Reviewerly/exHarmony huggingface dataset:
Augmented Reality Games and Presence: A Systematic Review

The sense of presence in augmented reality (AR) has been studied by multiple researchers through diverse applications and strategies. In addition to the valuable information provided to the scientific community, new questions keep being raised. These approaches vary from following the standards from virtual reality to ascertaining the presence of users' experiences and new proposals for evaluating presence that specifically target AR environments. It is undeniable that the idea of evaluating presence across AR may be overwhelming due to the different scenarios that may be possible, whether this regards technological devices-from immersive AR headsets to the small screens of smartphones-or the amount of virtual information that is being added to the real scenario. Taking into account the recent literature that has addressed the sense of presence in AR as a true challenge given the diversity of ways that AR can be experienced, this study proposes a specific scope to address presence and other related forms of dimensions such as immersion, engagement, embodiment, or telepresence, when AR is used in games. This systematic review was conducted following the PRISMA methodology, carefully analysing all studies that reported visual games that include AR activities and somehow included presence data-or related dimensions that may be referred to as immersion-related feelings, analysis or results. This study clarifies what dimensions of presence are being considered and evaluated in AR games, how presence-related variables have been evaluated, and what the major research findings are. For a better understanding of these approaches, this study takes note of what devices are being used for the AR experience when immersion-related feelings are one of the behaviours that are considered in their evaluations, and discusses to what extent these feelings in AR games affect the player's other behaviours.
Universal Quantum Multi‐Qubit Entangling Gates with Auxiliary Spaces

Abstract Universal quantum entangling gates are a crucial building block in the large‐scale quantum computation and quantum communication, and it is an important task to find simple ways to implement them. Here an effective quantum circuit for the implementation of a controlled‐NOT (CNOT) gate is constructed by introducing a non‐computational quantum state in the auxiliary space. Furthermore, the method is extended to the construction of a general n ‐control‐qubit Toffoli gate with qubit–qudit gates and single‐qudit gates. Based on the presented quantum circuits, the polarization CNOT and Toffoli gates with linear optics are designed by operating on the spatial‐mode degree of freedom of photons. The proposed optical schemes can be achieved with a higher success probability and no extra auxiliary photons are needed.
Federated learning and privacy

Building privacy-preserving systems for machine learning and data science on decentralized data.
Federated learning for violence incident prediction in a simulated cross-institutional psychiatric setting

Inpatient violence is a common and severe problem within psychiatry. Knowing who might become violent can influence staffing levels and mitigate severity. Predictive machine learning models can assess each patient's likelihood of becoming violent based on clinical notes. Yet, while machine learning models benefit from having more data, data availability is limited as hospitals typically do not share their data for privacy preservation. Federated Learning (FL) can overcome the problem of data limitation by training models in a decentralised manner, without disclosing data between collaborators. However, although several FL approaches exist, none of these train Natural Language Processing models on clinical notes. In this work, we investigate the application of Federated Learning to clinical Natural Language Processing, applied to the task of Violence Risk Assessment by simulating a cross-institutional psychiatric setting. We train and compare four models: two local models, a federated model and a data-centralised model. Our results indicate that the federated model outperforms the local models and has similar performance as the data-centralised model. These findings suggest that Federated Learning can be used successfully in a cross-institutional setting and is a step towards new applications of Federated Learning based on clinical notes
Few-shot Named Entity Recognition with Self-describing Networks

Few-shot NER needs to effectively capture information from limited instances and transfer useful knowledge from external resources.In this paper, we propose a self-describing mechanism for few-shot NER, which can effectively leverage illustrative instances and precisely transfer knowledge from external resources by describing both entity types and mentions using a universal concept set.Specifically, we design Self-describing Networks (SDNet), a Seq2Seq generation model which can universally describe mentions using concepts, automatically map novel entity types to concepts, and adaptively recognize entities ondemand.We pre-train SDNet with large-scale corpus, and conduct experiments on 8 benchmarks from different domains.Experiments show that SDNet achieves competitive performances on all benchmarks and achieves the new state-of-the-art on 6 benchmarks, which demonstrates its effectiveness and robustness.
Solving nuclear structure problems with the adaptive variational quantum algorithm

We use the Lipkin-Meshkov-Glick (LMG) model and the valence-space nuclear shell model to examine the likely performance of variational quantum eigensolvers in nuclear-structure theory. The LMG model exhibits both a phase transition and spontaneous symmetry breaking at the mean-field level in one of the phases, features that characterize collective dynamics in medium-mass and heavy nuclei. We show that with appropriate modifications, the ADAPT-VQE algorithm, a particularly flexible and accurate variational approach, is not troubled by these complications. We treat up to 12 particles and show that the number of quantum operations needed to approach the ground-state energy scales linearly with the number of qubits. We find similar scaling when the algorithm is applied to the nuclear shell model with realistic interactions in the $sd$ and $pf$ shells. Although most of these simulations contain no noise, we use a noise model from real IBM hardware to show that for the LMG model with four particles, weak noise has no effect on the efficiency of the algorithm.
Radar Target Tracking for Unmanned Surface Vehicle Based on Square Root Sage–Husa Adaptive Robust Kalman Filter

Dynamic information such as the position and velocity of the target detected by marine radar is frequently susceptible to external measurement white noise generated by the oscillations of an unmanned surface vehicle (USV) and target. Although the Sage-Husa adaptive Kalman filter (SHAKF) has been applied to the target tracking field, the precision and stability of SHAKF remain to be improved. In this paper, a square root Sage-Husa adaptive robust Kalman filter (SR-SHARKF) algorithm together with the constant jerk model is proposed, which can not only solve the problem of filtering divergence triggered by numerical rounding errors, inaccurate system mathematics, and noise statistical models, but also improve the filtering accuracy. First, a novel square root decomposition method is proposed in the SR-SHARKF algorithm for decomposing the covariance matrix of SHAKF to assure its non-negative definiteness. After that, a three-segment approach is adopted to balance the observed and predicted states by evaluating the adaptive scale factor. Finally, the unbiased and the biased noise estimators are integrated while the interval scope of the measurement noise is constrained to jointly evaluate the measurement and observation noise for better adaptability and reliability. Simulation and experimental results demonstrate the effectiveness of the proposed algorithm in eliminating white noise triggered by the USV and target oscillations.
Mining Privacy-Preserving Association Rules based on Parallel Processing in Cloud Computing

With the onset of the Information Era and the rapid growth of information technology, ample space for processing and extracting data has opened up. However, privacy concerns may stifle expansion throughout this area. The challenge of reliable mining techniques when transactions disperse across sources is addressed in this study. This work looks at the prospect of creating a new set of three algorithms that can obtain maximum privacy, data utility, and time savings while doing so. This paper proposes a unique double encryption and Transaction Splitter approach to alter the database to optimize the data utility and confidentiality tradeoff in the preparation phase. This paper presents a customized apriori approach for the mining process, which does not examine the entire database to estimate the support for each attribute. Existing distributed data solutions have a high encryption complexity and an insufficient specification of many participants' properties. Proposed solutions provide increased privacy protection against a variety of attack models. Furthermore, in terms of communication cycles and processing complexity, it is much simpler and quicker. Proposed work tests on top of a realworld transaction database demonstrate that the aim of the proposed method is realistic.
A Systematic Review of Voice Assistant Usability: An ISO 9241–11 Approach

Voice assistants (VA) are an emerging technology that have become an essential tool of the twenty-first century. The VA ease of access and use has resulted in high usability curiosity in voice assistants. Usability is an essential aspect of any emerging technology, with every technology having a standardized usability measure. Despite the high acceptance rate on the use of VA, to the best of our knowledge, not many studies were carried out on voice assistants' usability. We reviewed studies that used voice assistants for various tasks in this context. Our study highlighted the usability measures currently used for voice assistants. Moreover, our study also highlighted the independent variables used and their context of use. We employed the ISO 9241-11 framework as the measuring tool in our study. We highlighted voice assistant's usability measures currently used; both within the ISO 9241-11 framework, as well as outside of it to provide a comprehensive view. A range of diverse independent variables are identified that were used to measure usability. We also specified that the independent variables still not used to measure some usability experience. We currently concluded what was carried out on voice assistant usability measurement and what research gaps were present. We also examined if the ISO 9241-11 framework can be used as a standard measurement tool for voice assistants.
Splitting chemical structure data sets for federated privacy-preserving machine learning

Abstract With the increase in applications of machine learning methods in drug design and related fields, the challenge of designing sound test sets becomes more and more prominent. The goal of this challenge is to have a realistic split of chemical structures (compounds) between training, validation and test set such that the performance on the test set is meaningful to infer the performance in a prospective application. This challenge is by its own very interesting and relevant, but is even more complex in a federated machine learning approach where multiple partners jointly train a model under privacy-preserving conditions where chemical structures must not be shared between the different participating parties. In this work we discuss three methods which provide a splitting of a data set and are applicable in a federated privacy-preserving setting, namely: a. locality-sensitive hashing (LSH), b. sphere exclusion clustering, c. scaffold-based binning (scaffold network). For evaluation of these splitting methods we consider the following quality criteria (compared to random splitting): bias in prediction performance, classification label and data imbalance, similarity distance between the test and training set compounds. The main findings of the paper are a. both sphere exclusion clustering and scaffold-based binning result in high quality splitting of the data sets, b. in terms of compute costs sphere exclusion clustering is very expensive in the case of federated privacy-preserving setting.
Probabilistic Transformations of Quantum Resources

The difficulty in manipulating quantum resources deterministically often necessitates the use of probabilistic protocols, but the characterization of their capabilities and limitations has been lacking. We develop a general approach to this problem by introducing a new resource monotone that obeys a very strong type of monotonicity: it can rule out all transformations, probabilistic or deterministic, between states in any quantum resource theory. This allows us to place fundamental limitations on state transformations and restrict the advantages that probabilistic protocols can provide over deterministic ones, significantly strengthening previous findings and extending recent no-go theorems. We apply our results to obtain a substantial improvement in bounds for the errors and overheads of probabilistic distillation protocols, directly applicable to tasks such as entanglement or magic state distillation, and computable through convex optimization. In broad classes of resources, we strengthen our results to show that the monotone completely governs probabilistic transformations-it serves as a necessary and sufficient condition for state convertibility. This endows the monotone with a direct operational interpretation, as it can exactly quantify the highest fidelity achievable in resource distillation tasks by means of any probabilistic manipulation protocol.
Efficient Error Detection Architectures for Postquantum Signature Falcon’s Sampler and KEM SABER

Among the National Institute for Standards and Technology (NIST) postquantum cryptography (PQC) standardization Round 3 finalists (announced in 2020 and anticipated to conclude in 2022–2024), SABER and Falcon are efficient key encapsulation mechanism (KEM) and compact signature scheme, respectively. SABER is a simple and flexible cryptographic scheme, highly suitable for thwarting potential attacks in the postquantum era. Implementing SABER can be performed solely in hardware (HW) or on HW/software coprocessors. On the other hand, the compact key size, efficient design, and strong reliability proof in the quantum random oracle model (QROM) make Falcon a highly suitable signature algorithm for PQC. Although Falcon is crucial as a PQC signature scheme, the utilization of the Gaussian sampler makes it vulnerable to malicious attacks, e.g., fault attacks. This is the first work to present error detection schemes embedded efficiently in SABER as well as Falcon’s sampler architectures, which can detect both transient and permanent faults. Moreover, we implement HW design for the ModFalcon signature algorithm as well as the Gaussian sampler. These schemes are implemented on a formerly Xilinx field-programmable gate array (FPGA) family, for both SABER and Falcon variants, where we assess the error coverage and the performance. The proposed schemes incur low overhead (the area, delay, and power overheads being 22.59%, 19.77%, and 10.67%, respectively, in the worst case) while providing a high fault detection rate (99.9975% in the worst case scenario), making them suitable for high efficiency and compact HW implementations of constrained applications.
FedCDR: Privacy-preserving federated cross-domain recommendation

Cross-Domain Recommendation (CDR) aims to solve data sparsity and cold-start problems by utilizing a relatively information-rich source domain to improve the recommendation performance of the data-sparse target domain. However, most existing approaches rely on the assumption of centralized storage of user data, which undoubtedly poses a significant risk of user privacy leakage because user data are highly privacy-sensitive. To this end, we propose a privacy-preserving Federated framework for Cross-Domain Recommendation, called FedCDR. In our method, to avoid leakage of user privacy, a general recommendation model is trained on each user's personal device to obtain embeddings of users and items, and each client uploads weights to the central server. The central server then aggregates the weights and distributes them to each client for updating. Furthermore, because the weights implicitly contain private information about the user, local differential privacy is adopted for the gradients before uploading them to the server for better protection of user privacy. To distill the relationship of user embedding between two domains, an embedding transformation mechanism is used on the server side to learn the cross-domain embedding transformation model. Extensive experiments on real-world datasets demonstrate that our method achieves performance comparable with that of existing data-centralized methods and effectively protects user privacy.
Effects of interface layout design on mobile learning efficiency: a comparison of interface layouts for mobile learning platform

Purpose The purpose of this study is to explore the impact of mobile learning platforms on users' study efficiency and develop cognitive indicators to evaluate users' study efficiency on mobile learning platforms. Design/methodology/approach Layout style was the only independent factor that was investigated. A between-group experimental design was employed. Eye movement data were recorded during the experiment, following which participants were asked to complete an after-scenario questionnaire. This study evaluated the usability of the proposed new design using both subjective and objective data. The computer system usability questionnaire V3 (CSUQ) was used to measure subjective data. For the eye-tracking measure, gaze entropy, the proportion of fixation count and duration of each AOI were calculated. Gaze entropy reflects the complexity of information organization. Fixation counts and AOI duration represent the difficulty of information processing and attention distribution, respectively during the task. Findings The results indicated that interface layout presents significant effects on user's learning efficiency, usability and cognitive load. Sequential layout improved efficiency and satisfaction among participants and reduced information complexity. The results provided useful insights for designers whose goal is to improve user's learning efficiency under mobile learning scheme. Originality/value This study investigated the effects of interface layout on usability, user performance and cognitive load using subjective ratings and eye-tracking technology. Gaze entropy was used to measure the complexity of information organized by the interface design. Fixation count and duration proportion were used to identify the difficulty of information processing and distinguish users' distribution of cognitive resources. The results indicated that a vertical layout panel design was more efficient than a horizontal layout panel design. The design implications of the eye tracking indicators and research results were then summarized. This study is expected to encourage designers to optimize their design proposals using eye tracking testing.
Image retrieval of wool fabric. Part II: based on low-level color features

Color is difficult to distinguish by human vision and is described by keywords, resulting in low efficiency of wool fabric retrieval in factories at present. To obtain the process sheets of existing products and reduce the work of color measurement in sample analysis, this paper proposes an effective method based on dominant colors (DCs) and color moments (CMs) for wool fabric image retrieval. Firstly, the image was scaled to reduce computational time. Then, the hue, saturation, value color space was divided into 128 parts by the fast color quantization algorithm to extract the DCs of the image. Meanwhile, the CMs based on image partition were calculated in CIE L* a* b* color space to describe the spatial color information. Subsequently, different similarity measure methods were carried out based on the DC feature and CM feature. Finally, experiments were conducted on a wool fabric image database with 20,000 images for parameter optimization and verification. The average precision and recall were up to 87% and 44%, respectively. Experimental results show that the proposed scheme can retrieve images with the same or similar colors quickly and effectively and it outperformed other methods, providing referential assistance for the factory worker when retrieving wool fabrics.
How Efficiency Shapes Human Language

DOI of original article: https://doi.org/10.1016/j.tics.2019.02.003 (Trends in Cognitive Sciences 23, 389‒407, 2019) Due to a production error, the name of the third author was incorrectly spelled as Steven T. Piandadosi. The correct spelling is Steven P. Piantadosi. In addition, Box 2 contained a notation error. The formulization of mutual information was given as I[X : C] = H[X] ̶ H[X]C. The correction notation is I[X : C] = H[X] – H[X|C]. These errors have been corrected. How Efficiency Shapes Human LanguageGibson et al.Trends in Cognitive SciencesApril 18, 2019In BriefWe review recent research on the burgeoning topic of how language structure is shaped by principles of efficiency for communication and learning. Work in this area has infused long-standing ideas in linguistics and psychology with new precision and methodological rigor by bringing together information theory, newly available datasets, controlled experimentation, and computational modeling. We review a number of studies that focus on phenomena ranging from the lexicon through syntactic processes, and which deploy formal tools from information theory and probability theory to understand how and why language works the way that it does. Full-Text PDF
Modeling misretrieval and feature substitution in agreement attraction: A computational evaluation

We present computational modeling results based on a self-paced reading study investigating number attraction effects in Eastern Armenian. We implement three novel computational models of agreement attraction in a Bayesian framework and compare their predictive fit to the data using k-fold cross-validation. We find that our data are better accounted for by an encoding-based model of agreement attraction, compared to a retrieval-based model. A novel methodological contribution of our study is the use of comprehension questions with open-ended responses, so that both misinterpretation of the number feature of the subject phrase and misassignment of the thematic subject role of the verb can be investigated at the same time. We find evidence for both types of misinterpretation in our study, sometimes in the same trial. However, the specific error patterns in our data are not fully consistent with any previously proposed model.
Improving English-to-Indian Language Neural Machine Translation Systems

Most Indian languages lack sufficient parallel data for Machine Translation (MT) training. In this study, we build English-to-Indian language Neural Machine Translation (NMT) systems using the state-of-the-art transformer architecture. In addition, we investigate the utility of back-translation and its effect on system performance. Our experimental evaluation reveals that the back-translation method helps to improve the BLEU scores for both English-to-Hindi and English-to-Bengali NMT systems. We also observe that back-translation is more useful in improving the quality of weaker baseline MT systems. In addition, we perform a manual evaluation of the translation outputs and observe that the BLEU metric cannot always analyse the MT quality as well as humans. Our analysis shows that MT outputs for the English–Bengali pair are actually better than that evaluated by BLEU metric.