Reviewerly/exHarmony huggingface dataset:
Empirical Evaluation of Moving Target Selection in Virtual Reality Using Egocentric Metaphors

Virtual hand or pointer metaphors are among the key approaches for target selection in immersive environments. However, targeting moving objects is complicated by factors including target speed, direction, and depth, such that a basic implementation of these techniques might fail to optimize user performance. We present results of two empirical studies comparing characteristics of virtual hand and pointer metaphors for moving target acquisition. Through a first study, we examine the impact of depth on users' performance when targets move beyond and within arms' reach. We find that movement in depth has a great impact on both metaphors. In a follow-up study, we design a reach-bounded Go-Go (rbGo-Go) technique to address challenges of virtual hand and compare it to Ray-Casting. We find that target width and speed are significant determinants of user performance and we highlight the pros and cons for each of the techniques in the given context. Our results inform the UI design for immersive selection of moving targets.
State of the Practice in Application Programming Interfaces (APIs): A Case Study

Application Programming Interfaces (APIs) have become prevalent in today’s software systems and services. APIs are basically a technical means to realize the co-operation between software systems or services. While there are several guidelines for API development, the actually applied practices and challenges are less clear. To better understand the state of the practice of API development and management in the industry, we conducted a descriptive case study in four Finnish software companies: two consultancy companies developing software for their customers, and two companies developing their software products. As a result, we identified five different usage scenarios for APIs and emphasize that diversity of usage should be taken into account more explicitly especially in research. API development and technical management are well supported by the existing tools and technologies especially available from the cloud technology. This leaves as the main challenge the selection of the right technology from the existing technology stack. Documentation and usability are practical issues to be considered and often less rigorously addressed. However, understanding what kind of API management model to apply for the business context appears as the major challenge. We also suggest considering APIs more clearly a separate concern in the product management with specific practices, such as API roadmapping.
Hunting Shadows: Towards Packet Runtime-based Detection Of Computational Intensive Reversible Covert Channels

The appearance of novel ideas for network covert channels leads to an urge for developing new detection approaches. One of these new ideas are reversible network covert channels that are able to restore the original overt information without leaving any direct evidence of their appearance. Some of these reversible covert channels are based upon computational intensive operations, like for example encoding hidden information in the authentication hashes of a hash chain based one-time password. For such a covert channel implementation, the hash function has to be called repeatedly to extract the hidden message and to restore the original information.
Zero-Shot Object Detection With Transformers

Deep learning has significantly improved the precision of object detection with abundant labeled data. However, collecting and labeling sufficient data is extremely hard. Zero-shot object detection (ZSD) has been proposed to solve this problem which aims to simultaneously recognize and localize both seen and unseen objects. Recently, the transformer and its variant architectures have shown their effectiveness over conventional methods in many natural language processing and computer vision tasks. In this paper, we study the ZSD task and develop a new framework named zero-shot object detection with transformers (ZSDTR). ZSDTR consists of the head network, transformer encoder, transformer decoder, and the vision-semantic-attention trail network. We find that the transformer is very effective for improving the ability to recall unseen objects and the tail performs well for discriminating seen and unseen objects. To the best of our knowledge, our ZSDTR is the first method to use the transformer in ZSD task. Extensive experimental results on various zero-shot object detection benchmarks show that our ZSDTR outperforms the current state-of-the-art methods.
Automated Flag Qubit Insertion for Reliable Quantum Circuit Output

Quantum circuit compilers play an important role in mitigating errors of quantum circuits executed on Noisy Intermediate-Scale Quantum (NISQ) computers. The compiler maps the quantum algorithm into a physical quantum circuit that satisfies the physical constraints of NISQ architecture. However, the reliability of NISQ computers is still very challenging. In this work, we design a new class of quantum test circuits using flag qubits based on error propagation. Furthermore, we automate the test point insertion. Our approach can detect errors at different points in the circuit to assess its output reliability. The goal is to select a qubit allocation that improves the quantum circuit output fidelity. We show the effectiveness of our approach using an IBM quantum computer and a noisy simulator.
Holistic framework for evaluating and improving information security culture

Purpose The objective of this research was to propose and validate a holistic framework for information security culture evaluation, built around a novel approach, which includes technological, organizational and social issues. The framework's validity and reliability were determined with the help of experts in the information security field and by using multivariate statistical methods. Design/methodology/approach The conceptual framework was constructed upon a detailed literature review and validated using a range of methods: first, measuring instrument was developed, and then content and construct validity of measuring instrument was confirmed via experts' opinion and by closed map sorting method. Convergent validity was confirmed by factor analysis, while the reliability of the measuring instrument was tested using Cronbach's alpha coefficient to measure internal consistency. Findings The proposed framework was validated based upon the results of empirical research and the usage of multivariate analysis. The resulting framework ultimately consists of 46 items (manifest variables), describing eight factors (first level latent variables), grouped into three categories (second level latent variables). These three categories were built around technological, organizational and social issues. Originality/value This paper contributes to the body of knowledge in information security culture by developing and validating holistic framework for information security culture evaluation, which does not observe information security culture in only one aspect but takes into account its organizational, sociological and technical component.
Exploiting Causal Structure for Robust Model Selection in Unsupervised Domain Adaptation

In many real-world settings, such as healthcare, machine learning models are trained and validated on one labeled domain and tested or deployed on another, where feature distributions differ, i.e., there is <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">covariate shift</i> . When annotations are costly or prohibitive, an unsupervised domain adaptation (UDA) regime can be leveraged requiring only unlabeled samples in the target domain. Existing UDA methods are unable to factor in a model’s predictive loss based on predictions in the target domain and, therefore, suboptimally leverage density ratios of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">only the input covariates</i> in each domain. In this article, we propose a model selection method for leveraging model predictions on a target domain without labels by exploiting the domain invariance of causal structure. We assume or learn a causal graph from the source domain and select models that produce predicted distributions in the target domain that have the highest likelihood of fitting our causal graph. We thoroughly analyze our method under oracle knowledge using synthetic data. We then show on several real-world datasets, including several COVID-19 examples, that our method is able to improve on the state-of-the-art UDA algorithms for model selection.
Two-beam light with ’checkered-pattern’ photon-number distributions

Photon-number-resolved post-selection on one beam out of a correlated system of three beams with bi-partite photon-number correlations gives rise to joint photon-number distributions with the probabilities forming checkered patterns. These patterns originate in the convolution of two constituting photon-number distributions, one endowed with correlations in photon numbers, the other exhibiting anti-correlations in photon-number fluctuations. Using three twin beams of comparable intensity whose constituting beams suitably overlap on the photocathode of a photon-number-resolving iCCD camera, we experimentally as well as theoretically analyze the properties of such states as they change with the varying ratio of the correlated and anti-correlated contributions. The experimental photocount 2D histograms of the fields post-selected by the iCCD camera that are reconstructed by the maximum-likelihood approach confirm their non-classicality though the limited detection efficiency in post-selection conceals the checkered patterns. Contrary to this, the maximum-likelihood reconstruction of the experimental 3D photocount histogram similarly as a suitable 3D Gaussian fit, that reveal the states as they would be obtained by ideal post-selection, provide the photon-number distributions with the checkered patterns. The corresponding quasi-distributions of integrated intensities are determined. Nonclassical properties of the generated states are investigated using suitable non-classicality criteria and the corresponding non-classicality depths. These states with their correlations of varying intensity are prospective for two-photon excitations of atoms and molecules as well as two-photon spectroscopy.
Accelerated Primal-Dual Algorithm for Distributed Non-convex Optimization

This paper investigates accelerating the convergence of distributed optimization algorithms on non-convex problems. We propose a distributed stochastic gradient primal-dual with fixed parameters and “powerball” (DSGPA-F-PH) method to accelerate. We show that the proposed algorithm achieves the linear speedup convergence rate <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathcal{O}(1/\sqrt{nT})$</tex> for general smooth (possi-bly non-convex) cost functions. We demonstrate the efficiency of the algorithm through numerical experiments by training two-layer fully connected neural networks and convolutional neural networks on the MNIST dataset to compare with state-of-the-art distributed stochastic gradient descent (SGD) algorithms and centralized SGD algorithms.
Mineralogical and Textural Characteristics of Red Boles of Western Deccan Volcanic Province, India: Genetic and Paleoenvironmental Implications

This study presents physical, petrographical and mineralogical investigation of interbasaltic red boleRed bole within the late CretaceousCretaceous Deccan lava flows in Pune and Mahabaleshwar areas (India) to understand depositional and weathering conditions. The micromorphological study of red bolesRed bole reveals two genetic types characterized by distinct features. The first variety, with general sharp lower and upper contacts, consisting predominantly of altered basaltic fragments and volcanic glasses along with hematiteHematite, plagioclase, pyroxene (mainly augite), accessory olivine replaced by iddingsiteIddingsite, zircon, quartz, Fe–Ti opaque phases (ilmenite and magnetite), represents a volcaniclasticVolcaniclastic bole bed. The second variety of red boleRed bole, with gradational lower contact and sharp upper contact, containing basaltic clasts, alteromorphs/iddingsiteAlteromorph, abundant zeoliteZeolite and glaebulesGlaebule in groundmass of homogeneous, red, ferruginous clay, is classified as a volcanic paleosolVolcanic paleosol. They are devoid of olivine and pyroxene, with rare alteromorphsAlteromorph of glass. The clay mineral content of red bolesRed bole was investigated using XRD and VNIR. The presence of 2.21, 2.24 and 2.29 µm absorption features in reflectance spectraReflectance spectra of VNIR spectroscopy indicates a mixture of Al-smectite (montmorillonite) and Fe-smectite as the clay mineral constituents, which is confirmed by the XRD data. The presence of smectiteSmectite and hematiteHematite in both types of red bolesRed bole suggest their formation under near-neutral pH, poorly-drained and moderate weathering condition. HematiteHematite in the red boleRed bole suggests a combination of oxidative weathering and the effect of post-burial diagenesisDiagenesis. The volcaniclasticVolcaniclastic red bolesRed bole with partially to unaltered mafic minerals and glass shardsGlass shard possibly indicates limited subaerial/subaqueous alteration under oxic condition. The paleosol type red bolesRed bole with pedogenic featuresPedogenic feature indicate prolonged sub-aerial weathering of flow top basalt, indicating significant hiatus in Deccan volcanismDeccan volcanism. ZeoliteZeolite and other alteration products possibly indicate hydrothermal alteration on red boleRed bole.
Design and Evaluation of a Framework for Reciprocal Speech Interaction in Human-Robot Collaboration

Speech is a convenient hands-free communication channel where humans are already experienced users. It can implicitly create trustfulness between two operators and lead to a comfortable and natural collaborative environment. As stated in existing literature, speech interaction could increase efficiency and improve certain aspects of Human-Robot Collaboration (HRC). Anyway, speech recognition in industrial scenarios presents different challenges: the typical noisy environment can affect dramatically the interaction performance, leading to an unacceptable inaccuracy in understanding the uttered intention.In this work, we propose and evaluate a modular system for robust and natural speech interaction in challenging acoustical environments. The system has been integrated and tested in a realistic HRC scenario in which the acoustic interaction and efficiency have been evaluated. The developed framework focuses on decreasing the requirements in terms of signal-to-noise ratio, providing a methodology to evaluate the naturalness of the interaction and improvements in efficiency.The solution is designed with a modular approach, providing an easy configuration for ROS-based systems. In this way, it allows a simple integration in existing applications and future research projects, where a dual speech-based interaction can increase the overall performance of the HRC.
Optimised Multithreaded CV-QKD Reconciliation for Global Quantum Networks

Designing a practical Continuous Variable (CV) Quantum Key Distribution (QKD) system requires an estimation of the quantum channel characteristics and the extraction of secure keys based on a large number of distributed quantum signals. On standard processors, it can take hours to reconcile the required number of quantum signals. This problem is exacerbated for Low Earth Orbit (LEO) satellite CV-QKD, where the satellite flyover time is less than a few minutes. A potential solution is massive parallelisation of the classical reconciliation where a large-code block is subdivided into many shorter blocks for individual decoding. However, the penalty of this procedure on the important final secured key rate is non-trivial to determine and hitherto has not been formally analysed. In this work, we fill this important knowledge gap via detailed analyses and experimental verification of a CV-QKD sliced reconciliation protocol that uses large block-length low-density parity-check decoders. Our new solution results in a significant increase in the final key rate relative to non-optimised reconciliation. In addition, it allows for the acquisition of quantum secured messages between terrestrial stations and LEO satellites within a flyover timescale even using off-the-shelf processors. Our work allows for optimised global quantum networks secured via fundamental physics.
R-L ALGORITHM: AN APPROXIMATION ALGORITHM FOR FRACTAL SIGNALS BASED ON FRACTIONAL CALCULUS

An important characteristic of a fractal signal is that its graph is not smooth in any small interval. This indicates the difficulty of the approximation of fractal signals, because traditional approximation methods normally require some certain smoothness of the approximated function. However, recent studies have shown that fractal functions that satisfy the Hölder condition can be linearly changed in the fractal dimension of their graphs by the fractional calculus, which implies that we can use the fractional calculus to make graphs of fractal signals smoother, and then approximate these fractal signals. This paper first gives our research background and related theories of fractals and the fractional calculus, and then introduces the main research content, including the following aspects: (1) Proposing a fractal signal approximation algorithm, the R-L algorithm, explaining and deriving how to implement this algorithm. (2) Aiming at a specific fractal signal, carrying out an approximation experiment, and confirming the R-L algorithm is better than a direct approximation. (3) According to the experimental process and conclusions, introducing our future work, such as further optimization of the R-L algorithm, and showing the R-L algorithm may have the predictive capability outside the sampling interval. The R-L algorithm can effectively modify the smoothness of graphs of the fractal signal, so that more types of approximation algorithms can be selected with a better approximation effect.
Improving Neural Blind Deconvolution

The field of blind image deblurring was for a long time dominated by Maximum-A-Posteriori methods seeking the optimal pair of sharp image-blur of a suitable functional. Recently, learning-based methods, especially those based on deep convolutional neural networks, are proving effective and are receiving increasing attention by the research community. In 2020, Ren et al. proposed a deblurring method called SelfDeblur which combines the model-driven approach of traditional MAP methods and the generative power of neural nets. The method is capable of producing very high-quality results, yet it inherits some problems of MAP methods, especially possible convergence to a wrong local optimum. In this paper we propose several easy-to-implement modifications of SelfDeblur, namely suitable initialization, multiscale processing, and regularization, that improve the average performance of the original method and decrease the probability of failure.
Sangrahaka: a tool for annotating and querying knowledge graphs

We present a web-based tool Sangrahaka for annotating entities and relationships from text corpora towards construction of a knowledge graph and subsequent querying using templatized natural language questions. The application is language and corpus agnostic, but can be tuned for specific needs of a language or a corpus. The application is freely available for download and installation. Besides having a user-friendly interface, it is fast, supports customization, and is fault tolerant on both client and server side. It outperforms other annotation tools in an objective evaluation metric. The framework has been successfully used in two annotation tasks. The code is available from <a>https://github.com/hrishikeshrt/sangrahaka</a>.
Writing with the Eyes: The Effect of Age on Eye-Tracking Performance in Non-Disabled Adults and a Comparison with Bimanual Typing

Eye-tracking technology is advancing rapidly, becoming cheaper and easier to use and more robust. This has fueled an increase in its implementation for Augmentative and Alternative Communication (AAC). Nowadays, Eye-Tracking Communication Devices (ETCDs) can be an effective aid for people with disabilities and communication problems. However, it is not clear what level of performance is attainable with these devices or how to optimize them for AAC use. The objective of this observational study was to provide data on non-disabled adults’ performance with ETCD regarding (a) range of eye-typing ability in terms of speed and errors for different age groups and (b) relationship between ETCD performance and bimanual writing with a conventional PC keyboard and (c) to suggest a method for a correct implementation of ETCD for AAC. Sixty-seven healthy adult volunteers (aged 20–79 years) were asked to type a sample sentence using, first, a commercial ETCD and then a standard PC keyboard; we recorded the typing speed and error rate. We repeated the test 11 times in order to assess performance changes due to learning. Performances differed between young (20–39 years), middle-aged (40–59 years), and elderly (60–79 years) participants. Age had a negative impact on performance: as age increased, typing speed decreased and the error rate increased. There was a clear learning effect, i.e., repetition of the exercise produced an improvement of performance in all subjects. Bimanual and ETCD typing speed showed a linear relationship, with a Pearson’s correlation coefficient of 0.73. The assessment of the effect of age on eye-typing performance can be useful to evaluate the effectiveness of man-machine interaction for use of ETCDs for AAC. Based on our findings, we outline a potential method (obviously requiring further verification) for the setup and tuning of ETCDs for AAC in people with disabilities and communication problems.
Adaptive event-triggered distributed recursive filtering with stochastic parameters and faults

This paper investigates the distributed recursive filtering issue of a class of stochastic parameter systems with randomly occurring faults. An event-triggered scheme with an adaptive threshold is designed to better reduce the communication load by considering dynamic changes of measurement sequences. In the framework of Kalman filtering, a distributed filter is constructed to simultaneously estimate both system states and faults. Then, the upper bound of filtering error covariance is derived with the help of stochastic analysis combined with basis matrix inequalities. The obtained condition with a recursive feature is dependent on the statistical characteristic of stochastic parameter matrices as well as the time-varying threshold. Furthermore, the desired filter gain is derived by minimizing the trace of the obtained upper bound. Finally, two simulation examples are conducted to demonstrate the effectiveness and feasibility of the proposed filtering method.
Discovering repetitive code changes in ML systems

Similar to software evolution in other software systems, ML software systems evolve with many repetitive changes. Despite some research and tooling for repetitive code changes that exist in Java and other languages, there is a lack of such tools for Python. Given the significant rise of ML software development, and that many ML developers are not professionally trained developers, the lack of software evolution tools for ML code is even more critical. To bring the ML developers’ toolset into the 21st century, we implemented an approach to adapt and reuse the vast ecosystem of Java static analysis tools for Python. Using this approach, we adapted two software evolution tools, RefactoringMiner and CPATMiner, to Python. With the tools, we conducted the first and most fine-grained study on code change patterns in 59 ML systems and surveyed 253 developers. We recommend empirically-justified, actionable opportunities for tool builders and release the tools for researchers.