Reviewerly/exHarmony huggingface dataset:
Privacy-based medical data protection against internal security threats in heterogeneous Internet of Medical Things

Data and information security is considered to be an important and challenging task for any field of life. But it becomes more critical especially when it deals with the medical field due to life and health hazards. The ratio of internal security threats to external threats always remains high. A huge number of efforts and technical expertise are required in the case of attacking the system from the external environment. But it requires fewer efforts if a system is attacked internally by the stakeholders of the system. This article presents an access control model that secures the medical data of patients against internal cybersecurity threats. It allows only the legitimate users, that is, authorized patients and doctors to communicate despite the fact of physical boundaries. The proposed model implements authorization in combination with permissions and roles instead of roles only for medical staff. It removes the discrepancies in the existing access control models. The proposed model ensures communication among doctors and patients in a secure, private, and efficient manner. The model is demonstrated by using mathematical modeling along with implementation examples. The proposed model outperformed in comparison with state-of-the-art access control models.
It's How You Say It

Designing chatbots that produce language that is natural and appropriate to a given context is critical in satisfying user expectations. Currently, little is known about how a chatbot's linguistic choices should be designed to conform with the language humans produce in similar contexts. In this paper, we draw on existing sociolinguistic theory to adapt a technique calledregister analysis to (a) characterize the linguistic register used by humans in a specific conversational context; and (b) drive chatbot language design. Our exploratory study investigates the application of register analysis for tourist assistants chatbots and shows how the results could be used to develop them to adopt the appropriate register.
Data Representation and Learning With Graph Diffusion-Embedding Networks

Recently, graph convolutional neural networks have been widely studied for graph-structured data representation and learning. In this paper, we present Graph Diffusion-Embedding networks (GDENs), a new model for graph-structured data representation and learning. GDENs are motivated by our development of graph based feature diffusion. GDENs integrate both feature diffusion and graph node (low-dimensional) embedding simultaneously into a unified network by employing a novel diffusion-embedding architecture. GDENs have two main advantages. First, the equilibrium representation of the diffusion-embedding operation in GDENs can be obtained via a simple closed-form solution, which thus guarantees the compactivity and efficiency of GDENs. Second, the proposed GDENs can be naturally extended to address the data with multiple graph structures. Experiments on various semi-supervised learning tasks on several benchmark datasets demonstrate that the proposed GDENs significantly outperform traditional graph convolutional networks.
Detecting malicious URLs using binary classification through adaboost algorithm

Malicious Uniform Resource Locator (URL) is a frequent and severe menace to cybersecurity. Malicious URLs are used to extract unsolicited information and trick inexperienced end users as a sufferer of scams and create losses of billions of money each year. It is crucial to identify and appropriately respond to such URLs. Usually, this discovery is made by the practice and use of blacklists in the cyber world. However, blacklists cannot be exhaustive, and cannot recognize zero-day malicious URLs. So to increase the observation of malicious URL indicators, machine learning procedures should be incorporated. This study aims to discuss the exposure of malicious URLs as a binary classification problem using machine learning through an AdaBoost algorithm.
Global brain-reflective accounting practices

Purpose The purpose of this paper is to explore the intersection of accounting practices and new technologies in the age of agility as a form of intellectual capital, through sharing the conceptualization and real implications of accounting and accountability ideas in exploring and deploying new technologies, such as big data analytics, blockchain and augmented accounting practices and expounding how they constitute new forms of intellectual capital to support value creation and realise Sustainable Development Goals (SDGs). Design/methodology/approach The adopted methodology is cyber-ethnography, which investigates online practices through observation and discourse analysis, reflecting on new business models and practices, and how accounting relates to these developments. The global brain sets the conceptual context, which reflects the distributed network intelligence that is created through the internet. Findings The main findings focus on various developments of accounting practice that reflect, utilise or support digital companies and new technologies, including augmentation, big data analytics and blockchain technology, as new forms of intellectual capital, that is knowledge and skills within organisations, that have the potential to support value creation and realise SDGs. These relate to and originate from the global brain, which constitutes the umbrella of tech-related intellectual capital. Originality/value This paper determines new developments in accounting practices in relation to new technologies, due to the continuous expansion and influence of the intelligence of the collective network, the global brain, as forms of intellectual capital, contributing to value creation, sustainable development and the realisation of SDGs.
Doubly Robust Bias Reduction in Infinite Horizon Off-Policy Estimation

Infinite horizon off-policy policy evaluation is a highly challenging task due to the excessively large variance of typical importance sampling (IS) estimators. Recently, Liu et al. (2018) proposed an approach that significantly reduces the variance of infinite-horizon off-policy evaluation by estimating the stationary density ratio, but at the cost of introducing potentially high risks due to the error in density ratio estimation. In this paper, we develop a bias-reduced augmentation of their method, which can take advantage of a learned value function to obtain higher accuracy. Our method is doubly robust in that the bias vanishes when either the density ratio or value function estimation is perfect. In general, when either of them is accurate, the bias can also be reduced. Both theoretical and empirical results show that our method yields significant advantages over previous methods.
Adapted Visual Analytics Process for Intelligent Decision-Making: Application in a Medical Context

The theoretical and practical researches on Visual Analytics for intelligent decision-making tasks have remarkably advanced in the past few years. Intelligent Decision Support Systems (IDSS) introduce effective and efficient paths from raw data to decision by involving visualization and data mining technologies. Data mining-based DSS produces potentially interesting patterns from data. The transition from extracted patterns to knowledge is a delicate task. In this context, we propose to adapt a common visual analytics process for creating a path that enables the user (decision-maker) to automatically explore and visually extract insights by interacting with the patterns. This proposal is inspired from integrating traditional visual analytics concepts with the mental model of knowledge visualization. The idea is to combine an automatic and visual analysis of patterns to generate knowledge for the purpose of decision-making. To validate our proposal, we have applied it to a medical case study for the fight against Nosocomial Infections in Intensive Care Units. The developed platform was evaluated according to the utility and usability dimensions.
BayesOpt Adversarial Attack

Black-box adversarial attacks require a large number of attempts before finding successful adversarial examples that are visually indistinguishable from the original input. Current approaches relying on substitute model training, gradient estimation or genetic algorithms often require an excessive number of queries. Therefore, they are not suitable for real-world systems where the maximum query number is limited due to cost. We propose a query-efficient black-box attack which uses Bayesian optimisation in combination with Bayesian model selection to optimise over the adversarial perturbation and the optimal degree of search space dimension reduction. We demonstrate empirically that our method can achieve comparable success rates with 2-5 times fewer queries compared to previous state-of-the-art black-box attacks.
VideoFlow: A Conditional Flow-Based Model for Stochastic Video Generation

Generative models that can model and predict sequences of future events can, in principle, learn to capture complex real-world phenomena, such as physical interactions. However, a central challenge in video prediction is that the future is highly uncertain: a sequence of past observations of events can imply many possible futures. Although a number of recent works have studied probabilistic models that can represent uncertain futures, such models are either extremely expensive computationally as in the case of pixel-level autoregressive models, or do not directly optimize the likelihood of the data. To our knowledge, our work is the first to propose multi-frame video prediction with normalizing flows, which allows for direct optimization of the data likelihood, and produces high-quality stochastic predictions. We describe an approach for modeling the latent space dynamics, and demonstrate that flow-based generative models offer a viable and competitive approach to generative modeling of video.
An augmented reality system characterization of placement accuracy in neurosurgery

<h2>Abstract</h2> Computer assisted navigation (CAN) is a technology which has been available for commercial use in operating rooms for quite some time now. CAN relies on the information presented in patient imaging (usually CT or MRI images) and the surgical site. The method for registration between these two sets of data is crucial for safe image guided navigation during surgery. Although the existing technologies are extremely accurate, they still pose problems in the operating. Motivation for this study is to explore the possibility of using augmented reality (AR) to improve ease of use for surgical navigation and provide a system which complements the existing operating room workflow. As with all commercially available surgical navigation systems, registration accuracy is of utmost important to maintain patient safety. In this paper, we propose a novel method to quantify registration accuracy for augmented reality (AR) devices in neurosurgery.
MA-DST: Multi-Attention-Based Scalable Dialog State Tracking

Task oriented dialog agents provide a natural language interface for users to complete their goal. Dialog State Tracking (DST), which is often a core component of these systems, tracks the system's understanding of the user's goal throughout the conversation. To enable accurate multi-domain DST, the model needs to encode dependencies between past utterances and slot semantics and understand the dialog context, including long-range cross-domain references. We introduce a novel architecture for this task to encode the conversation history and slot semantics more robustly by using attention mechanisms at multiple granularities. In particular, we use cross-attention to model relationships between the context and slots at different semantic levels and self-attention to resolve cross-domain coreferences. In addition, our proposed architecture does not rely on knowing the domain ontologies beforehand and can also be used in a zero-shot setting for new domains or unseen slot values. Our model improves the joint goal accuracy by 5% (absolute) in the full-data setting and by up to 2% (absolute) in the zero-shot setting over the present state-of-the-art on the MultiWoZ 2.1 dataset.
Being present in a real or virtual world: A EEG study

This study proposes an approach to evaluation and measuring of presence for man-machine interaction in the virtual reality based on electroencephalographic data. It analyzes stable electroencephalographic patterns that allow us to trace a connection between a brain activity and purposeful actions of an individual in various environments. The subjects of the study were experienced downhill skiers equipped with electroencephalographs, who performed real-life skiing on a downhill course, after which they were offered a virtual simulation of downhill skiing using an HTCVive headset and a programmed 2D or desktop simulator. The results of measurement showed neuropatterns similar in the cases of virtual reality simulation and physical downhill skiing (in part of changes in space and power parameters of electroencephalograms in the different frequency ranges) and different from a 2D simulator. This observation enables us to make an assumption of realism of a virtual reality simulator in the context of reproduction of the subjects' similar cognitive and semantic connections and motor programs. Further research work will focus on evaluation of efficiency in performing psychophysiological tests (time response to a mobile object) in the virtual reality and 2D desktop application.
A Probably Secure Bi-GISIS Based Modified AKE Scheme With Reusable Keys

In this paper, we propose a novel authenticated key exchange scheme based on the Bi-GISIS problem for the post-quantum world.The security of the proposed scheme is based on the hardness assumption of the Bi-GISIS problem.The reusable key property is provided for the proposed scheme in the random oracle model by using the bilateral pasteurization method.To obtain an authenticated key exchange scheme, we use the implicit authentication steps.The security analysis of the proposed scheme is presented in the Bellare-Rogaway security model, where weak perfect forward secrecy is provided.We also give novel perspective to the Bi-GISIS based authenticated key exchange problem.
Multi-process image encryption scheme based on compressed sensing and multi-dimensional chaotic system

With the rapid development of computer science, the storage and dissemination of information are often carried out between various types of computer hardwares and various networks. The traditional information encryption scheme has gradually disappeared. Therefore, computer-based information encryption algorithms have gradually become a research hotspot in recent years. By combining the theory of wavelet packet transform, compressed sensing and chaotic system, a multi-process image encryption scheme based on compressed sensing and multi-dimensional chaotic system is proposed. The encryption scheme implements compression and encryption for grayscale images and corresponding decompression and decryption process. The wavelet packet transform theory is applied to the image preprocessing stage to perform wavelet packet decomposition on the original image. At the same time, the image signal components obtained by the decomposition are classified according to the threshold processing method, and the characteristics of the image signal components are processed in the subsequent processing. They are compressed, encrypted, or reserved in a differentiated manner. In the image compression stage, by introducing the compressed sensing algorithm to overcome the shortcomings of the traditional Nyquist sampling theorem, such as high sampling cost and low reconstruction quality, the compression efficiency and compression quality are improved while the ciphertext image reconstruction quality is guaranteed. In the image encryption stage, the encryption scheme combines multi-class and multi-dimensional chaotic systems to confuse and scramble the related image signal components, and introduces a high-dimensional chaotic system to make the encryption scheme have a large enough key space to further enhance the ciphertext image reliability. Finally, the complete reconstruction of the original image is achieved by applying the inverse of compression, encryption and wavelet packet transform. The simulation results show that the image encryption scheme effectively protects the basic information about ciphertext images by virtue of algorithm robustness against external interference, and does not reveal any useful information when dealing with cracking methods such as plaintext attacks. In addition, the information entropy and correlation coefficient of ciphertext images encrypted by this encryption scheme are closer to ideal values than those of the encryption algorithm in the references, and its encryption performance is significantly improved.
Hyper-Laplacian regularized multi-view subspace clustering with low-rank tensor constraint

In this paper, we propose a novel hyper-Laplacian regularized multiview subspace clustering with low-rank tensor constraint method, which is referred as HLR-MSCLRT. In the HLR-MSCLRT model, the subspace representation matrices of different views are stacked as a tensor, and then the high order correlations among data can be captured. To reduce the redundancy information of the learned subspace representations, a low-rank constraint is adopted to the constructed tensor. Since data in the real world often reside in multiple nonlinear subspaces, the HLR-MSCLRT model utilizes the hyper-Laplacian graph regularization to preserve the local geometry structure embedded in a high-dimensional ambient space. An efficient algorithm is also presented to solve the optimization problem of the HLR-MSCLRT model. The experimental results on some data sets show that the proposed HLR-MSCLRT model outperforms many state-of-the-art multi-view clustering approaches.
EFL Pre-Service Teachersâ€™ Perception toward the Use of Mobile Assisted Language Learning in Teaching English

In 21st century, educational process has been changed by the development of technology. One of the strategies that can be applied to support educational process in digital era is that the use of Mobile Learning strategy. Related to language learning, Mobile learning is further developed into the term Mobile Assisted Language Learning (MALL). This study aimed at investigating EFL pre-service teachersâ€™ perception toward the use of Mobile Assisted Language Learning in teaching English as Foreign Language. Explanatory sequential mixed method design was used as the method of this study. The participants of this study were 70 participants from semester 8 students of English Language Education in Ganesha University of Education. Data of this study were collected through questionnaire and interview. As the result, EFL pre-service teachers had positive perceptions toward the use of MALL in teaching English. It shows that EFL pre-service teachers understand and aware toward MALL in teaching English.
Timed-image based deep learning for action recognition in video sequences

The paper addresses two issues relative to machine learning on 2D + X data volumes, where 2D refers to image observation and X denotes a variable that can be associated with time, depth, wavelength, etc. The first issue addressed is conditioning these structured volumes for compatibility with respect to convolutional neural networks operating on 2D image file formats. The second issue is associated with sensitive action detection in the “2D + Time” case (video clips and image time series). For the data conditioning issue, the paper first highlights that referring 2D spatial convolution to its 1D Hilbert based instance is highly accurate for information compressibility upon tight frames of convolutional networks. As a consequence of this compressibility, the paper proposes converting the 2D + X data volume into a single meta-image file format, prior to machine learning frameworks. This conversion is such that any 2D frame of the 2D + X data is reshaped as a 1D array indexed by a Hilbert space-filling curve and the third variable X of the initial file format becomes the second variable in the meta-image format. For the sensitive action recognition issue, the paper provides: (i) a 3 category video database involving non-violent, moderate and extreme violence actions; (ii) the conversion of this database into a timed meta-image database from the 2D + Time to 2D conditioning stage described above and (iii) outstanding 2-level and 3-level violence classification results from deep convolutional neural networks operating on meta-image databases.
No-Reference Quality Assessment for Contrast-Distorted Images

Contrast distortion is a common distortion type in the image applications.However, there are still very limited approaches proposed for quantifying the quality of the contrast-distorted images reliably.In this paper, we devise a novel no-reference/blind quality assessment method for those contrast-distorted images.In the proposed method, we characterize the image quality by deeply investigating multiple contrast distortion-relevant properties of the image, i.e., spatial characteristics, image histogram, visual perception characteristics and chrominance, which can describe the image quality more comprehensively and precisely.Accordingly, a series of quality-aware features are developed to characterize the contrast-distorted image quality properly.Support vector regression (SVR) is then employed to integrate all the extracted features and infer the image quality score.Extensive experiments conducted on the standard contrast-distorted image databases/datasets demonstrate that the proposed method achieves superior prediction performance to the state-of-the-art NR quality assessment models on evaluating the contrast-distorted image quality.
Effective Fragmentation Minimization by Cloud Enabled Back Up Storage

Nowadays to increase the efficiency, consistency and the quality of the organizations and to further extend the business world wide the digitization is followed in processing, storing and conveying the information. This in turn has also caused huge set of data flow paving way for the data recovery services. The cloud computing with the massive storage capabilities have become a predominantly used paradigm for data storage and recovery due to its on demand network access, elasticity, flexibility and pay as you go. Moreover to secure the information that is stored the information’s are fragmented and stored. However this fragmentation process often occurs in the form of dispersed and scattered packages lacking proper order heightening the time and minimizing the efficiency of the recovery and information collection. To bring down the restoration time and enhance its efficiency the proposed method in the paper tries to reduce the fragmentation by minimizing the number of dispersed and scattered packages for this the paper utilizes the Hybridized Historical aware algorithm (HHAR) along with the cache aware filter to gather the historical information’s associated with the back-up system and the identify the out of order containers respectively. Further the every data package is protected applying the advanced encryption standard by producing a key to authenticate the access of the data. The proposed model is simulated using the network simulator-II and the results obtained shows that the recovery time is enhanced by 95% and the restore performance is improved by 94.3%.