problem of script recognition, related to similar layouts of characters found for certain scripts. The proposed work involves fine tuning of a pretrained model of Inception net V3 on a combination of word and the constituent character images extracted from the sample images of the scene text. In this model, the relu activation function originally used with Inception net is replaced by an experimentally selected Mish activation function and a softmax layer is added at the output to have a probability distribution for possible scripts of the input word images. To identify the script of an input word image, maximum of the sums of the weighted probability values for the word image and the images of its constituent characters is considered for all the script categories. The proposed method is tested on MLe2e and CVSI-2015 datasets showing encouraging results by slightly crossing the existing benchmarks.
Intelligent tutoring systems for word problem solving in COVID-19 days: could they have been (part of) the solution?

The COVID-19 pandemic led to the lockdown of schools in many countries, forcing teachers and students to carry out educational activities remotely. In the case of mathematics, developing remote instruction based on both synchronous and asynchronous technological solutions has proven to be an extremely complex challenge. Specifically, this was the case in topics such as word problem solving, as this domain requires intensive supervision and feedback from the teacher. In this piece of research, we present an evaluation of how technology is employed in the teaching of mathematics, with particular relevance to learning during the pandemic. For that purpose, we conducted a systematic review, revealing the almost complete absence of experiments in which the use of technology is not mediated by the teacher. These results reflect a pessimistic vision within the field of mathematics education about the possibilities of learning when the student uses technology autonomously. Bringing good outcomes out of a bad situation, the pandemic crisis may represent a turning point from which to start directing the research gaze towards technological environments such as those mediated by artificial intelligence. As an example, we provide a study illustrating to what extent intelligent tutoring systems can be cost-effective compared to one-to-one human tutoring and mathematic learning-oriented solutions for intensive supervision in the teaching of word problem solving, especially appropriate for remote settings. Despite the potential of these technologies, the experience also showed that student socioeconomic level was a determining factor in the participation rate with an intelligent tutoring system, regardless of whether or not the administration guaranteed students' access to technological resources during the COVID-19 situation.