Reviewerly/exHarmony huggingface dataset:
A heuristic approach to handling missing data in biologics manufacturing databases

The biologics sector has amassed a wealth of data in the past three decades, in line with the bioprocess development and manufacturing guidelines, and analysis of these data with precision is expected to reveal behavioural patterns in cell populations that can be used for making predictions on how future culture processes might behave. The historical bioprocessing data likely comprise experiments conducted using different cell lines, to produce different products and may be years apart; the situation causing inter-batch variability and missing data points to human- and instrument-associated technical oversights. These unavoidable complications necessitate the introduction of a pre-processing step prior to data mining. This study investigated the efficiency of mean imputation and multivariate regression for filling in the missing information in historical bio-manufacturing datasets, and evaluated their performance by symbolic regression models and Bayesian non-parametric models in subsequent data processing. Mean substitution was shown to be a simple and efficient imputation method for relatively smooth, non-dynamical datasets, and regression imputation was effective whilst maintaining the existing standard deviation and shape of the distribution in dynamical datasets with less than 30% missing data. The nature of the missing information, whether Missing Completely At Random, Missing At Random or Missing Not At Random, emerged as the key feature for selecting the imputation method.
The Transformative Effect of the Internet of Things on Business and Society

This paper summarizes the discussion in a panel session on the Internet of things (IoT) at the 2017 International Conference on Information Systems (ICIS) in Seoul, Korea. The panel explored a research agenda on IoT technology and its interaction with business and society. IoT refers to the ever-growing number of numerous physical devices that feature software and location-based technologies that connect together in a network and exchange data with one another. IoT has garnered significant attention in information systems due to its rapidly expanding market and demand from a wide range of stakeholders such as consumers, businesses organizations, and government agencies. The IoT may be the next industrial revolution in which interconnected physical devices will automate skills and tasks. In today's hyper-connected economy, IoT can radically transform businesses and society through increased transparency, optimized production processes, and decreased operating expenses. Overall, the panel identified a six-pronged IS research agenda for IoT that comprises the IoT's impact on business and society, IoT monetization and end-user services, the IoT as a distributed platform, the convergence of the IoT and blockchain, security concerns and solutions, and the IoT and ethics. The paper concludes with a future direction for IoT.
Shakespeare and his contemporaries: Designing a genre classification scheme for Early English Books Online 1560–1640

Abstract The language of Early Modern texts can potentially reveal a lot about Shakespeare’s language. In this paper I describe the creation of a genre classification scheme for a segment of Early English Books Online – Text Creation Partnership (EEBO-TCP), covering the period 1560–1640. This categorisation permits meaningful comparison of the language of Shakespeare with that of his contemporaries and makes an integral contribution to The Encyclopaedia of Shakespeare’s Language project at Lancaster University. I outline the rationale behind the scheme, describe preliminary automatic genre classification work and present the prototype approach adopted for this categorisation. I also provide specific examples of classification in practice and discuss internal and external factors which influenced genre selection. I finish by suggesting how a range of scholars might benefit from this research.
Sample-Optimal Parametric Q-Learning with Linear Transition Models.

Consider a Markov decision process (MDP) that admits a set of state-action features, which can linearly express the process's probabilistic transition model. We propose a parametric Q-learning algorithm that finds an approximate-optimal policy using a sample size proportional to the feature dimension $K$ and invariant with respect to the size of the state space. To further improve its sample efficiency, we exploit the monotonicity property and intrinsic noise structure of the Bellman operator, provided the existence of anchor state-actions that imply implicit non-negativity in the feature space. We augment the algorithm using techniques of variance reduction, monotonicity preservation, and confidence bounds. It is proved to find a policy which is $\epsilon$-optimal from any initial state with high probability using $\widetilde{O}(K/\epsilon^2(1-\gamma)^3)$ sample transitions for arbitrarily large-scale MDP with a discount factor $\gamma\in(0,1)$. A matching information-theoretical lower bound is proved, confirming the sample optimality of the proposed method with respect to all parameters (up to polylog factors).
Seismic signal denoising using total generalized variation with overlapping group sparsity in the accelerated ADMM framework

The total generalized variation (TGV) denoising model is the extension of the total variation (TV) model and is capable of avoiding the staircase artifacts of the TV model. However, the TGV model independently disposes the pixels, ignoring the structural similarity prior of the processed image. Thus, the TGV model is not robust to high-amplitude noise. The motivation of this paper is to employ the structural similarity and improve the TGV denoising effect. By introducing the overlapping group sparsity to the TGV model, a new improved TGV model is then proposed, exploring both the first-order and second-order neighborhood differential gradient information to improve the robustness of the TGV to heavy noise pollution. To solve the proposed model, we adopted the accelerated alternating direction method of multipliers (ADMM), in which the multi-constrained problem is divided into several sub-problems. To avoid large-scale matrix computations in the spatial domain, we regard the differential operators as the convolution form, and thus the fast Fourier transform and the convolution theorem are employed to solve the proposed model efficiently. Finally, experiments were conducted on several seismic signals under different types of noise to verify the proposed method. The findings are listed as follows (1). The proposed model is particularly good at removing the heavy noise in smooth areas (2). The accelerated ADMM with a restart process is capable of solving the proposed model and is much faster than the traditional ADMM (3). The group size should be chosen properly to arrive the best performance of the proposed method.
Learning Optical Flow Using Deep Dilated Residual Networks

Nowadays, convolutional neural networks achieve remarkable performance on optical flow estimation because of its strong non-linear fitting ability. Most of them adopt the U-Net architecture, which contains an encoder part and a decoder part. In the encoder part, the resolution of the feature map is reduced with the deepening of the network layer. In the decoder part, the feature map is enlarged by the deconvolution layer to recover the estimated flow as full resolution. However, the motion details are usually lost with the contracting and expanding operations. Moreover, learning methods, especially supervised networks, always ignore the advantages of many well-proven constraints used in the variational model. In this paper, we introduce a novel architecture named dilated residual networks for learning optical flow, which can avoid the loss of details of the U-Net architecture and can directly learn the residual functions rather than the unreferenced functions to enhance the learning ability of the network. Furthermore, inspired by variational methods, the traditional prior assumptions, such as brightness constancy, gradient constancy, and smoothness assumption, are used in the supervised network as extra auxiliary terms to guide the training of network. Our method is tested on several benchmarks, such as MPI-Sintel, KITTI2012, and KITTI2015. The experimental results show that the dilated residual network is suitable for dense optical flow estimation due to the capability of preserving motion details and can boost the accuracy of optical flow estimation.
Adaptive suggestions to increase learnability for voice user interfaces

Voice User Interfaces (VUIs) are growing in popularity. However, their lack of visual guidance challenges users in learning how to proficiently operate these systems. My research focuses on adapting a VUI's spoken feedback to suggest verbal commands to users encountering errors. Based on observations in my previous research, these adaptive suggestions adapt to the detected user proficiency and predicted user goal to customize feedback to support user needs. The objectives of this technique are to guide users to 1) learn what verbal commands execute VUI actions and 2) learn the actions supported to accomplish desired tasks with the system.
Spatio-temporal modelling and uncertainty estimation of hourly global solar irradiance using Extreme Learning Machines

Solar photovoltaic (PV) is one of the most promising technologies for the transition from fossil fuels to renewable energy production. Accurate spatial and temporal modelling of solar irradiance is a key factor in the evaluation of PV technology potential for harvesting solar energy. We present here a data-driven approach based on an ensemble of Extreme Learning Machines using geographic and topographic features in input to predict the global horizontal irradiance in Switzerland from coarse-resolution satellite measurements. This provides a precise mapping of hourly global solar irradiance for each (250 × 250) m2 pixel of a grid covering the entire country. The uncertainty on predicted values is quantified through a variance-based analysis, able to distinguish between model and data uncertainty. The former amounts to 1%, whereas the latter is close to 15% of the predicted values. The presented methodology is scalable and applicable to any large environmental dataset. Our modelling of solar irradiance at hourly temporal resolution and of its uncertainty will allow for an estimate of hourly PV potential in Switzerland to facilitate a more efficient integration of solar photovoltaics into the built environment.
Blockchain based crowdfunding systems in Malaysian Perspective

Blockchain was only used as a foundation of cryptocurrency initially, but today, we can see the rise of this new emerging technology being implemented in many industries. In the future, most technologies around the world are expected to use blockchain as an efficient way to make online transactions. One of the areas that blockchain technologies can be applied is crowdfunding platforms. The most common problem with current crowdfunding scene in Malaysia (and around the world) is that the campaigns are not regulated and some of the crowd-funding campaign turned out to be fraud. Besides, the completion of some projects also was significantly delayed. This project aims to solve these problems by applying Ethereum smart contracts to the crowdfunding site to that the contracts will be fully automatically executed, thus preventing frauds and ensuring that the projects can be delivered within duration given.
RNA: An Accurate Residual Network Accelerator for Quantized and Reconstructed Deep Neural Networks

With the continuous refinement of Deep Neural Networks (DNNs), a series of deep and complex networks such as Residual Networks (ResNets) show impressive prediction accuracy in image classification tasks. Unfortunately, the structural complexity and computational cost of residual networks make hardware implementation difficult. In this paper, we present the quantized and reconstructed deep neural network (QR-DNN) technique, which first inserts batch normalization (BN) layers in the network during training, and later removes them to facilitate efficient hardware implementation. Moreover, an accurate and efficient residual network accelerator (RNA) is presented based on QR-DNN with batch-normalization-free structures and weights represented in a logarithmic number system. RNA employs a systolic array architecture to perform shift-and-accumulate operations instead of multiplication operations. QR-DNN is shown to achieve a 1∼2% improvement in accuracy over existing techniques, and RNA over previous best fixed-point accelerators. An FPGA implementation on a Xilinx Zynq XC7Z045 device achieves 804.03 GOPS, 104.15 FPS and 91.41% top-5 accuracy for the ResNet-50 benchmark, and state-of-the-art results are also reported for AlexNet and VGG.
Effect of Fragile Speckle Patterns on Accuracy of Digital Volume Correlation

Due to challenges in generating high-quality 3D speckle patterns for Digital Volume Correlation (DVC) strain measurements, DVC experiments often utilize the intrinsic texture and contrast of composite microstructures. One common deficiency of these natural speckle patterns is their poor durability under large deformations, which can lead to decorrelation and inaccurate strain measurements. Using syntactic foams as a model material, the effects of speckle pattern degradation on the accuracy of DVC displacement and strain measurements are assessed with both experimentally-acquired and numerically-generated images. It is shown that measurement error can be classified into two regimes as a function of the percentage of markers that have disappeared from the speckle pattern. For minor levels of damage beneath a critical level of damage, displacement and strain error remained near the noise floor of less than 0.05 voxels and 100 με, respectively; above this level, error rapidly increased to unacceptable levels above 0.2 voxels and 10,000 με. This transition occurred after 30%–40% of the speckles disappeared, depending on characteristics of the speckle pattern and its degradation mechanisms. These results suggest that accurate DVC measurements can be obtained in many types of fragile materials despite severe damage to the speckle pattern.
Graph Learning Based on Spatiotemporal Smoothness for Time-Varying Graph Signal

Graph learning often boils down to uncovering the hidden structure of data, which has been applied in various fields such as biology, sociology, and environmental studies. However, distributed sensing in realistic application often gives rise to spatiotemporal signals, which can be characterized through new tools of graph signal processing as a time-varying graph signal. It calls upon the development from static graph signal studies to the joint space-time analysis. In this paper, we study the problem of learning graphs from time-varying graph signals. Based on the correlated properties in observed signals, a dynamic graph-based model is first presented, which particularly takes into account space-time interactions in signal representation. Considering the case that the time correlation pattern is unavailable, the graph learning problem is cast as a joint correlation detecting and graph refining problem. Then it is solved by the proposed correlation-aware and spatiotemporal smoothness-based graph learning method (CASTS), which novelly introduces the spatiotemporal smooth prior to the field of time-vertex signal analysis. By promoting such smoothness in each learning steps, the graph learning accuracy can be efficiently improved. The experiments on both synthetic and real-world datasets demonstrate the improvement of the proposed CASTS over current state-of-the-art graph learning methods, and meanwhile show the capability of dynamic prediction in climate analysis.
A Multi-dimensional Adversary Analysis of RSA and ECC in Blockchain Encryption

During this current age of big data, the security of sensitive data in the cyberspace has become utmost important. Blockchain, as a new age technology, provides the necessary tools to ensure data integrity and data protection using some encryption. Smaller transaction size and higher transaction efficiency are the essential requirements of the blockchain. However, these requirements are closely related to the efficiency of the encryption algorithms that blockchain uses. In this paper, we have analyzed and compared the performance of Rivest-Shamir-Adleman (RSA) algorithm and Elliptic Curve Cryptography (ECC) algorithm that is most commonly used in blockchain by having a general consideration of a transaction size and transaction efficiency. We aim to provide a better understanding of the reason behind their extensive use in the blockchain. We hope that our evaluation and analysis of these two encryption algorithms can help promote the further development of blockchain.
Cryptanalysis of a Certificateless Aggregate Signature Scheme for Healthcare Wireless Sensor Network

Certificateless aggregate signatures aggregate <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mrow><mml:mi>n</mml:mi></mml:mrow></mml:math> signatures from <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M2"><mml:mrow><mml:mi>n</mml:mi></mml:mrow></mml:math> different users into one signature. Therefore, a verifier can judge whether all signatures are valid by verifying once. With this advantage, certificateless aggregate signatures are widely used in the environment of limited computing resources. Recently, a novel certificateless aggregate signature scheme was proposed by Kumar et al. This scheme’s security was claimed to be secure against two types of attackers under the random oracle model. In this paper, we indicate that their scheme is unable to achieve this security goal. We show an attack algorithm that the second type of attacker could forge a valid signature under an identity without the private key of the target user. Moreover, we demonstrate that the second type of attacker could forge a valid aggregate signature.
Curvature-Bounded Lengthening and Shortening for Restricted Vehicle Path Planning

In this paper, the traditional shortest path planning problem for vehicle is advanced to length-targeted path planning problem, i.e., to plan path with its length being as close to a specified value as possible. Lengthening and shortening of given initial paths are used to solve this problem. Based on an operation set consisting of three basic path homotopies, we build a comprehensive and systematic framework to plan paths with target length, which is a generalization of the existing related studies. Thereby, the expected paths can be independently searched through such deformation processes within topological classes. The proposed framework can produce the largest length coverage in different scenarios and under different conditions, and it can also generate expected paths with arbitrary topological classification in terms of the curvature constraint and the obstacle constraint. Examples show that our lengthening and shortening method can effectively solve the length-targeted path planning problem in environment without or with obstacles.
Dimensionality Reduction for Tukey Regression.

We give the first dimensionality reduction methods for the overconstrained Tukey regression problem. The Tukey loss function $\|y\|_M = \sum_i M(y_i)$ has $M(y_i) \approx |y_i|^p$ for residual errors $y_i$ smaller than a prescribed threshold $\tau$, but $M(y_i)$ becomes constant for errors $|y_i| > \tau$. Our results depend on a new structural result, proven constructively, showing that for any $d$-dimensional subspace $L \subset \mathbb{R}^n$, there is a fixed bounded-size subset of coordinates containing, for every $y \in L$, all the large coordinates, with respect to the Tukey loss function, of $y$. Our methods reduce a given Tukey regression problem to a smaller weighted version, whose solution is a provably good approximate solution to the original problem. Our reductions are fast, simple and easy to implement, and we give empirical results demonstrating their practicality, using existing heuristic solvers for the small versions. We also give exponential-time algorithms giving provably good solutions, and hardness results suggesting that a significant speedup in the worst case is unlikely.
ABCD-Strategy: Budgeted Experimental Design for Targeted Causal Structure Discovery

Determining the causal structure of a set of variables is critical for both scientific inquiry and decision-making. However, this is often challenging in practice due to limited interventional data. Given that randomized experiments are usually expensive to perform, we propose a general framework and theory based on optimal Bayesian experimental design to select experiments for targeted causal discovery. That is, we assume the experimenter is interested in learning some function of the unknown graph (e.g., all descendants of a target node) subject to design constraints such as limits on the number of samples and rounds of experimentation. While it is in general computationally intractable to select an optimal experimental design strategy, we provide a tractable implementation with provable guarantees on both approximation and optimization quality based on submodularity. We evaluate the efficacy of our proposed method on both synthetic and real datasets, thereby demonstrating that our method realizes considerable performance gains over baseline strategies such as random sampling.
An Improved Random Forest Algorithm for classification in an imbalanced dataset

Nowadays machine learning algorithms are being used extensively in industrial applications. Many a times these algorithms are modified and fine tuned so as to improve the current products and get better results. In this paper, we analyse an industrial problem that was put forward in the ‘IDA 2016 challenge’ and propose an improved solution over the best solution identified as part of the challenge.
ASIS: Autonomous Seismic Imaging System With In Situ Data Analytics and Renewable Energy

Understanding earthquakes and volcano eruptions phenomena require the analysis of large amounts of subsurface data. Currently, data collected from these events need post-processing in an off-line mode due to hardware limitations and strong bandwidth constraints. Furthermore, analytics may take days, even weeks, in being processed after collection. In some cases, underground structures require previous studies and subsurface knowledge from experts to establish certain input parameters. By leveraging current IoT technologies, we introduce an autonomous seismic imaging system (ASIS), a real-time system for monitoring and generating analytics of seismic data based on a sensor network. ASIS processes real-time analytics near where the events are generated (fog computing) to mitigate bandwidth limitations. Calculations for earthquake detection, location, and magnitude are processed in situ . ASIS allows monitoring sensor status, visualizing the data of each sensor, and generating two dimensional/three dimensional (2-D/3-D) subsurface structures. The system is able to learn the underground structure by taking advantage of ambient noise data analysis, which significantly reduces the need of initial parameters. We incorporated a renewable energy source to extend sensor functionality, and we made the system self-healing and fault-tolerant. Indoor and outdoor evaluation of the system showed the error of earthquake detection is 13.7 ms and the spatial location accuracy is 5.1 ft.