Reviewerly/exHarmony huggingface dataset:
Predicting Patient Outcomes with Graph Representation Learning

Recent work on predicting patient outcomes in the Intensive Care Unit (ICU) has focused heavily on the physiological time series data, largely ignoring sparse data such as diagnoses and medications. When they are included, they are usually concatenated in the late stages of a model, which may struggle to learn from rarer disease patterns. Instead, we propose a strategy to exploit diagnoses as relational information by connecting similar patients in a graph. To this end, we propose LSTM-GNN for patient outcome prediction tasks: a hybrid model combining Long Short-Term Memory networks (LSTMs) for extracting temporal features and Graph Neural Networks (GNNs) for extracting the patient neighbourhood information. We demonstrate that LSTM-GNNs outperform the LSTM-only baseline on length of stay prediction tasks on the eICU database. More generally, our results indicate that exploiting information from neighbouring patient cases using graph neural networks is a promising research direction, yielding tangible returns in supervised learning performance on Electronic Health Records.
Fast Approximation of Optimal Perturbed Long-Duration Impulsive Transfers via Artificial Neural Networks

The design of multitarget rendezvous missions requires a method to quickly and accurately approximate the optimal transfer between any two rendezvous targets. In this article, an artificial neural-network-based method is proposed for the rapid approximation of optimal perturbed long-duration impulsive transfers. The relationship between the optimal transfer velocity increments and the initial right ascension of the ascending node difference between the departure body and the rendezvous target is analyzed, and the result suggests that the perturbed long-duration impulsive transfers should be divided into three types. An efficient database generation method is developed. Three regression multilayer perceptrons (MLPs) are trained individually and applied to approximate the corresponding types of transfers. The simulation results show that the well-trained MLPs are capable of quickly estimating the optimal velocity increments with a relative error of less than 3% for all three types of transfers. Additional tests of the debris chains with total velocity increments of several thousand m/s show that the estimation results are very close to the optimized results, with a final estimation error of less than 10 m/s.
WITHDRAWN: Analysis of twitter data through big data based sentiment analysis approaches

The common and various forms of Twitter information render this one of the best controlling and recording virtual environments of information. The growth in social media nowadays gives internet users immense interest. In several pups like prediction, advertisement, sentiment analysis …, the data on such a social network platform is used. People exchange good or bad views on problems, items and administrations through the web and informal communities. The capacity to assess such a data productively is presently observed as a noteworthy upper hand in settling on choices all the more proficiently. In this sense, associations use methods, for example, Sentiment Analysis (SA). The utilization of web based life around the globe is growing, however, greatly speeding up mass data generation and stopping us from providing useful insights in conventional SA systems. These data volumes can be processed effectively, using SA and Big Data technology. Big data is not a luxury, in fact, but an important prediction.
Rethinking Scope Islands

Relative clauses and tensed clauses are standardly assumed to be scope islands. However, naturally occurring counterexamples are abundant and easy to find. Therefore, we should revisit analyses that reject Quantifier Raising on the assumption that QR is clause-bounded. The data show that scope islands are sensitive to the identity of both the scope-taker and the predicate embedding the island. I propose the Scope Island Subset Constraint: given two scope islands, the scope-takers trapped by one will be a subset of the scope-takers trapped by the other. A simple refinement of semantic types allows encoding and enforcing of scope islands.
Vehicle Type Recognition Algorithm Based on Improved Network in Network

Vehicle type recognition algorithms are broadly used in intelligent transportation, but the accuracy of the algorithms cannot meet the requirements of production application. For the high efficiency of the multilayer perceptive layer of Network in Network (NIN), the nonlinear features of local receptive field images can be extracted. Global average pooling (GAP) can avoid the network from overfitting, and small convolution kernel can decrease the dimensionality of the feature map, as well as downregulate the number of model training parameters. On that basis, the residual error is adopted to build a novel NIN model by altering the size and layout of the original convolution kernel of NIN. The feasibility of the algorithm is verified based on the Stanford Cars dataset. By properly setting weights and learning rates, the accuracy of the NIN model for vehicle type recognition reaches 97.2%.
Identification/segmentation of indian regional languages with singular value decomposition based feature embedding

Language identification (LID) is identifying a language in a given spoken utterance. Language segmentation is equally important as language identification where language boundaries can be spotted in a multi-language utterance. Language identification could be a trivial front-end process for real-time mixed-speech recognition applications. India is a multilingual country and mixing two languages in a single conversation is very usual. In this paper, we have experimented with two schemes for language identification in Indian regional language context as very few works have been done. Singular value-based feature embedding is used for both of the schemes. In the first scheme, the singular value decomposition (SVD) is applied to the n-gram utterance matrix and in the second scheme, SVD is applied to the difference supervector matrix space. We have observed that in both the schemes, 55–65% singular value energy is sufficient to capture the language context. We have also seen how these two schemes are preserving language context. In n-gram based feature representation, we have seen that different skipgram models capture different language context. We have observed that for short test duration, supervector based feature representation is better but with a longer duration test signal, n-gram based feature performed better. We have also extended our work to explore language-based segmentation, where we have seen that segmentation accuracy of four language group with ten language training model, scheme-1 has performed well but with same four language training model, scheme-2 has shown better accuracy. In a multilingual language setup, the language-based identification and segmentation will be useful to identify the language as well as the duration of its presence. Further, the language-specific model can be used to identify the speech.
A Study and Analysis of Applications of Classical Computing and Quantum Computing: A Survey

In the world of computation, the invention of Computers has reduced human effort for accomplishing task. Depending on the type, size and nature of the work the computers are manufactured in different magnitudes. As the technology grows, it facilitated to manufacture high performing, high throughput, and high memory Classical computers. Classical computers store any information in the form of bits in the memory device, which can be either 1 or 0 (binary system) and all the processing will be done in the same logic i.e., high voltage/low voltage. Because of this logic the classical computers solve some problems by using more resources. Quantum computer are the next level computers which uses quantum theory principles for solving problems. The quantum computer uses Qubit as basic unit which uses principles of quantum computing such as superposition and entanglement, which gives qubit, the efficiency of exhibiting many logical states at a time. This paper shows a comparative study on classical and quantum computers and illustrates various problems in classical computers and respective solutions with use of quantum computers.
An Argumentation-Based Approach to Generate Domain-Specific Explanations

In argumentation theory, argument schemes are constructs to generalise common patterns of reasoning; whereas critical questions (CQs) capture the reasons why argument schemes might not generate arguments. Argument schemes together with CQs are widely used to instantiate arguments; however when it comes to making decisions, much less attention has been paid to the attacks among arguments. This paper provides a high-level description of the key elements necessary for the formalisation of argumentation frameworks such as argument schemes and CQs. Attack schemes are then introduced to represent attacks among arguments, which enable the definition of domain-specific attacks. One algorithm is articulated to operationalise the use of schemes to generate an argumentation framework, and another algorithm to support decision making by generating domain-specific explanations. Such algorithms can then be used by agents to make recommendations and to provide explanations for humans. The applicability of this approach is demonstrated within the context of a medical case study.
The phase matching quantum key distribution protocol with 3-state systems

Quantum key distribution allows two remote parties to generate a theoretically secure key. However, its application still faces a great challenge, that is, the fundamental linear secret key-rate constraint. Recently, several QKD schemes have been presented to obtain a secret key rate which is higher than that of the previous schemes. Based on QKD scheme, a phase-matching quantum key distribution protocol with 3-state system is proposed for the first time in this paper. Here, each successful detection is responded in three different ways, and the two parties obtain the key through “flip and flip,” so this greatly improves the success rate of the phase-matching and makes the final key rate higher than that of the previous scheme. The simulation results show that the proposed protocol breaks the limit of linear key generation rate in a shorter distance, and the longest practical transmission distance is about 470 km.
Fast point cloud registration in multidirectional affine transformation

This paper proposed a fast multidirectional affine registration (FMDAR) algorithm for matching two three-dimensional point clouds after multidirectional affine transformation with disorder, noise and missing points. The FMDAR algorithm is based on the statistical characteristics and shape features of point clouds. First, the eigenvalues of the point clouds matrix and the scaling rations are used to establish a system of nonlinear equations by the Vieta's Formulas. In addition, the rotational invariance of global vector features as a constraint is introduced and the cost function is established. Secondly, the scaling rations are calculated by using the Trust Region method to optimize the cost function. Finally, the multidirectional affine registration is transformed into a rigid registration after the scale rations are obtained. The FMDAR algorithm is robust in the presence of noises and we validate the FMDAR algorithm on three-dimensional point clouds with varying degrees of deformation in complex cases. What’s more, results of the simulation show that the FMDAR algorithm has faster registration speed and higher accuracy compared with some existing algorithms.
Automatic Uniform Quantum State Preparation Using Decision Diagrams

Most quantum algorithms assume some specific initial state in superposition of basis states before performing the desired application-specific computations. The preparation of such states itself requires a computation performed by a quantum circuit. In this paper, we investigate the automatic state preparation of a specific subset of quantum states that are uniform superpositions over a subset of basis states, called uniform quantum states. We exploit that such states can be represented using Boolean functions and present a recursive algorithm based on functional decomposition. When using binary decision diagrams as function representation, we can enable fast and scalable quantum state preparation with respect to the size of the decision diagram. We show that the algorithm can find quantum circuits for functions, where state-of-the-art algorithms cannot be applied anymore.
Line following robots on factory floors: Significance and Simulation study using CoppeliaSim

Abstract Line following robots has been around for many years now. Research and development of these robots have been keenly studied and examined for many industrial applications. Line following robots can provide maximum effectiveness to in-house transportation of jobs inside a factory, which is traditionally done by forklifts and different types of cranes. These traditional methods are high maintenance and extremely unsafe for the employees. This paper presents the design, assembly and dynamic simulation of a line following robot integrated with a proximity sensor for collision avoidance and vision sensors for line tracking. The model is designed using Autodesk Inventor 2018 and the assembly and simulation is carried out using CoppeliaSim software. This simulation study intends to provide significance of the implementation of such robots on a factory floor, which can cover a path of distance 10 meters in approximately 8 seconds.
Low overhead performance monitoring for shared infrastructures

Abstract Modern systems make use of virtualization to build scalable and elastic applications. A major example is cloud computing, which has emerged in the last decades as a cost-effective paradigm for hosting and delivering services over the Internet. Cloud providers adopt server consolidation strategies to manage resources, reducing underutilized hosts. However, collocated applications compete for physical resources. So, their particularities, as well as workload fluctuations, may negatively affect cloud applications’ performance. Performance monitors are useful tools for tracking performance behavior. The observation of events occurring in the system provides a detailed view of how applications affect resource usage. This way, performance counters can assist developers and enable the detection of bottlenecks, allowing administrators to verify whether service level agreements are met. However, the monitoring itself can be a contention source, especially in large scale systems, where hundreds of performance counters are collected, and the acquired data are stored at a high frequency. In this work, the influence of monitoring overhead in virtualized environments is analyzed. As a mean to reduce shared resource contention, it is proposed an approach to alleviate monitoring costs, bringing as benefits: an automatic method for performance counters selection; and overhead reduction by discarding unnecessary performance counters. Through experimental evaluation, this work characterizes the monitoring overhead in virtualized environments. A performance comparison using both the complete and the reduced set of performance counters highlights the benefits of adopting the proposed approach.
Digital technology adoption in a bank Treasury and performing a Digital Maturity Assessment

The importance of a Treasury department in a commercial bank has increased in recent decades. After the 2008 financial crisis the remit of a bank Treasury has expanded in terms of scope and strategic significance, evolving from a transactional cash manager to a balance sheet custodian. In order to meet this broader strategic mandate Treasurers must consider ways to become more efficient and streamlined. Adoption of digital technologies and related innovation that underpins the Fourth Industrial Revolution can address many of the traditional Treasury challenges and deliver a wide range of benefits. The adoption of digital technology in bank Treasuries tend to be lower than in other business areas within a bank. There are a number of reasons, but it often includes the lack of a well-articulated digital transformation roadmap. There is thus a need for researching a Treasury specific approach that can provide guidance on developing a digital implementation strategy and enable the transition towards a next generation ‘smart’ digital Treasury department. This paper focusses on a key component of digital transformation, namely performing a Digital Maturity Assessment. It is essential to determine the digital maturity level of a Treasury function, in order to ensure the appropriate Treasury activities and digital technologies are identified and prioritized for digitalization.
Speech recognition system for a service robot - a performance evaluation

In this work we adapt and evaluate different solutions for automatic speech recognition (ASR) to be used as an HMI for the assistant robot. Two on-device solutions: Kaldi (DNN-HMM) and Mozilla's DeepSpeech (end-to-end), and three internet service APIs: IBM Watson, Microsoft Azure and Google Speech to Text are evaluated. The systems are adapted to the domain of robot commands and evaluated on a set of expected inputs. As the goal is to retain the ability to recognise general language, the systems are also evaluated on out of domain data.
Cooperative Training of Triplet Networks for Cross-Domain Matching

Recently, Deep Convolutional Neural Networks have been applied to various computer vision problems and achieved state-of-the-art results. Among these, Siamese and Triplet networks have obtained great traction in intra-domain matching. However, it is impossible to directly use these networks in cross-domain problems. Thus, this paper proposes a data-driven approach for cross-domain matching of complex data that do not share similar features. A pair of triplet networks are trained with a new cooperative approach to perform Deep Metric Learning. In order to validate our proposed method, we apply it to a cross-domain image matching problem that aims to assist with underwater robot localization. We train a pair of networks using our methodology on a dataset composed of acoustic and segmented aerial images and evaluate it on a dataset acquired in another location. Our results show that our method is able to achieve up to 83% accuracy in matching acoustic and segmented aerial images.
Effective Knowledge Discovery Using Data Mining Algorithm

In present data science world, data is primary for any analytics, analysis, mining, prediction, and description activity. Although many steps are defined and actively used in random for cleaning and preprocessing of dataset previously but there exist some gaps, degrading the overall quality of data as well as knowledge discovery procedure. In this work, some major additional activities are identified in data manipulation paradigm that can enhance decision-making capability of any mining algorithm. With introduction of new methods at collection and cleaning levels, a data preprocessing algorithm is also proposed here. The improvement in overall knowledge discovery process is demonstrated using a real-time dataset.
Benefiting from Bicubically Down-Sampled Images for Learning Real-World Image Super-Resolution

Super-resolution (SR) has traditionally been based on pairs of high-resolution images (HR) and their low-resolution (LR) counterparts obtained artificially with bicubic downsampling. However, in real-world SR, there is a large variety of realistic image degradations and analytically modeling these realistic degradations can prove quite difficult. In this work, we propose to handle real-world SR by splitting this ill-posed problem into two comparatively more well-posed steps. First, we train a network to transform real LR images to the space of bicubically down-sampled images in a supervised manner, by using both real LR/HR pairs and synthetic pairs. Second, we take a generic SR network trained on bicubically downsampled images to super-resolve the transformed LR image. The first step of the pipeline addresses the problem by registering the large variety of degraded images to a common, well understood space of images. The second step then leverages the already impressive performance of SR on bicubically downsampled images, sidestepping the issues of end-to-end training on datasets with many different image degradations. We demonstrate the effectiveness of our proposed method by comparing it to recent methods in real-world SR and show that our proposed approach outperforms the state-of-the-art works in terms of both qualitative and quantitative results, as well as results of an extensive user study conducted on several real image datasets.
Extractive Single Document Summarization Using an Archive-Based Micro Genetic-2

Research on automatic text summarization is growing with the rapid growth of online information services. In the current study, Extractive Single Document Summarization (ESDS) is formulated as a multi-objective optimization problem, and an Archive-based Micro Genetic-2 Algorithm (AMGA2) is employed to solve this problem. In this approach, each summary is represented in a binary form. The initial population is created using the clustering and Latin Hypercube (LH) sampling, resulting in a good overall unbiased population distribution in the genotypic space. Two objective functions were formed considering content coverage and informativeness as objective function 1, and anti-redundancy as objective function 2. The summary length limit is considered as the constraint. For generating offspring, an archive-based mating pool and modified mutation operator of differential evaluation are used. Finally, from the optimal set of solutions, the best solution is selected to generate the summary. For evaluation, DUC-2001 and DUC-2002 datasets are utilized, and the obtained results are compared with various existing methods using ROUGE measures. The obtained result illustrates the superiority of our approach in terms of convergence rate and ROUGE scores.
Clustering Collision Power Attack on RSA-CRT

In this paper, we propose two new attack algorithms on RSA implementations with CRT (Chinese remainder theorem).To improve the attack efficiency considerably, a clustering collision power attack on RSA with CRT is introduced via chosen-message pairs.This attack method is that the key parameters d p and d q are segmented by byte, and the modular multiplication collisions are identified by k-means clustering.The exponents d p and d q were recovered by 12 power traces of six groups of the specific message pairs, and the exponent d was obtained.We also propose a second order clustering collision power analysis attack against RSA implementation with CRT, which applies double blinding exponentiation.To reduce noise and artificial participation, we analyze the power points of interest by preprocessing and k-means clustering with horizontal correlation collisions.Thus, we recovered approximately 91% of the secret exponents manipulated with a single power curve on RSA-CRT with countermeasures of double blinding methods.