Reviewerly/exHarmony huggingface dataset:
A conceptual peer review model for arXiv and other preprint databases

A global survey conducted by arXiv in 2016 showed that 58% of arXiv users thought arXiv should have a peer review system. The current opinion is that arXiv should adopt the Community Peer Review model. This paper evaluates and identifies two weak points of Community Peer Review and proposes a new peer review model – Self‐Organizing Peer Review. We propose a model in which automated methods of matching reviewers to articles and ranking both users and articles can be implemented. In addition, we suggest a strategic plan to increase recognition of articles in preprint databases within academic circles so that second generation preprint databases can achieve faster and cheaper publication.
A stochastic variational framework for Recurrent Gaussian Processes models

Gaussian Processes (GPs) models have been successfully applied to the problem of learning from sequential observations. In such context, the family of Recurrent Gaussian Processes (RGPs) have been recently introduced with a specifically designed structure to handle dynamical data. However, RGPs present a limitation shared by most GP approaches: they become computationally infeasible when facing very large datasets. In the present work, with the aim of improving scalability, we modify the original variational approach used with RGPs in order to enable inference via stochastic mini-batch optimization, giving rise to the Stochastic Recurrent Variational Bayes (S-REVARB) framework. We review recent related literature and comprehensively contextualize it with our approach. Moreover, we propose two learning procedures, the Local and Global S-REVARB algorithms, which prevent computational costs from scaling with the number of training samples. The global variant permits even greater scalability by also preventing the number of variational parameters from increasing with the training set, through the use of neural networks as sequential recognition models. The proposed framework is evaluated in the task of dynamical system identification for large scale datasets, a scenario not readily supported by the standard batch inference for RGPs. The promising results indicate that the S-REVARB framework opens up the possibility of applying powerful hierarchical recurrent GP-based models to massive sequential data.
Adversarial Adaptation of Scene Graph Models for Understanding Civic Issues

Citizen engagement and technology usage are two emerging trends driven by smart city initiatives. Typically, citizens report issues, such as broken roads, garbage dumps, etc. through web portals and mobile apps, in order for the government authorities to take appropriate actions. Several mediums - text, image, audio, video - are used to report these issues. Through a user study with 13 citizens and 3 authorities, we found that image is the most preferred medium to report civic issues. However, analyzing civic issue related images is challenging for the authorities as it requires manual effort. In this work, given an image, we propose to generate a Civic Issue Graph consisting of a set of objects and the semantic relations between them, which are representative of the underlying civic issue. We also release two multi-modal (text and images) datasets, that can help in further analysis of civic issues from images. We present an approach for adversarial adaptation of existing scene graph models that enables the use of scene graphs for new applications in the absence of any labelled training data. We conduct several experiments to analyze the efficacy of our approach, and using human evaluation, we establish the appropriateness of our model at representing different civic issues.
The effect of propagation delay on the dynamic evolution of the Bitcoin blockchain

This paper analyzes the selfish-mine strategy in the Bitcoin blockchain introduced in 2013 by I. Eyal and E. G. Sirer. This strategy could be used by a colluding pool of miners to earn more than their fair share of the mining revenue and in consequence to force other honest miners to join them to decrease the variance of their revenues and make their monthly revenues more predictable. It is a very dangerous dynamic that could allow the rogue pool of miners to go toward a majority by accumulating powers of news adherents and control the entire network. Considering that the propagation delay of information between any two miners in the network, which is not negligible and follows a normal distribution with mean proportional to the physical distance between the two miners, and a constant variance independent of others' delays, we prove that no guarantee can be given about the success or failure of the selfish-mine attack because of the variability of information propagation in the network.
Basing Diversified Services of Complex IIoT Applications on Scalable Block Graph Platform

In complex Industrial Internet of Things (IIoT) application systems, there may exist a variety of functional sub-systems which provide divergent services to the users and the relationship among these services could be strong, which is evidently very common in practice. As one such IoT application, the smart city is designed to use intelligent services in an environment where transport, environment, energy, living, governance, and civic components are integrated to operate the city in a smart and efficient manner. Surprisingly, however, the current state-of-the-art on blockchain does not show the progress on diversified services of the complex IIoT applications. This paper tends to fill the gap and presents a scalable block graph platform and consensus protocol for the complex IIoT applications by adopting the limited resources of IIoT devices. The platform and consensus protocol are generic in the sense that they can support any practical service. The directed acyclic graph represents a significant exhibition in improving the scalability of blockchains and supports our proposed consensus protocol in substance; moreover, the detailed syntax of the block graph is defined. The consensus protocol is showed to be secure against disloyal voting, sybil attack, and 51% attack. The real use case of smart city demonstrates the effectiveness of the proposal.
Hybrid intelligent deep kernel incremental extreme learning machine based on differential evolution and multiple population grey wolf optimization methods

Focussing on the problem that redundant nodes in the kernel incremental extreme learning machine (KI-ELM) which leads to ineffective iteration increase and reduce the learning efficiency, a novel improved hybrid intelligent deep kernel incremental extreme learning machine (HI-DKIELM) based on a hybrid intelligent algorithms and kernel incremental extreme learning machine is proposed. At first, hybrid intelligent algorithms are proposed based on differential evolution (DE) and multiple population grey wolf optimization (MPGWO) methods which used to optimize the hidden layer neuron parameters and then to determine the effective hidden layer neurons number. The learning efficiency of the algorithm is improved by reducing the network complexity. Then, we bring in the deep network structure to the kernel incremental extreme learning machine to extract the original input data layer by layer gradually. The experiment results show that the HI-DKIELM methods proposed in this paper with more compact network structure have higher prediction accuracy and better ability of generation compared with other ELM methods.
Precision roll angle measurement based on digital speckle pattern interferometry

An approach to precise measurement of small roll angle based on digital speckle pattern interferometry (DSPI) is introduced. A simple relationship between the roll angle and interferometric phase distribution is established by analyzing the mechanism of fringe formation in the process of roll detection using a dual-beam DSPI, and reasonable approximate processing. The presented method enjoys some important advantages, such as being free of a cooperative target and a very high measurement resolution. In our experiments, a measurement resolution of 1 µrad, which can compete with the best resolution of the existing methods, is achieved. Theoretically, the measurement resolution can even be improved to tens of nano-radians at the cost of increasing the size of the measuring device. The performance of the method is also demonstrated by a comparison experiment.
Descriptive, Predictive, and Prescriptive Analytics

This chapter provides an overview of the descriptive, predictive, and prescriptive analytics landscape. Data mining is first introduced, followed by coverage of the role of machine learning and artificial intelligence in analytics. Supervised and unsupervised learning are compared, along with the different applications that fall under each. The characteristics and role of reports in descriptive analytics are described, along with the extraction of data in a multidimensional environment. Key algorithms, covering different predictive analytics applications, are described in some detail.
Dynamic data auditing scheme for big data storage

When users store data in big data platforms, the integrity of outsourced data is a major concern for data owners due to the lack of direct control over the data. However, the existing remote data auditing schemes for big data platforms are only applicable to static data. In order to verify the integrity of dynamic data in a Hadoop big data platform, we presents a dynamic auditing scheme meeting the special requirement of Hadoop. Concretely, a new data structure, namely Data Block Index Table, is designed to support dynamic data operations on HDFS (Hadoop distributed file system), including appending, inserting, deleting, and modifying. Then combined with the MapReduce framework, a dynamic auditing algorithm is designed to audit the data on HDFS concurrently. Analysis shows that the proposed scheme is secure enough to resist forge attack, replace attack and replay attack on big data platform. It is also efficient in both computation and communication.
Entanglement and physical attributes of the interaction between two SC-qubits and thermal field in the presence of a magnetic field

The interaction between two superconducting (SC) qubits and thermal field in the presence of a magnetic field, the qubit–qubit interaction and the Stark interaction are investigated. The wave function, and hence density matrix, that describe this system is obtained and the behavior of physical quantities that characterize it are evaluated. In particular, the von Neumann entropy and quantum concurrence as a measure of entanglement between parts of the system are discussed in detail. The effects of thermal and low temperature on the dynamical properties of the von Neumann entropy and nonclassical correlation are discussed. Also, the influences of the atomic coherence and magnetic field on the statistical properties of the physical quantities are examined. Moreover, some important physical phenomena during the time evolution of the interaction between the two SC-qubits, thermal field, and magnetic field are explored. The nonclassical correlation between the thermal field and the two SC-qubits is enhanced by increasing temperature whereas the SC qubit–qubit entanglement decreases. The magnetic field and Stark shifts play a central role in the dynamical and physical attributes of the two types of entanglement.
Designing Valuable Augmented Reality Tourism Application Experiences

Augmented Reality (AR) has gained increased popularity in the tourism sector, for its ability to create enhanced tourist experiences. This, coupled with the proliferation of technologies has increased pressure for tourism organisations design and add value to tourist experiences. Whilst much research attention has focused on the potential of AR, exploring areas such as adoption, acceptance and usability, there is a lack of research outlining guidelines for the design of valuable AR application tourist experiences. This study uses a small UNESCO recognised museum in the UK, to identify key value adding features for the effective design of AR tourism applications. Adopting a multi-stakeholder approach, fifty interviews were held with five stakeholder groups, revealing four AR design categories; visitor value, organisational value, stakeholder value, economic value. Findings identified these categories should be considered for the effective design and implementation of enhanced tourist experiences, bridging a gap in current research.
Joint Pedestrian and Body Part Detection via Semantic Relationship Learning

While remarkable progress has been made to pedestrian detection in recent years, robust pedestrian detection in the wild e.g., under surveillance scenarios with occlusions, remains a challenging problem. In this paper, we present a novel approach for joint pedestrian and body part detection via semantic relationship learning under unconstrained scenarios. Specifically, we propose a Body Part Indexed Feature (BPIF) representation to encode the semantic relationship between individual body parts (i.e., head, head-shoulder, upper body, and whole body) and highlight per body part features, providing robustness against partial occlusions to the whole body. We also propose an Adaptive Joint Non-Maximum Suppression (AJ-NMS) to replace the original NMS algorithm widely used in object detection, leading to higher precision and recall for detecting overlapped pedestrians. Experimental results on the public-domain CUHK-SYSU Person Search Dataset show that the proposed approach outperforms the state-of-the-art methods for joint pedestrian and body part detection in the wild.
Representation of online handwriting using multi-component sinusoidal model

The representation of online handwriting is an important aspect of handwriting applications, which involves the extraction of various spatial and temporal attributes for analysis and individualization of handwritten patterns. In this work, a model based representation is proposed for online handwriting using a multi-component sinusoidal model. The method extracts sinusoidal parameters from handwriting by modeling its horizontal and vertical velocities between each successive pair of zero crossing points with a half period of the sine function. Thus, each velocity profile is represented by the sinusoidal oscillations whose parameters are modulated at the zero-crossing points. The use of multiple oscillations to model the velocities results in a better representation of the complex trajectories. The parameters of the proposed model are computed iteratively from its residual signals. We hypothesize that the analysis of the sinusoidal components and its parameters may provide added dynamic information about the handwriting. The efficacy of the proposal is demonstrated for online signature representation and synthetic variability generation by modifying the extracted parameters. Further, the proposed feature set is also employed for online handwriting recognition task. It is observed that the multi-component sinusoidal representation combined with existing point-based features provide an improvement in the recognition performance.
Architecture Optimization and Performance Comparison of Nonce-Misuse-Resistant Authenticated Encryption Algorithms

This paper presents a performance comparison of new authenticated encryption (AE) algorithms which are aimed at providing better security and resource efficiency compared to existing standards. Specifically, these algorithms improve the security of existing AE standards by providing a critical property termed nonce-misuse resistance. This paper addresses algorithm to architectural mappings of several candidates from the ongoing Competition for AE: Security, Applicability, and Robustness as well as a submission from the Crypto Forum Research Group. Implementations of the architectures on both field-programmable gate arrays and application-specific integrated circuits platforms are provided and compared with the architecture of a popular standard: Advanced Encryption Standard in Galois Counter mode (AES-GCM). Optimizations that are applicable to AE, in general, and nonce-misuse-resistant architectures, in particular, are presented. A hardware-software codesign approach to optimization is also discussed. The implementations via proposed optimizations demonstrate that new AE algorithms can provide comparable performance as standard AES-GCM while enhancing security and resource utilization for specific use-case scenarios.
Outsourced Ciphertext-Policy Attribute-Based Encryption with Equality Test

In the cloud era people get used to store their data to the cloud server, and would use encryption technique to protect their sensitive data from leakage. However, encrypted data management is a challenging problem, for example, encrypted data classification. Besides, how to effectively control the access to the encrypted data is also an important problem. Ciphertext-policy attribute-based encryption with equality test (CP-ABEET) is an efficient solution to the aforementioned problems, which enjoys the advantage of attribute-based encryption, and in the meanwhile supports the test of whether two different ciphertexts contain the same message without the need of decryption. However, the existing CP-ABEET schemes suffer from high computation costs. In this paper, we study how to outsource the heavy computation in CP-ABEET scheme to a third-party server. We introduce the notion of CP-ABEET supporting outsourced decryption (OCP-ABEET), which saves a lot of local computation loads of CP-ABEET. We propose a concrete construction of OCP-ABEET, and prove its security based on a reasonable number-theoretic assumption in the random oracle model. Compared with the existing CP-ABEET schemes, our scheme is more computationally .
On the role of in-situ making and evaluation in designing across cultures

There is a growing interest in designing products and interactions across cultures. In this paper, we report on our attempts to use in-situ making and evaluation to facilitate a short co-design process with outside designers in an ethnic and rural community. We found that rapid prototyping in the local context provided a mechanism to quickly engage designers with locals in informing iterative design refinement. Our research suggests that using in-situ making interlaced with evaluation is a feasible approach to drive designers to immerse, exchange and design within a cultural different context in the early stage of design exploration. We found that the rapid nature of our process makes it more suited for cultural product design led by designers than cross-cultural design.
Extreme learning machine-based non-linear observer for fault detection and isolation of wind turbine

This paper presents a robust fault detection and isolation (FDI) scheme for a variable speed wind turbine. The proposed scheme (extreme learning machine–state-dependent differential Riccati equation (ELM-SDDRE)) is an observer model-based approach, especially, a non-linear observer using SDDRE based on an improved model of the wind turbine by using the ELM. The standard SDDRE can be used for small model uncertainties. However, when the uncertainties are large, the SDDRE cannot detect and isolate the faults. The main objective of the ELM is the prediction of unknown nominal model dynamics to construct a new improved nominal model used by the observer for FDI. This makes the effect of uncertainties weak and consequently allows better faults detection. The faults considered in this paper are sensor faults in the rotating speeds of the rotor and generator outputs. The effectiveness of the proposed approach is illustrated through simulation.
Multi-tier hybrid feature selection by combining filter and wrapper for subset feature selection in cancer classification

Objectives: We have implemented a bio-inspired algorithm, particle swarm optimization method of miRNA subset selection to indentify the irreverent and redundant miRNAs for proper assessment of cancer diagnosis. Methods/Statistical Analysis: In this study we develop a creative multitier framework for subset selection to improve accuracy of caner classification. In the first tier we have used different filter methods to rank miRNAs according to their class relation then using union operator we have created a combinational model (Second tier) which consist of top ranked features of individual filter methods. Here the miRNAs are indentified according to their ranking with the threshold value defined. In third tier (feature pre selection model) improvised competitive swarm optimization algorithm is used to generate feasible optimal subset from the generated weighted miRNA in second tier to detect the biomarker gene for cancer detection. To minimize the gap between exploration and exploitation we have used Mamdani Fuzzy interference system. All selected genes from the fourth tire (feature reselection) is classify with classifier such as KNN. Findings: The objective has successfully achieved by implementing improvised competitive swarm optimization technique. Experimental result demonstrated that the proposed ICSO-KNN performs better than other method like PSO, PCA and PSO-KNN. ICSO-KNN outperformed with less error and larger amount of new solutions. Application/Improvements: We have four tier frameworks as an efficient feature selection algorithm which outperforms better. This approach may help to use any other metaheuristic feature selection to solve multimodal subset problem. Keywords: Filter, Mamdani, Wrapper, ICSO, KNN
Unsupervised dimensionality reduction versus supervised regularization for classification from sparse data

Unsupervised matrix-factorization-based dimensionality reduction (DR) techniques are popularly used for feature engineering with the goal of improving the generalization performance of predictive models, especially with massive, sparse feature sets. Often DR is employed for the same purpose as supervised regularization and other forms of complexity control: exploiting a bias/variance tradeoff to mitigate overfitting. Contradicting this practice, there is consensus among existing expert guidelines that supervised regularization is a superior way to improve predictive performance. However, these guidelines are not always followed for this sort of data, and it is not unusual to find DR used with no comparison to modeling with the full feature set. Further, the existing literature does not take into account that DR and supervised regularization are often used in conjunction. We experimentally compare binary classification performance using DR features versus the original features under numerous conditions: using a total of 97 binary classification tasks, 6 classifiers, 3 DR techniques, and 4 evaluation metrics. Crucially, we also experiment using varied methodologies to tune and evaluate various key hyperparameters. We find a very clear, but nuanced result. Using state-of-the-art hyperparameter-selection methods, applying DR does not add value beyond supervised regularization, and can often diminish performance. However, if regularization is not done well (e.g., one just uses the default regularization parameter), DR does have relatively better performance—but these approaches result in lower performance overall. These latter results provide an explanation for why practitioners may be continuing to use DR without undertaking the necessary comparison to using the original features. However, this practice seems generally wrongheaded in light of the main results, if the goal is to maximize generalization performance.
Principles and Guidelines for Quantum Performance Analysis

Expanding access to practical quantum computers prompts a widespread need to evaluate their performance. Principles and guidelines for carrying out sound empirical work on quantum computing systems are proposed. The guidelines draw heavily on classical experience in experimental algorithmics and computer systems performance analysis, with some adjustments to address issues in quantum computing. The focus is on issues related to quantum annealing processors, although much of the discussion applies to more general scenarios.