Reviewerly/exHarmony huggingface dataset:
Towards Universal Accessibility on the Web: Do Grammar Checking Tools Improve Text Readability?

Readable text is a key ingredient in a universally accessible web. WCAG2.1 recommends that text should be readable by someone with basic schooling, a criterion that is hard to quantify and implement. Writers rely on qualitative clear-language recommendations, their own experience, and tools. This study set out to investigate if one class of such tools, automatic grammar checkers, has a measurable effect on the readability of text. A controlled experiment was conducted employing 15 participants who brought a piece of their own writing to the experiment tasked with improving the text using a grammar checker. Changes in readability of the text before and after applying the grammar tool were measured. Results show that there were significant reductions in error rates by applying the grammar tool, while there were no significant effects on readability. The results suggest that other automatic tools beside grammar checkers are needed to improve readability. These results have implications for web content providers.
Relative Robustness of Quantized Neural Networks Against Adversarial Attacks

Neural networks are increasingly being moved to edge computing devices and smart sensors, to reduce latency and save bandwidth. Neural network compression such as quantization is necessary to fit trained neural networks into these resource constrained devices. At the same time, their use in safety-critical applications raises the need to verify properties of neural networks. Adversarial perturbations have potential to be used as an attack mechanism on neural networks, leading to "obviously wrong" misclassification. SMT solvers have been proposed to formally prove robustness guarantees against such adversarial perturbations. We investigate how well these robustness guarantees are preserved when the precision of a neural network is quantized. We also evaluate how effectively adversarial attacks transfer to quantized neural networks. Our results show that quantized neural networks are generally robust relative to their full precision counterpart (98.6%-99.7%), and the transfer of adversarial attacks decreases to as low as 52.05% when the subtlety of perturbation increases. These results show that quantization introduces resilience against transfer of adversarial attacks whilst causing negligible loss of robustness.
Analyzing the Effects of "People also ask" on Search Behaviors and Beliefs

This study investigates the impact of collective questions and answers displayed on Search engine result pages (SERP), known as People also ask on searchers' behaviors and beliefs. Two experiments were conducted in which participants were asked to perform health-related search tasks. In both experiments, items in People also ask were manipulated. Experiment 1 focused on the effect of question, answer, and answer's opinion. Experiment 2 focused on the effect of the alternative question, i.e., a question related to the solution that can achieve the same goal as a query. The results revealed the following. (i) Participants issued fewer queries and spent less time on a SERP when People also ask were presented. (ii) Participants were less likely to interact with a SERP when they first encounter a belief-inconsistent answer. (iii) We could not confirm the effect of People also ask on beliefs at the current state. The findings suggest that People also ask might not help mitigate confirmation bias as participants are likely to spend less effort on the search process (i.e., issue fewer queries) when they first encounter a belief-inconsistent answer unlike when they encounter a belief-inconsistent document within the search results. An additional experiment is required to validate that participants who first encounter a belief-inconsistent answer are more likely to alter their beliefs as the number of such participants was inadequate.
Mapping systemic resources in problem solving

In the wild, thinking demonstrably uses interactive processes that draw on a wide range of external resources, spanning multiple time scales. As Malafouris (2015, p. 361) puts it, “cognition is not a within property; it is an in-between process”. Interactive processes configure extended systems within which each human agent is embedded. Yet much research on higher cognition, such as problem solving, reflects an implicit but deep commitment to methodological individualism that casts the agent as the ontological locus of cognition, and largely dictates the nature of the research enterprise. Thus, tasks to measure capacities and gauge reasoning performance are designed in a manner that reduces or eliminates the possibility of interacting with the problem presentation; if thinking takes place in the head, there is no need or reason to engineer procedures wherein agents can interact with the task's physical constituents. Conversely, a methodological interactivism forces one to acknowledge the participative yet not all-encompassing role of capacities such as working memory and thinking dispositions; it also encourages the granular mapping of the cognitive ecosystem from which new ideas emerge. To adopt an interactivist perspective is thus to focus on the cognitive resources of the extended system inviting a careful description of how these resources are dynamically configured over time and space to promote the development of new ideas in problem solving. In turn, a systemic perspective encourages the development of interventions that promote cognitive performance through the optimisation of systemic rather than individualist cognitive resources.
Mental Model Mapping Method for Cybersecurity

Visualizations can enhance the efficiency of Cyber Defense Analysts, Cyber Defense Incident Responders and Network Operations Specialists (Subject Matter Experts, SME) by providing contextual information for various cybersecurity-related datasets and data sources. We propose that customized, stereoscopic 3D visualizations, aligned with SMEs internalized representations of their data, may enhance their capability to understand the state of their systems in ways that flat displays with either text, 2D or 3D visualizations cannot afford. For these visualizations to be useful and efficient, we need to align these to SMEs internalized understanding of their data. In this paper we propose a method for interviewing SMEs to extract their implicit and explicit understanding of the data that they work with, to create useful, interactive, stereoscopically perceivable visualizations that would assist them with their tasks.
A framework for data security, privacy, and trust in “consumer internet of things” assemblages in South Africa

Abstract The Internet of Things (IoT) brings opportunities and challenges to humankind. IoT technology makes it possible to connect all of a person's devices to create a smart ecosystem. However, IoT has also raised issues of data security, privacy, and trust to the consumer. In a world where cybercriminals have enormously increased, people need to be aware of the advantages, and the risks that come with these technological advances. This study conducted a literature review to develop a framework to address the issues of data security, privacy, and trust in consumer IoT (CIoT). The study suggested an integrated and holistic framework that promotes safer adoption of CIoT. The researchers concluded that for CIoT to thrive, safety is crucial, and all the stakeholders in the IoT assemblage need to ensure the protection of consumers. The suggested framework may assist in the protection of consumers of IoT, data trust, as well as trust in the CIoT assemblage. It covers the concerns, experiences, and adaptations of consumers of IoT regarding data security, privacy, and trust concerning technology and privacy legislation in South Africa.
A New Data-Based Dust Estimation Unit for PV Panels

Solar photovoltaic (PV) is playing a major role in the United Arab Emirates (UAE) smart grid infrastructure. However, one of the challenges facing PV-based energy systems is the dust accumulation on solar panels. Dust accumulation on solar panels results in a high degradation in the output power. The UAE has low intensity rainfall and wind velocity; therefore solar panels must be cleaned manually or using automated cleaning methods. Estimating dust accumulation on solar panels will increase the output power and reduce maintenance costs by initiating cleaning actions only when required. In this paper, the impact of natural dust accumulation on solar panels is investigated using field measurements and regression modeling. Experimental data were collected under various real weather conditions and controlled levels of dust. Moreover, this paper proposes a data-driven approach based on machine learning to estimate the accumulated dust level on solar panels. In this approach, a dust estimation unit based on a regression tree model has been developed to estimate the dust accumulation. This unit is trained using experimental records of solar irradiance, ambient temperature, and the output power generated from solar panels as well as the amount of dust at these conditions. The proposed unit is evaluated through different case studies with a random amount of dust applied to the solar panels to demonstrate the accurate performance of the proposed unit.
Localization and Cultural Adaptation on the Web: An Explorative Study in the Fashion Domain

The use of the internet and of the (mass) media have in some way technically simplified the internationalization processes of companies, reducing the time needed in order to exchange data and information and facilitating the resolution of operational issues. Within this environment, fashion plays a relevant role; in fact, a considerable part of website and e-commerce offers come from this sector. When facing new markets, fashion companies cannot only provide unique and captivating products, but they also need to offer an opportune and personalized communication, able to cross all the cultural and linguistic boundaries that might occur. In order to face this issue a well-designed localization strategy is required. According to Mele et al. [1] localization can be defined as a "cultural translation" and when applied to online contents it consists in the adaptation of different elements such as texts, images, videos, but also units of measure, sizes or calendars. The paper aims, in this sense, to address the lack of literature concerning localization practices in the online fashion environment, providing a first framework on the depiction of the different cultural values on different fashion websites. The present study is an exploratory benchmarking of the field, realized through a comparative research, based on previous studies related to the depiction of Hofstede's [2] and Hall's [3] cultural values provided by Singh et al. and Yalcin et al. [4, 5].
Large-Scale Least Squares Twin SVMs

In the last decade, twin support vector machine (TWSVM) classifiers have achieved considerable emphasis on pattern classification tasks. However, the TWSVM formulation still suffers from the following two shortcomings: (1) TWSVM deals with the inverse matrix calculation in the Wolfe-dual problems, which is intractable for large-scale datasets with numerous features and samples, and (2) TWSVM minimizes the empirical risk instead of the structural risk in its formulation. With the advent of huge amounts of data today, these disadvantages render TWSVM an ineffective choice for pattern classification tasks. In this article, we propose an efficient large-scale least squares twin support vector machine (LS-LSTSVM) for pattern classification that rectifies all the aforementioned shortcomings. The proposed LS-LSTSVM introduces different Lagrangian functions to eliminate the need for calculating inverse matrices. The proposed LS-LSTSVM also does not employ kernel-generated surfaces for the non-linear case, and thus uses the kernel trick directly. This ensures that the proposed LS-LSTSVM model is superior to the original TWSVM and LSTSVM. Lastly, the structural risk is minimized in LS-LSTSVM. This exhibits the essence of statistical learning theory, and consequently, classification accuracy on datasets can be improved due to this change. The proposed LS-LSTSVM is solved using the sequential minimal optimization (SMO) technique, making it more suitable for large-scale problems. We further proved the convergence of the proposed LS-LSTSVM. Exhaustive experiments on several real-world benchmarks and NDC-based large-scale datasets demonstrate that the proposed LS-LSTSVM is feasible for large datasets and, in most cases, performed better than existing algorithms.
An Efficient Deep Reinforcement Learning Framework for UAVs

3D Dynamic simulator such as Gazebo has become a popular substitution for unmanned aerial vehicle (UAV) because of its user-friendly in real-world scenarios. At this point, well-functioning algorithms on the UAV controller are needed for guidance, navigation, and control for autonomous navigation. Deep reinforcement learning (DRL) comes into sight as its famous self-learning characteristic. This goal-orientated algorithm can learn how to attain a complex objective or maximize along a particular dimension over many steps. In this paper, we propose a general framework to incorporate DRL with the UAV simulation environment. The whole system consists of the DRL algorithm for attitude control, packing algorithm on the Robot Operation System (ROS) to connect DRL with PX4 controller, and a Gazebo simulator that emulates the real-world environment. Experimental results demonstrate the effectiveness of the proposed framework.
Naturalistic approaches applied to AR technology: an evaluation

There is a significant body of research in “naturalistic designs” of augmented reality (AR), concerning different fields (medicine, education, arts etc). Although naturalistic approaches have the potential to enable new forms of experiencing and experimenting with AR technologies, it remains unclear how they can impact participants’ motivation. From within an evaluation of the relevant literature and research results pertaining to naturalistic approaches to AR technology, this study aims to show how naturalistic approaches can be particularly effective in increasing the usefulness of the three widely recognized types of AR (marker-based AR, markerless and location-based AR). This study presents the results of an analysis of a review of articles of the peer-reviewed literature on naturalistic approaches applied to AR, considering the advantages, disadvantages and effectiveness of the combination of naturalistic approaches with AR across various domains. In total 33 studies published in peer-reviewed journals and conferences were analyzed. The implications of this research are that naturalistic approaches applied to AR technology help to foster positive attitudes towards AR, to facilitate collaboration and to enhance the users’ social collaboration, personal development and skills in the use of AR software. Among all domains of applications of AR with naturalistic approaches (education, medicine, digital arts, cultural heritage), the domain of human-computer interaction has attracted more attention in experimental researches. Also, motion-sensing input devices are the kind of technology which appears more beneficial for these fields.
New psychoactive substances on dark web markets: From deal solicitation to forensic analysis of purchased substances

Abstract The dark web scene has been drawing the attention of law enforcement agencies and researchers alike. To date, most of the published works on the dark web are based on data gained by passive observation. To gain a more contextualized perspective, a study was conducted in which three vendors were selected on the “Dream Market” dark web marketplace, from whom subsequently several new psychoactive substances (NPS) were ordered. All transactions were documented from the initial drug deal solicitation to the final qualitative analysis of all received samples. From the selected vendors, a total of nine NPS samples was obtained, all of which were analyzed by NMR, HRMS, LC‐UV, and two also by x‐ray diffraction. According to our analyses, four of the five substances offered under already known NPS names contained a different NPS. The selected vendors therefore either did not know about their product, or deliberately deceived the buyers. Furthermore, two of three obtained samples of purportedly novel NPS were identified as already documented substances sold under a different name. However, the third characterized substance sold as “MPF‐47700” was a novel, yet uncharacterized, NPS. Finally, we received a single undeclared substance, later identified as 5F‐ADB. In addition to chemical analysis of the nine obtained NPS samples, the methodology used also yielded contextual information about the accessibility of NPS on the dark web, the associated purchase process, and the modus operandi of three NPS vendors. Direct participation in dark web marketplaces seems to provide additional layers of information useful for forensic studies.
Spatial‐channel relation learning for brain tumor segmentation

Purpose Recently, research on brain tumor segmentation has made great progress. However, ambiguous patterns in magnetic resonance imaging data and linear fusion omitting semantic gaps between features in different branches remain challenging. We need to design a mechanism to fully utilize the similarity within the spatial space and channel space and the correlation between these two spaces to improve the result of volumetric segmentation. Methods We propose a revised cascade structure network. In each subnetwork, a context exploitation module is introduced between the encoder and decoder, in which the dual attention mechanism is adopted to learn the information within the spatial space and channel space, and space interaction learning is employed to model the relation between the spatial and channel spaces. Results Extensive experiments on the BraTS19 dataset have evaluated that our approach improves the dice coefficient (DC) by a margin of 2.1, 2.0, and 1.4 for whole tumor (WT), tumor core (TC), and enhancing tumor (ET), respectively, obtaining results competitive with the state‐of‐art approaches working on brain tumor segmentation. Conclusions Context exploitation in the embedding feature spaces, including intraspace relations and interspace relations, can effectively model dependency in semantic features and alleviate the semantic gap in multimodel data. Our approach is also robust to variations in different modality.
Privacy amplification of entanglement parametric-down conversion based quantum key distribution via quantum logistic map for photon bases choice

This paper proposes a new quantum key distribution (QKD) protocol, namely the pseudo-random bases entangled-photons based QKD (PRB-EPQKD) protocol. The latest mainly focuses on the privacy amplification analysis of spontaneous parametric down conversion (SPDC) entangled photon source based QKD using quantum logistic map (QLM) as pseudo-random number generator (PRNG) for photon polarization state measurement bases choice. For this reason, the SPDC wave function is derived and the probability distribution is deduced. The comparison shows that, this probability follows Poisson distribution, implying that the photons pairs produced are independent. Based on this, the quantum bit error rate (QBER) is evaluated and consequently the secure key rate. Simulations show that this protocol is particularly efficient as regard to the maximum tolerable communication distance and the secure key size as well. The fact that, an eavesdropper must crack the chaotic and the QKD systems simultaneously in order to extract any useful information concerning the encryption key makes the key generation protocol more private as compared to existing ones.
Improving Student-System Interaction Through Data-driven Explanations of Hierarchical Reinforcement Learning Induced Pedagogical Policies

Motivated by the recent advances of reinforcement learning and the traditional grounded Self Determination Theory (SDT), we explored the impact of hierarchical reinforcement learning (HRL) induced pedagogical policies and data-driven explanations of the HRL-induced policies on student experience in an Intelligent Tutoring System (ITS). We explored their impacts first independently and then jointly. Overall our results showed that 1) the HRL induced policies could significantly improve students' learning performance, and 2) explaining the tutor's decisions to students through data-driven explanations could improve the student-system interaction in terms of students' engagement and autonomy.
Research on Information-based Teaching and its Influence on Future Education under the Background of Epidemic Situation

Affected by the COVID-19 epidemic in 2020, the traditional teaching mode is going to be broken, while "Internet + Education" is becoming a hot topic again.Presently, the traditional teaching mode has some new challenges such as virtualization of teaching environment, mediatization of teaching content, fragmentation of teaching resources and diversification of teaching methods.Aiming at solving these challenges, this paper first analyzes the characteristics of information-based teaching under the epidemic situation in detail.Furthermore, this paper discusses the solution of integrating information techniques with teaching in combining the advantages of "Internet + Education" and traditional classroom teaching.This research is expected to provide good opportunities and new direction for future education development.
Analysis and Correction of the Rank-Deficient Error for 2-D Mirrored Aperture Synthesis

In two-dimensional mirrored aperture synthesis (2-D MAS), the rank of the transformation matrix can affect the accuracy of the solved cosine visibilities; therefore, it has an impact on the accuracy of the reconstructed brightness temperature image. In this article, the influence of the rank deficient on the accuracy of the reconstructed brightness temperature image of 2-D MAS is discussed. An analysis of the rank-deficient error is performed by computing its impact on the radiometric accuracy for a reference scene. Two correction methods based on multiple measurements are proposed to correct the rank-deficient error. The simulations and experiments are carried out to demonstrate the effectiveness of the proposed methods.
SessionStore: A Session-Aware Datastore for the Edge

It is common for storage systems designed to run on edge datacenters to avoid the high latencies associated with geo-distribution by relying on eventually consistent models to replicate data. Eventual consistency works well for many edge applications because as long as the client interacts with the same replica, the storage system can provide session consistency, a stronger consistency model that has two additional important properties: (i) read-your-writes, where subsequent reads by a client that has updated an object will return the updated value or a newer one; and, (ii) monotonic reads, where if a client has seen a particular value for an object, subsequent reads will return the same value or a newer one. While session consistency does not guarantee that different clients will perceive updates in the same order, it nevertheless presents each individual client with an intuitive view of the world that is consistent with the client's own actions. Unfortunately, these consistency guarantees break down when a client interacts with multiple replicas housed on different datacenters over time, either as a result of application partitioning, or client or code mobility. SessionStore is a datastore for fog/edge computing that ensures session consistency on a top of otherwise eventually consistent replicas. SessionStore enforces session consistency by grouping related data accesses into a session, and using a session-aware reconciliation algorithm to reconcile only the data that is relevant to the session when switching between replicas. This approach reduces data transfer and latency by up to 90% compared to full replica reconciliation.