Reviewerly/exHarmony huggingface dataset:
Exploration of inter-marker interactions in Tangible AR

Inter-marker interactions in marker based Augmented Reality mobile applications are limited to movement and placement. In this paper we explore multiple inter-marker interactions in the tangible AR space along with their use cases. We developed prototypes that demonstrate primarily five inter-marker interactions; namely, proximity of two or more markers, placement of makers over each other, flipping of markers, marker as a toggle and marker as a controller. These interactions are designed such that they would correlate with multiple contexts of application. To demonstrate their usage we have chosen lattice structures in Chemistry as the context. Using our prototypes and the insights from initial evaluations, we discuss the benefits and drawbacks of such interaction methods. We further outline the opportunities of using these interactions and extending these concepts in several other contexts.
Senso Scale

Data sharing in the cloud is operated by favorable scalable trends in the field. A cloud provides flexibility to store a large amount of variable data, ensuring the emergence of a promising and fortunate technique for the users. However, the growth rate of data opportunities often initiates a necessity of analyzing and defining the authorization ability for the users' access and convenience. Sharing the data with the third party also invites intruders and prying eyes. They may alter or modify the data according to their observance. This leads to getting loopholes in user accessibility, validation, and affects information utility conventions. This chapter proposes Senso Scale that focuses on division scaling of data over the sensitivity parameter, possessing the user authorization and accessibility factor. It also computes the technical depth of analyzing sensitivity range effectiveness measures on 0–100 evaluation slabs. Furthermore, performance analysis and experimental results depict data overhead reduction and a practical slab-based privacy-preserving solution “to provide maximum privacy” by integrating variable privacy measures.
"I've talked without intending to": Self-disclosure and Reciprocity via Embodied Avatar

Considering the importance of self-disclosure in building relationships, it is vital to investigate how self-disclosure is affected via avatars in virtual environments, as they are likely to become increasingly popular. Previous work has shown that using avatars increases friendliness and honesty. However, issues such as how and what types of avatars affect self-disclosure and reciprocity have not been fully explored. We conducted a study with 108 participants (54 dyads) discussing a personal topic using one of three communication media: videoconferencing, avatars with avatar-self appearance similarity, or avatars without avatar-self appearance similarity. The results showed that self-disclosure is most encouraged via the avatars without similarity. Reciprocity was formed via both types of avatars but not videoconferencing. Interestingly, although the participants disclosed more about themselves via avatars than via videoconferencing, there was no difference in their subjective experiences. It means that avatar users can disclose more about themselves without being particularly aware that they are using avatars, which suggests the usefulness of avatars to understand people's candid thoughts and feelings. Finally, we discuss the design implications for applications requiring self-disclosure.
On the development of an information system for monitoring user opinion and its role for the public

Abstract Social media services and analytics platforms are rapidly growing. A large number of various events happen mostly every day, and the role of social media monitoring tools is also increasing. Social networks are widely used for managing and promoting brands and different services. Thus, most popular social analytics platforms aim for business purposes while monitoring various social, economic, and political problems remains underrepresented and not covered by thorough research. Moreover, most of them focus on resource-rich languages such as the English language, whereas texts and comments in other low-resource languages, such as the Russian and Kazakh languages in social media, are not represented well enough. So, this work is devoted to developing and applying the information system called the OMSystem for analyzing users’ opinions on news portals, blogs, and social networks in Kazakhstan. The system uses sentiment dictionaries of the Russian and Kazakh languages and machine learning algorithms to determine the sentiment of social media texts. The whole structure and functionalities of the system are also presented. The experimental part is devoted to building machine learning models for sentiment analysis on the Russian and Kazakh datasets. Then the performance of the models is evaluated with accuracy, precision, recall, and F1-score metrics. The models with the highest scores are selected for implementation in the OMSystem. Then the OMSystem’s social analytics module is used to thoroughly analyze the healthcare, political and social aspects of the most relevant topics connected with the vaccination against the coronavirus disease. The analysis allowed us to discover the public social mood in the cities of Almaty and Nur-Sultan and other large regional cities of Kazakhstan. The system’s study included two extensive periods: 10-01-2021 to 30-05-2021 and 01-07-2021 to 12-08-2021. In the obtained results, people’s moods and attitudes to the Government’s policies and actions were studied by such social network indicators as the level of topic discussion activity in society, the level of interest in the topic in society, and the mood level of society. These indicators calculated by the OMSystem allowed careful identification of alarming factors of the public (negative attitude to the government regulations, vaccination policies, trust in vaccination, etc.) and assessment of the social mood.
An Intelligent Healthcare System for Residential Aged Care during the COVID-19 Pandemic

There are many healthcare possibilities for the elderly, such as hospitals, nursing homes, and home-based care. However, in times of COVID-19, most home-based elderly people did not have sufficient supplies or healthcare as usual. Fulfilling their desire for an independent lifestyle while protecting them from falls, sudden illness, or accidents is difficult. This study represents a smart system for coping with this problem in public healthcare. The existing methods for residential aged care (RAC), such as fall detection, focus on personal profiles and physical symptoms records or use a collaborative filtering method to notify caregivers or family members that the elderly person may be at a high level of risk. However, these methods have many limitations in times of COVID-19, including insufficient risk factors, problems gathering information from mobile sensors, and issues with handling human variability. This study proposes a new method for RAC in times of COVID-19 called the Intelligent Healthcare Agent System (IHAS), which, unlike the old system, incorporates context information, such as indoor and outdoor (IO), standing and lying (SL), and resting and moving (RM). IHAS integrates diverse mobile sensor data and utilizes artificial intelligence (AI) technologies into the research model and learning-oriented prototype system that can manage human variability. Ultimately, this study’s findings should contribute to the existing research and industrial applications of RAC, as well as offer new avenues of study in future research.
A Secured Half-Duplex Bidirectional Quantum Key Distribution Protocol against Collective Attacks

Quantum Key Distribution is a secure method that implements cryptographic protocols. The applications of quantum key distribution technology have an important role: to enhance the security in communication systems. It is originally inspired by the physical concepts associated with quantum mechanics. It aims to enable a secure exchange of cryptographic keys between two parties through an unsecured quantum communication channel. This work proposes a secure half-duplex bidirectional quantum key distribution protocol. The security of the proposed protocol is proved against collective attacks by estimating the interception of any eavesdropper with high probability in both directions under the control of the two parties. A two-qubit state encodes two pieces of information; the first qubit represents the transmitted bit and the second qubit represents the basis used for measurement. The partial diffusion operator is used to encrypt the transmitted qubit state as an extra layer of security. The predefined symmetry transformations induced by unitary in conjunction with the asymmetrical two-qubit teleportation scheme retain the protocol’s secrecy. Compared to the previous protocols, the proposed protocol has better performance on qubit efficiency.
Robust Dynamical Decoupling for the Manipulation of a Spin Network Via a Single Spin

High-fidelity control of quantum systems is crucial for quantum information processing, but is often limited by perturbations from the environment and imperfections in the applied control fields. Here, we investigate the combination of dynamical decoupling (DD) and robust optimal control (ROC) to address this problem. In this combination, ROC is employed to find robust shaped pulses, wherein the directional derivatives of the controlled dynamics with respect to control errors are reduced to a desired order. Then, we incorporate ROC pulses into DD sequences, achieving a remarkable improvement of robustness against multiple error channels. We demonstrate this method in the example of manipulating nuclear spin bath via an electron spin in the nitrogen-vacancy center system. Simulation results indicate that ROC-based DD sequences outperform the state-of-the-art robust DD sequences. Our work has implications for robust quantum control on near-term noisy quantum devices.
Active non-uniform illumination-based underwater polarization imaging method for objects with complex polarization properties

Active polarization imaging is one of the most effective underwater optical imaging methods that can eliminate the degradation of image contrast and clarity caused by macro-molecule scattering. However, the non-uniformity of active illumination and the diversity of object polarization properties may decrease the quality of underwater imaging. This paper proposes a non-uniform illumination-based active polarization imaging method for underwater objects with complex optical properties. Firstly, illumination homogenization in the frequency domain is proposed to extract and homogenize the natural incident light from the total receiving light. Then, the weight values of the polarized and non-polarized images are computed according to each pixel's degree of linear polarization (DoLP) in the original underwater image. By this means, the two images can be fused to overcome the problem of reflected light loss generated by the complex polarization properties of underwater objects. Finally, the fusion image is normalized as the final result of the proposed underwater polarization imaging method. Both qualitative and quantitative experimental results show that the presented method can effectively eliminate the uneven brightness of the whole image and obtain the underwater fusion image with significantly improved contrast and clarity. In addition, the ablation experiment of different operation combinations shows that each component of the proposed method has noticeable enhancement effects on underwater polarization imaging. Our codes are available in Code 1.
Autonomous vehicle path planning for smart logistics mobile applications based on modified heuristic algorithm

Abstract In this study, a heuristic algorithm is used to find an optimal route for smart logistics loading and unloading applications. Various environments, such as traditional building blocks, satellite images, terrain environments, and Google map environments are developed by converting into a binary occupancy grid and used to optimize the viable path in the smart mobile logistics application. The proposed autonomous vehicle (AV) route planning navigation approach is to forecast the AV’s path until it detects an imminent obstacle, at which point it should turn to the safest area before continuing on its route. To demonstrate the path navigation results of proposed algorithms, a navigational model is developed in the MATLAB/Simulink 2D virtual environment. The particle swarm optimization (PSO) method, the Bat search algorithm, and its proposed variants are used to identify a smooth and violation-free path for a given application environment. The proposed variants improve the algorithm’s effectiveness in finding a violation-free path while requiring less time complexity by using cubic spline curve interpolation and its improved constriction factor. Extensive simulation and benchmark validation results show the proposed standard PSO has a significantly shorter violation-free path, quick convergence rate and takes less time to compute the distance between loading and unloading environment locations than the cooperative coevolving PSO, Bat algorithm, or modified frequency Bat algorithms.
Vision measurement system for geometric parameters of tubing internal thread based on double-mirrored structured light

Geometric parameter measurement of tubing internal thread is critical for oil pipeline safety. In response to the shortcomings of existing methods for measuring internal thread geometric parameters, such as low efficiency, poor accuracy, and poor accessibility, this paper proposes a vision system for measuring internal thread geometric parameters based on double-mirrored structured light. Compared to previous methods, our system can completely reproduce the internal thread tooth profiles and allows multi-parameter measurement in one setup. To establish the correlation between the structural and imaging parameters of the vision system, three-dimensional (3D) optical path models (OPMs) for the vision system considering the mirror effect of the prism is proposed, which extends the scope of the optical path analysis and provides a theoretical foundation for designing the structural parameters of the vision system. Moreover, modeling and three-step calibration methods for the vision system are proposed to realize high-accuracy restoration from the two-dimensional (2D) virtual image to the actual 3D tooth profiles. Finally, a vision measurement system is developed, and experiments are carried out to verify the accuracy and measure the three geometric parameters (i.e., taper, pitch, and tooth height) of typical internal threads. Based on the validation results using the reference system, the vision measurement accuracy and efficiency are 6.7 and 120 times that of the traditional system, which verifies the measurement effectiveness and accuracy of the vision system proposed in this paper.
Yet Another Generating Method of Fluent Interfaces Supporting Flat- and Sub-chaining Styles

Researchers discovered methods to generate fluent interfaces equipped with static checking to verify their calling conventions. This static checking is done by carefully designing classes and method signatures to make type checking to perform a calculation equivalent to syntax checking. In this paper, we propose a method to generate a fluent interface with syntax checking, which accepts both styles of method chaining; flat-chaining style and sub-chaining style. Supporting both styles is worthwhile because it allows programmers to wrap out parts of their method chaining for readability. Our method is based on grammar rewriting so that we could inspect the acceptable grammar. In conclusion, our method succeeds generation when the input grammar is LL(1) and there is no non-terminal symbol that generates either only an empty string or nothing.
Threshold theorem in isolated quantum dynamics with stochastic control errors

We investigate the effect of stochastic control errors in the time-dependent Hamiltonian on isolated quantum dynamics. The control errors are formulated as time-dependent stochastic noise in the Schrödinger equation. For a class of stochastic control errors, we establish a threshold theorem that provides a sufficient condition to obtain the target state, which should be determined in noiseless isolated quantum dynamics, as a relation between the number of measurements and noise strength. The theorem guarantees that if the sum of the noise strengths is less than the inverse of computational time, the target state can be obtained through a constant-order number of measurements. If the opposite is true, the number of measurements to guarantee obtaining the target state increases exponentially with computational time. Our threshold theorem can be applied to any isolated quantum dynamics such as quantum annealing and adiabatic quantum computation. This article is part of the theme issue 'Quantum annealing and computation: challenges and perspectives'.
High-capacity quantum key distribution based on hyperentangled Bell states and hyper-encoding

According to the properties of the hyperentangled Bell state and hyper-encoding technology, this paper proposes a high-capacity quantum key distribution protocol with qudits hyper-encoded in polarization and spatial mode degrees of freedom (DOFs). In our protocol, 8 bits of information can be transmitted in one iteration. In contrast, the previous quantum key distribution protocols can only transmit up to 4 or 6 bits in one iteration. Moreover, quantum memory is unnecessary in our protocol. We analyze the relationship between the amount of eavesdropping and the success rate of eavesdropping. With the increase of eavesdropping information, the eavesdropping success probability will decrease sharply. The results show that our protocol is secure. Finally, this protocol is flexible; each qubit has two completely independent DOFs. If one DOF is destroyed, the other can still be used to transmit secure information. The protocol may be useful in the future quantum communication field.
Substation switching device identification method based on deep learning

In order to realize the real-time and efficient detection of substation switching device status, and to solve some existing problems of substation inspection system, such as the limited degree of automation and the need for human intervention to solve the problem. This paper proposes a status recognition method of substation switching device based on deep learning. The method obtains image data by the optical camera of an inspection robot, and uses the deep learning technology to analyze and detect the data. In this method, firstly, the image data of substation switching device is selected and marked as the data set of model training, then the Yolov3 target detection network is used to build an automatic status recognition model of substation switch device. Experimental results show that this method can automatically identify the substation switching device status, and the recognition accuracy reached 97%.
Energy and Accuracy Characterization of a Burst-Mode Range Sensing Approach for Smart Contact Lenses

In this paper, we present the characterization of a new range sensing approach for use in emerging smart contact lens applications. Smart contact lenses offer a promising approach to treating the most common form of vision loss by using a tunable lens to accommodate for focal errors. A range sensor is an integral component of the system because it estimates an object's distance from the user in order to determine the target focal length. We performed an empirical study with custom fabricated coils in a mock eyeball setup to understand the energy-accuracy trade-offs of a burst-mode sensing approach based on transmission and reception of square pulses between the coils. We wirelessly transmitted square pulses between the coils and estimated the range of an object by sensing the received voltages and inferring the angular relationship between the two contacts. We demonstrate a functioning range sensing approach that can be implemented with energy as low as 1.8 nJ per measurement with at least 95% accuracy.
SoK: SCA-secure ECC in software – mission impossible?

This paper describes an ECC implementation computing the X25519 keyexchange protocol on the Arm Cortex-M4 microcontroller. For providing protections against various side-channel and fault attacks we first review known attacks and countermeasures, then we provide software implementations that come with extensive mitigations, and finally we present a preliminary side-channel evaluation. To our best knowledge, this is the first public software claiming affordable protection against multiple classes of attacks that are motivated by distinct real-world application scenarios. We distinguish between X25519 with ephemeral keys and X25519 with static keys and show that the overhead to our baseline unprotected implementation is about 37% and 243%, respectively. While this might seem to be a high price to pay for security, we also show that even our (most protected) static implementation is at least as efficient as widely-deployed ECC cryptographic libraries, which offer much less protection.
Research on The Realization Path of Industry-education Integration to Promote Regional Industrial Transformation and Upgrading

The integration of industry and education has become the only way for the country to promote industrial transformation and upgrading, drawing on Bourdieu's field theory to analyze the coupling relationship between industry-education integration and industrial transformation and upgrading, and by analyzing and comparing the current situation of higher education and industrial structure development, find the practical problems of education in the transformation and upgrading of the service industry, and then put forward the realization path of industrial transformation and upgrading from the aspects of regional layout, hierarchy and professional subject category of higher education.
Optimal Values Selection of Q-learning Parameters in Stochastic Mazes

Abstract The model-free characteristic of the Q-learning algorithm, without obtaining information about the environment and being available for agents to learn by themselves, enables Q-learning to be widely applied to path planning fields. Nonetheless, the selection of parameter values will have a crucial impact on the results. In this paper, how to determine an appropriate value of learning rate and discount factor and these parameters’ effect on the overall results will be presented. The agents with different learning rate or discount factor values will perform in randomly generated mazes, the results of which will be aggregated and compared. When the learning rate equals 0.9, under the condition of setting the learning rate as variable and discount factor as invariant, the aggregated data of 0.9 can reach convergence way more quickly than in other settings (0.6, 0.3, 0.1); when the discount factor equals 0.9 and the experiment follows the unique variable principle, the aggregated data of 0.9 searches for shorter path length and faster than other groups (0.6, 0.3, 0.1); when both the learning rate and discount factor are set to 0.9 – other groups are 1.0, 0.1, and 0 – the group of 0.9 is more stable than the group of 0.1 and shows convergence, which does not appear in the group of 1.0 and 0, within 80 iterations.