Reviewerly/exHarmony huggingface dataset:
One-Photon Solutions to the Multiqubit Multimode Quantum Rabi Model for Fast <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mrow><mml:mi>W</mml:mi></mml:mrow></mml:math> -State Generation

General solutions to the quantum Rabi model involve subspaces with an unbounded number of photons. However, for the multiqubit multimode case, we find special solutions with at most one photon for an arbitrary number of qubits and photon modes. Such solutions exist for arbitrary single qubit-photon coupling strength with constant eigenenergy, while still being qubit-photon entangled states. Taking advantage of their peculiarities and the reach of the ultrastrong coupling regime, we propose an adiabatic scheme for the fast and deterministic generation of a two-qubit Bell state and arbitrary single-photon multimode $W$ states with nonadiabatic error less than 1%. Finally, we propose a superconducting circuit design to catch and release the $W$ states, and shows the experimental feasibility of the multimode multiqubit quantum Rabi model.
An empirical analysis of machine learning frameworks for digital pathology in medical science

Abstract Digital pathology is a technology that allows pathological information created from a digital slide to be accessed, handled, and interpreted. Using optical pathology scanners, glass slides are collected and transformed to digitized glass slides that can be viewed on your computer monitor. Relevant support for education and the practice of human anatomy is offered by digital pathology. With the recent developments in digital pathology led to computer-aided diagnosis using machine learning approaches. So, machine learning frameworks assist physicians in diagnosing critical cases such as cancer, tumors, etc and improve patient management. With an ever growing number of choices, it can be hard to pick a better machine learning method for pathological data. Big potential attempts are made in this paper to research the full context of digital pathology with the specifics of how artificial intelligence has contributed to digital pathology. This review also analyzes various machine learning frameworks by providing as much information as possible and quantifying what the tradeoffs will be. This paper ultimately provides the improvements in the frameworks available that will be required in the near future applications.
Chatbot Security and Privacy in the Age of Personal Assistants

The rise of personal assistants serves as a testament to the growing popularity of chatbots. However, as the field advances, it is important for the conversational AI community to keep in mind any potential vulnerabilities in existing architectures and how attackers could take advantantage of them. Towards this end, we present a survey of existing dialogue system vulnerabilities in security and privacy. We define chatbot security and give some background regarding the state of the art in the field. This analysis features a comprehensive description of potential attacks of each module in a typical chatbot architecture: the client module, communication module, response generation module, and database module.
Differential Permutation Index DCSK Modulation for Chaotic Communication System

This letter proposes a differential permutation index differential chaos shift keying (DPI-DCSK) modulation for wireless communications. The conventional PI-DCSK has the disadvantage of wasting transmission energy and the loss of system performance. In M-ary DPI-DCSK, one chaotic sequence to be sent represents current modulated M-ary symbol as well as the reference of next modulation symbol in a data frame, which can fully utilize the transmission energy with the same hardware implementation as PI-DCSK. The bit error performance of the proposed DPI-DCSK over the multipath Rayleigh fading channel is theoretically analyzed. The simulation results show that the proposed DPI-DCSK achieves a performance gain of more than 3 dB over PI-DCSK, especially in high-delay multipath fading channels. The performance advantage of the proposed scheme is also illustrated in practical ultra-wideband (UWB) channels. Thus, DPI-DCSK is an efficient alternative transmission scheme for chaos-based wireless communication, such as indoor applications of transmitted-reference UWB communications.
RT-GSOM: Rough tolerance growing self-organizing map

The concept of rough tolerance set is introduced within growing self-organizing map (GSOM) to reduce the uncertainty in decision-making by developing a new algorithm, namely rough tolerance GSOM (RT-GSOM). This algorithm aims to address the issues of (i) identifying the suitable size of clusters in SOM, (ii) information loss in rough SOM (RSOM), and (iii) uncertainty arising from the overlapping patterns of decision classes in GSOM. In RT-GSOM, the network is allowed to grow based on indiscernible reducts and tolerance thresholds extracted from data in an unsupervised way. The network is initialized with the samples extracted from these reducts, and the weights are initialized with random category index. For each decision class, one set of indiscernible reducts is obtained. The tolerance threshold for each decision class is defined using the average distance among all the samples present in the reduct set corresponding to the same class. The superiority of RT-GSOM is demonstrated over twelve benchmark datasets (both categorical and continuous) obtained from UCI machine learning repository. Results reveal that RT-GSOM is efficient than some state-of-the-art algorithms in terms of learning rate, and quality of clusters for both categorical, and continuous data.
Cohort intelligence with self-adaptive penalty function approach hybridized with colliding bodies optimization algorithm for discrete and mixed variable constrained problems

Abstract Recently, several socio-/bio-inspired algorithms have been proposed for solving a variety of problems. Generally, they perform well when applied for solving unconstrained problems; however, their performance degenerates when applied for solving constrained problems. Several types of penalty function approaches have been proposed so far for handling linear and non-linear constraints. Even though the approach is quite easy to understand, the precise choice of penalty parameter is very much important. It may further necessitate significant number of preliminary trials. To overcome this limitation, a new self-adaptive penalty function (SAPF) approach is proposed and incorporated into socio-inspired Cohort Intelligence (CI) algorithm. This approach is referred to as CI–SAPF. Furthermore, CI–SAPF approach is hybridized with Colliding Bodies Optimization (CBO) algorithm referred to as CI–SAPF–CBO algorithm. The performance of the CI–SAPF and CI–SAPF–CBO algorithms is validated by solving discrete and mixed variable problems from truss structure domain, design engineering domain, and several problems of linear and nonlinear in nature. Furthermore, the applicability of the proposed techniques is validated by solving two real-world applications from manufacturing engineering domain. The results obtained from CI–SAPF and CI–SAPF–CBO are promising and computationally efficient when compared with other nature inspired optimization algorithms. A non-parametric Wilcoxon’s rank sum test is performed on the obtained statistical solutions to examine the significance of CI–SAPF–CBO. In addition, the effect of the penalty parameter on pseudo-objective function, penalty function and constrained violations is analyzed and discussed along with the advantages over other algorithms.
Volumetric Video in Augmented Reality Applications for Museological Narratives

Cross-reality technologies are quickly establishing themselves as commonplace platforms for presenting objects of historical, scientific, artistic, and cultural interest to the public. In this space, augmented reality (AR) is notably successful in delivering cultural heritage applications, including architectural and environmental heritage reconstruction, exhibition data management and representation, storytelling, and exhibition curation. Generally, it has been observed that the nature of information delivery in applications created for narrating exhibitions tends to be informative and formal. Here we report on the assessment of a pilot scene for a prototype AR application that attempts to break this mold by employing a humorous and playful mode of communication. This bespoke AR experience harnessed the cutting-edge live-action capture technique of volumetric video to create a digital tour guide that playfully embellished the museological experience of the museum visitors. This applied research article consists of measuring, presenting, and discussing the appeal, interest, and ease of use of this ludic AR storytelling strategy mediated via AR technology in a cultural heritage context.
A new differential evolution algorithm for joint mining decision and resource allocation in a MEC-enabled wireless blockchain network

This paper studies a mobile edge computing-enabled wireless blockchain network, in which a set of Internet of Things (IoT) devices can act as miners to participate in mining. In this blockchain network, we jointly optimize the mining decision and resource allocation to maximize the total profit of all miners. When using evolutionary algorithms to solve this problem, each individual usually represents the mining decisions and resource allocations of all miners, which results in the redundant search space due to the fact that not all miners participate in mining. In this paper, we propose a new differential evolution (DE) algorithm, called DEMiDRA. In DEMiDRA, each individual represents the resource allocation of a participating miner and the resource allocations of all participating miners constitute the whole population. Then, DE is adopted to optimize the resource allocation. As for the optimization of the mining decision, we need to select miners to participate in mining and update the number of participating miners. Since the population size is equal to the number of participating miners, we transform the update of the number of participating miners into the adjustment of the population size and design an adaptive strategy. Besides, a tabu strategy is developed to prevent unpromising miners from participating in mining. The effectiveness of DEMiDRA is verified by comparing it with three other algorithms on a set of instances.
Single Image Haze Removal With Haze Map Optimization for Various Haze Concentrations

Hazy images suffer from poor visibility and possess low-contrast, degrading the visibility of the scene. The performance of single-image haze removal methods is limited by priors or constraints. This article presents an efficient single image dehazing method by cascading two models. The first model is a new atmospheric scattering model from which atmospheric light and transmission map are estimated and the second model is the proposed sparse haze model from which a haze map is estimated. A least-square optimization is carried out on the haze map to restore the haze-free image. Moreover, the analysis of the haze removal process has been investigated by a membership function to observe how much haze has been removed from the input hazy images containing various degrees of haze. Dehazing performances are evaluated on three types of datasets, i.e., real-world hazy images, synthetic images of various degrees of haze, and our developed dataset containing thick haze. Experimental results demonstrate that the proposed approach generates better performance than the state-of-the-art methods, especially in the sky or objects containing white objects, both qualitatively and quantitatively.
The impact of digital pedagogy training on in-service teachers’ attitudes towards digital technologies

Abstract To prepare schools for the demands of the twenty-first century, teachers have been challenged to expand their use of digital technologies in their teaching. This study is a part of OpenDigi, which aimed to create teachers’ communities for enhancing digital pedagogy skills and the use of digital assessment tools. The aim of this study is to investigate the impact of digital pedagogy training on in-service teachers’ attitudes towards digital technologies. Self-report questionnaires were collected from 98 elementary and lower secondary school in-service teachers in southern Finland. A total of 22 in-service teachers completed both pre- and post-test. The results showed that the impact of the digital pedagogy training depended on teachers’ ICT confidence level. Teachers who had low confidence in ICT use showed an increased ICT confidence level after the programme, while teachers who already had high confidence in ICT use showed no significant changes in their confidence level. Moreover, the results indicated that the need for ICT support was lower after the training for the teachers in high confidence group, while there were no significant changes in the need for ICT support for the teachers in the low confidence group. These results suggested that the training could fulfil teachers’ needs for ICT support, particularly for the teachers in the high confidence group. The challenges teachers face in expanding the use of digital technologies in practice and their commitment to the digital culture are discussed.
Agora: A Privacy-aware Data Marketplace

We propose Agora, the first privacy-aware data marketplace that enables parties to get compensated for contributing data, without relying on a trusted third party. We leverage cryptographic techniques to achieve three security properties: (i) data privacy-raw data remain private except for a function output, (ii) output verifiability-the output is proven to be correct, and (iii) atomicity of payments-parties cannot avoid paying for provided services. Agora is designed as a decentralized blockchain application via smart contracts. We implement a prototype on Ethereum and evaluate its performance in terms of computation overhead and monetary cost.
Detecting Fake News: Two Problems for Content Moderation

The spread of fake news online has far reaching implications for the lives of people offline. There is increasing pressure for content sharing platforms to intervene and mitigate the spread of fake news, but intervention spawns accusations of biased censorship. The tension between fair moderation and censorship highlights two related problems that arise in flagging online content as fake or legitimate: firstly, what kind of content counts as a problem such that it should be flagged, and secondly, is it practically and theoretically possible to gather and label instances of such content in an unbiased manner? In this paper, I argue that answering either question involves making value judgements that can generate user distrust toward fact checking efforts.
Deep Adversarial Network for Scene Independent Moving Object Segmentation

The current prevailing algorithms highly depend on additional pre-trained modules trained for other applications or complicated training procedures or neglect the inter-frame spatio-temporal structural dependencies. Also, the generalized effect of existing works with completely unseen data is difficult to identify. Specifically, the outdoor videos suffer from adverse atmospheric conditions like poor visibility, inclement weather, etc. In this letter, a novel end-to-end multi-scale temporal edge aggregation (MTPA) network is proposed with adversarial learning for scene dependent and independent object segmentation. The MTPA is proposed to extract the comprehensive spatio-temporal features from the current and reference frame. These MTPA features are used to guide the respective decoder through skip connections. To get authentic and consistent foreground object(s), the respective scale feedback of previous frame output is provided with respective MTPA features at each decoder input. The performance analysis of the proposed method is verified on CDnet-2014 and LASIESTA video datasets. The proposed method outperforms the existing state-of-the-art methods with scene dependent and independent analysis.
Adaptive Informative Sampling with Environment Partitioning for Heterogeneous Multi-Robot Systems

Multi-robot systems are widely used in environmental exploration and modeling, especially in hazardous environments. However, different types of robots are limited by different mobility, battery life, sensor type, etc. Heterogeneous robot systems are able to utilize various types of robots and provide solutions where robots are able to compensate each other with their different capabilities. In this paper, we consider the problem of sampling and modeling environmental characteristics with a heterogeneous team of robots. To utilize heterogeneity of the system while remaining computationally tractable, we propose an environmental partitioning approach that leverages various robot capabilities by forming a uniformly defined heterogeneity cost space. We combine with the mixture of Gaussian Processes model-learning framework to adaptively sample and model the environment in an efficient and scalable manner. We demonstrate our algorithm in field experiments with ground and aerial vehicles.
A modified salp swarm algorithm (SSA) combined with a chaotic coupled map lattices (CML) approach for the secured encryption and compression of medical images during data transmission

Medical image data analysis is a significant data in medical systems to analyze patient's health record. The biomedical image processing techniques and modernization of image devices are enhancing the medical image data. The main advantages of medical imaging data are (i) the medical imaging data are transmitted in the secure network in safe from the hackers (ii) medical imaging data can be shared quickly and easily between health care providers and departments. The chaotic system based different encryption algorithms using researches is analyzed. There are numerous issues in transmission of image such as weak security, small key space and high computational time. In this manuscript, a combined coupled map lattice (CML) with modified salp swarm algorithm is proposed. This algorithm compresses and encrypts the image using coupled map lattice (CML). This CML initially generates the number of encrypted images in modified SSA population. After the initialization, the modified SSA with whale optimization algorithm (WOA) is applied to reduce the computational time and maximize entropy in image cipher. To enhance the security of medical image is encrypted as a cipher image and transmits over the network. The experimental outcomes demonstrate that the proposed technique is more efficient to encrypt the medical image and has capability to resist the different distinct attacks.
A Simple Logic of Functional Dependence

Abstract This paper presents a simple decidable logic of functional dependence LFD, based on an extension of classical propositional logic with dependence atoms plus dependence quantifiers treated as modalities, within the setting of generalized assignment semantics for first order logic. The expressive strength, complete proof calculus and meta-properties of LFD are explored. Various language extensions are presented as well, up to undecidable modal-style logics for independence and dynamic logics of changing dependence models. Finally, more concrete settings for dependence are discussed: continuous dependence in topological models, linear dependence in vector spaces, and temporal dependence in dynamical systems and games.
A novel solution for counterfeit prevention in the wine industry based on IoT, smart tags, and crowd-sourced information

This paper describes a novel solution for counterfeit prevention and brand protection in the wine industry. The presented approach combines Internet of Things, Cloud, and Mobile technologies with the use of custom designed smart tags applied to each bottle of wine in order to provide food track and trace capabilities. The smart tags combine QR code with additional information printed with an invisible photochromatic ink. The tags are activated by flashlight on mobile devices during the scanning. Before scanning, users are prompted to select the context of the wine bottle (in store, sold, consumed) in order to provide additional information about each bottle as it moves through the supply chain. This information is used by the custom-made heuristic to help users and wine makers detect issues with individual instances of the product. The system was implemented as a pilot project that was executed during a period of 6 months. End users showed a great interest in the possibility to implement such a system, consumers liked the interaction with the product using the mobile app and smart tags, while wine makers expressed their interest in the solution. Besides counterfeit that affects profit, the benefits of such systems include improved brand protection and reduced risk of health hazards.
ANFIS fusion algorithm for eye movement recognition via soft multi-functional electronic skin

Eye movement detection has attracted increasing attention in the fields of safety driving, eye motion tracking, psychological assessment and telemedicine. Soft multi-functional electronic skin (SMFES) is designed to collect electrooculogram (EOG), skin temperature and sweat signals simultaneously for eye movement detection. Serpentine structure is adopted to ensure the stretchability of SMFES for satisfying large deformation (>30%) of the soft skin surface. The paper demonstrates that EOG, skin temperature and sweat signals are successfully collected under different eye movements. The feature data from EOG, skin temperature and sweat signals are extracted with different eye movements, and the principal component analysis (PCA) method is adopted to reduce the dimensionality of the feature space. The paper also proposes an intelligent data fusion algorithm for eye movement classification whose input vector is represented by the first three principal components. Adaptive neuro fuzzy inference system (ANFIS) is built to classify and recognize the eye movements (Up, Down, Left, and Right). Furthermore, experiments have demonstrated that ANFIS algorithm achieves 90% recognition accuracy of such eye movements. This work demonstrates that SMFES integrated with data fusion algorithm can successfully solve the eye movement tracking problem, with significant impact in safety driving and wearable electronics.
A High Performance Concurrency Protocol for Smart Contracts of Permissioned Blockchain

Although the emergence of the programmable smart contract makes blockchain systems easily embrace a wide range of industrial services, how to execute smart contracts efficiently becomes a big challenge nowadays. Due to the existence of Byzantine nodes, existing mature concurrency control protocols in database cannot be employed directly, since the mechanism of executing smart contracts varies a lot. Furthermore, even though smart contract execution follows a two-phase style, i.e., the primary node executes a batch of smart contracts in the first phase and the validators replay them in the second phase, existing parallel solutions merely focus on the optimization for the first phase, rather than the second phase. In this paper, we propose a novel two-phase concurrency control protocol to optimize both phases for the first time. First, the primary executes transactions in parallel and generates a transaction dependency graph with high parallelism for validators. Then, a graph partition algorithm is devised to divide the original graph into several sub-graphs to preserve parallelism and reduce communication cost remarkably. Finally, we propose a deterministic replay protocol to re-execute the primary’s parallel schedule concurrently. Moreover, this two-phase protocol is further optimized by integrating with PBFT. Theoretical analysis and extensive experimental results illustrate that the proposed scheme outperforms state-of-art solutions significantly.