Reviewerly/exHarmony huggingface dataset:
Federated Data Science to Break Down Silos [Vision]

Similar to Open Data initiatives, data science as a community has launched initiatives for sharing not only data but entire pipelines, derivatives, artifacts, etc. (Open Data Science). However, the few efforts that exist focus on the technical part on how to facilitate sharing, conversion, etc. This vision paper goes a step further and proposes KEK, an open federated data science platform that does not only allow for sharing data science pipelines and their (meta)data but also provides methods for efficient search and, in the ideal case, even allows for combining and defining pipelines across platforms in a federated manner. In doing so, KEK addresses the so far neglected challenge of actually finding artifacts that are semantically related and that can be combined to achieve a certain goal.
Research on Intelligent Vehicle Trajectory Planning and Control Based on an Improved Terminal Sliding Mode

Aiming at precisely tracking an intelligent vehicle on a desired trajectory, this paper proposes an intelligent vehicle trajectory planning and control strategy based on an improved terminal sliding mold. Firstly, the traditional RRT algorithm is improved by using the target bias strategy and the separation axis theorem to improve the algorithm search efficiency. Secondly, an improved terminal sliding mode controller is designed. The controller comprehensively considers the lateral error and heading error of the tracking control, and the stability of the control system is proven by the Lyapunov function. Finally, the performance of the designed controller is verified by the Matlab-Carsim HIL simulation platform. The test results of the Matlab-Carsim HIL simulation platform show that, compared with the general terminal sliding mode controller, the improved terminal sliding mode controller designed in this paper has higher control accuracy and better robustness.
Consumer Perception of Internet Banking and Mobile Banking Using Twitter Analytics

Mobile and Internet banking have introduced a new way of monetary transactions without the need for physical presence. This research proposes to analyze the sentiments of people regarding digital transactions, Mobile and Internet banking. The explosion of Internet usage and the huge funding initiatives in electronic banking has drawn the attention of researchers towards Internet and mobile banking. This study focuses on customer value perceptions of the Internet and mobile banking in India. The recent and forecasted Digital India scheme shows high growth in e-banking in India. The demographic, attitudinal, and behavioral characteristics of mobile bank users were examined. In this study, datasets obtained from Twitter were used. After extensive and repeated analysis, it is found that both Mobile and Internet banking are well received, the number of positive Tweets, especially regarding mobile banking, is much higher than that of Internet banking. This leads to the interpretation that people find mobile banking easier and safer, especially during the ongoing COVID-19 pandemic.
Post-quantum Security of Plain OAEP Transform

In this paper, we show that OAEP transform is indistinguishable under chosen ciphertext attack in the quantum random oracle model if the underlying trapdoor permutation is quantum partial-domain one-way. The existing post-quantum security of OAEP (TCC 2016-B [14]) requires a modification to the OAEP transform using an extra hash function. We prove the security of the OAEP transform without any modification and this answers an open question in one of the finalists of NIST competition, NTRU submission [6], affirmatively.
Design and Implementation of Multipurpose Chatbot

Chatbots, also called chatterbots are applications that simulate human conversation either taking voice or text commands or both as input. They are automated programs that utilize Natural language processing techniques to understand process and responds accordingly to the users. Chatbots are cost effective and require a very little investment cost in the beginning stage of development. The software program can be built using AIML that use Extensible Mark-up Language as a base. In python, libraries like NLTK and JSON package can be used for the efficient designing of a Chatbot model. Chatbots use Corpus which is the vocabulary or the huge amount of data from different human dataset to the model. This paper elaborates on a Chatbot model that uses the functions of nltk library in python to pre-process data and responds accurately to the user using the cosine similarity approach. This chatbot can be implemented for any specific domain by changing the corpus. The corpus used in this design is about chatbots itself. The chatbot is capable of accurately responding to the queries related to chatbots. Accuracy of the proposed chatbot is 93% that is out of 150 queries it answered 140 queries correctly.
Photocount statistics of the Josephson parametric amplifier

Parametric amplifiers are known to squeeze the vacuum state of the electromagnetic field, which results in predictable statistics of the photocounts at their output. However, recent theoretical work arXiv:1112.4159 predicts a very different statistical distribution for an amplifier based on a Josephson junction. We test the hypothesis experimentally and recover the expected squeezed vacuum statistics. We explain this discrepancy by showing theoretically how the photocount statistics is dictated by the detection process, from single mode (our experiment) to multimode, fully resolved in frequency (as in arXiv:1112.4159).
Improving Feature Discrimination for Object Tracking by Structural-similarity-based Metric Learning

Existing approaches usually form the tracking task as an appearance matching procedure. However, the discrimination ability of appearance features is insufficient in these trackers, which is caused by their weak feature supervision constraints and inadequate exploitation of spatial contexts. To tackle this issue, this article proposes a novel appearance matching tracking (AMT) method to strengthen the feature restraints and capture discriminative spatial representations. Specifically, we first utilize a triplet structural loss function, which improves the learning capability of features by applying a structural similarity constraint with a triplet metric format on the features. It leverages feature statistics to capture the complex interactions of visual parts. Second, we put forward an adaptive matching module that exploits the dual spatial enhancement module to reinforce target feature discrimination. This not only boosts the representation ability of spatial context but also realizes spatially dynamic feature selection by attending to target deformation information. Moreover, this model introduces a simple but effective matching unit to intuitively evaluate the relative appearance differences between the target and the proposals. In addition, with the obtained discriminative features, AMT is capable of providing precise localization for the target. Therefore, the impact of spatial suppression imposed by window functions can be alleviated, allowing for effective tracking of high-speed moving objects. Extensive experiments prove that AMT outperforms state-of-the-art methods on six public datasets and demonstrate the effectiveness of each component in AMT.
A Multi-task based Bilateral-Branch Network for Imbalanced Citation Intent Classification

Identifying the purpose of citations plays an important role in evaluating the impact of the literature. There is a data imbalanced problem on different types of citation intents which harms the performance of the classification model. To alleviate this problem, We adapt the bilateral-branch network proposed in the computer vision domain to our topic in the natural language processing domain by constructing shared and non-shared encoder layers using pre-trained language model and word attention layer respectively. In addition, to learn rich representations by leveraging the auxiliary information, we propose a multi-task based bilateral-branch network. On the issue of how to integrate multi-task model and bilateral-branch network, because one advantage of multi-task learning is using more data or information to learn better representations, we propose a solution of integrating the networks of the auxiliary tasks with the representation learning branch of the bilateral- branch network. The experimental results show that our model outperforms other models used for citation intent classification.
Machine Learning Solutions for Investigating Streams Data using Distributed Frameworks: Literature Review

Distributed Frameworks such as Apache Spark have become an integral part of data analysis in the modern world. Machine learning algorithms implemented in distributed frameworks have become popular due to their impressive processing power. Research now is shifting to the analysis of data in a streaming environment that has data coming in real-time. This research focuses on identifying and conducting a literature review that investigates machine learning methods in a distributed framework using streams data. The review identifies some interesting patterns in the focus of machine learning algorithms and the use of various distributed frameworks. A lot of research opportunities are discussed while discussing research papers.
Improved List-Decodability and List-Recoverability of Reed-Solomon Codes via Tree Packings: [Extended Abstract]

This paper shows that there exist Reed-Solomon (RS) codes, over large finite fields, that are combinatorially list-decodable well beyond the Johnson radius, in fact almost achieving list-decoding capacity. In particular, we show that for any ε E (0,1] there exist RS codes with rate <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\Omega(\frac{\varepsilon}{1\not\varepsilon(1/_{\in})+1})$</tex> that are list-decodable from radius of 1-ε. We generalize this result to list-recovery, showing that there exist <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(1-\varepsilon,\ell, O(\ell/\varepsilon))$</tex> -list-recoverable RS codes with rate <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\Omega\left(\frac{\varepsilon}{\sqrt{\ell}(\log(1/\varepsilon)+1)}\right)$</tex> . Along the way we use our techniques to give a new proof of a result of Blackburn on optimal linear perfect hash matrices, and strengthen it to obtain a construction of strongly perfect hash matrices. To derive the results in this paper we show a surprising connection of the above problems to graph theory, and in particular to the tree packing theorem of Nash-Williams and Tutte. We also state a new conjecture that generalizes the tree-packing theorem to hypergraphs, and show that if this conjecture holds, then there would exist RS codes that are optimally (non-asymptotically) list-decodable. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> A full version of this paper is available online at https://arxiv.org/abs/2011.04453.
Attentive Octave Convolutional Capsule Network for Medical Image Classification

Medical image classification plays an essential role in disease diagnosis and clinical treatment. More and more research efforts have been dedicated to the design of effective methods for medical image classification. As an effective framework, the capsule network (CapsNet) can realize translation equivariance. Lots of current research applies capsule networks in medical image analysis. In this paper, we propose an attentive octave convolutional capsule network (AOC-Caps) for medical image classification. In AOC-Caps, an AOC module is used to replace the traditional convolution operation. The purpose of the AOC module is to process and fuse the high- and low-frequency information in the input image simultaneously, and weigh the important parts automatically. Following the AOC module, a matrix capsule is used and the expectation maximization (EM) algorithm is applied to update the routing weights. The proposed AOC-Caps and comparative methods are tested on seven datasets, including PathMNIST, DermaMNIST, OCTMNIST, PneumoniaMNIST, OrganMNIST_Axial, OrganMNIST_Coronal, and OrganMNIST_Sagittal, which are from MedMNIST. In the experiments, baselines include the traditional CNN models, automated machine learning (AutoML) methods, and related capsule network methods. The experimental results demonstrate that the proposed AOC-Caps achieves better performance on most of the seven medical image datasets.
Application of Blockchain Digital Identity Technology in Healthcare Consumer Finance System

In order to solve the problems of information leakage, data silos and uncontrolled identity caused by centralized storage in the existing healthcare consumer finance system, this paper proposes a design of healthcare consumer finance system based on distributed digital identity architecture. The article introduces the overall architecture and application process of the system, and builds a corresponding demonstration system based on Hyperledger Indy blockchain technology platform, implements the key functions of the system and conducts feasibility verification. It is proved that the system model has good security and time efficiency, and can realize secure and efficient information sharing between financial institutions and financial consumers.
Object Region and Class Learning for Weakly-Supervised Semantic Segmentation

In this paper, we study the weakly-supervised semantic segmentation problem that trains the segmentation network using image-level label. In conventional WSSS approaches, to train the segmentation network in the absence of the pixel-level labels, the classification network is used to generate the pseudo-label. The potential problems in the conventional approaches are: 1) the labeled regions in pseudo-label is small and sparse, 2) the pseudo-labels for complex images are inaccurate. To handle this, we propose a novel WSSS framework that can train the segmentation network without generating the pseudo-label. By masking the input image using the segmented output and delivering the masked image to the classification network, the segmentation network is penalized if the segmented output is inaccurate. We show that the proposed approach can effectively train the segmentation network.
A visualization technique to assist in the comparison of large meteorological datasets

The comparison of meteorological data is a fundamental task within the techniques of meteorological forecasting due to the cyclical nature of the climate. However, interpretation of meteorological datasets can be difficult due to their large size. The goal of information visualization is to support a better understanding of the data what includes assisting the user in the data comparison process. However, few visualization techniques have been specifically developed to support the comparison process of big meteorological data. In this paper, we improved a visualization technique for large time-series comparison. The new technique is more suitable for the comparison of meteorological data.
Hypothesis of Cyclic Structures of Pre- and Consciousness as a Transition in Neuron-like Graphs to a Special Type of Symmetry

We study the proposed statistical kinetic model for describing the pre- and consciousness structures based on the cognitive neural networks. The method of statistics of the growth graph systems and a possible transition to symmetric structures (a kind of phase transition) is applied. With the complication of a random Erdőos-Rényi (ER) graph during the percolation transition from the tree structure to the large cluster structures is obtained. In the evolutionary model two classes of algorithms have been developed. The differences between the cycle parameters in the obtained neural network models can reach thousands or more times. This is due to the tree-like architecture of the neural graph, which mimics the columnar structures of the neocortex. These cluster and cyclic structures can be interpreted as the primary elements of consciousness and as a necessary condition for the effect of consciousness itself. The comparison with other known theoretical mainly statistical models of consciousness is discussed. The presented results are promising in neurocomputer interfaces, man-machine systems and artificial intelligence systems.
Modified Viterbi-Based Local-Multipath Doppler Difference Estimation in Over-the-Horizon Radar

In sky-wave over-the-horizon radar, local multipath returns from a maneuvering target, generated as a result of reflections from ionosphere and earth surfaces, generally yield three distinct components with highly time-varying and closely separated Doppler frequencies. The difference between these Doppler signatures provides important information on target velocity in the elevation direction and enables high-resolution target altitude estimation. However, it is often a challenging task to accurately estimate this Doppler difference due to the proximity of the Doppler components. In this paper, we develop a low-complexity modified Viterbi-based approach to provide improved Doppler difference estimation in impaired observing conditions. Simulation results verify the effectiveness of the proposed method.
Image denoising based on global image similar patches searching and HOSVD to patches tensor

Abstract Nonlocal self-similarity of natural image is an essential property for image processing. But how to measure the similarity between different patches and how to better exploit the similarity of patches are two crucial problems for image denoising. In this article, we establish a novel image denoising method based on a global image similar patches searching method and the similar patches tensor high-order singular value decomposition theory. Particularly, in order to find the reasonable similar patches to a reference, a variant of Gaussian mixture model (GMM) global similar patches searching method is proposed, and the graph process unit-based GMM model training method is performed to speed up the training process. Furthermore, the k -means and the local inter-class searching are used to improve on the similarity measure between patches. To better exploit the similarity of patches to image denoising, we rearrange similar patches to a tensor for each reference, and an iterative adaptive weighted tensor low-rank approximation method is established to perform image denoising. Experimental results clearly show that the proposed method is comparable to many recent denoising algorithms from the PNSR, SSIM and NCC viewpoint. For higher noise level (e.g., $$sigma=50$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>s</mml:mi> <mml:mi>i</mml:mi> <mml:mi>g</mml:mi> <mml:mi>m</mml:mi> <mml:mi>a</mml:mi> <mml:mo>=</mml:mo> <mml:mn>50</mml:mn> </mml:mrow> </mml:math> ), the performance of our proposed algorithm evinces a 0.06-unit, 0.05-unit and 0.12-unit improvement in the PNSR, SSIM and NCC score, respectively, when compared to state-of-the-art.
Speech Emotion Recognition Using Multi-Layer Sparse Auto-Encoder Extreme Learning Machine and Spectral/Spectro-Temporal Features with New Weighting Method for Data Imbalance

The importance of doing research into affective computing has multiplied with the growing popularity of intelligent and human-machine interface systems. In this research, a speech emotion recognition (SER) system is proposed using new techniques in different parts. The given system extracts speech features from speech and glottal signals in feature extraction section including spectro-temporal ones obtained from Gabor filter bank (GBFB) and separate Gabor filter bank (SGBFB) which have not been so far utilized for SER. At the classification step, a hierarchical adaptive weighted multi-layer extreme learning machine (H-AWELM) is employed. This hybrid classifier consists of two parts: the first part for sparse unsupervised feature learning using a multi-layer neural network (NN) with sparse extreme learning machine auto-encoder (ELM-AE) layers, and the second part for feature classification in the last layer using Tikhonov’s regularized least squares (LS) technique. One of the most important problems in multi-class ELM training process is how to deal with data imbalance issue. This paper presents an adaptive weighting method to solve this problem that can be more accurate than current weighting methods. Finally, the proposed system is evaluated to recognize the emotion of EMODB dataset.
Privacy-preserving file sharing on cloud storage with certificateless signcryption

In recent years, data generated by Internet of Things (IoT) devices has become increasingly massive. The amount of data stored in the cloud is also enormous and needs to be processed in a timely, efficient, secure, and private manner. While data stored on the cloud is guaranteed to be available, this does not mean that the secrecy and privacy of the sender are guaranteed. Moreover, sharing data on the cloud may sometimes lead to unpleasant surprises, such as undeniable disputes due to the shared content. Our certificateless signcryption (CLSC) scheme is proposed to address the above issues. It provides unforgeability, indistinguishability, non-transferability, and privacy of sender identity to ensure secrecy and privacy of the sender. Our contributions are not only limited to solving the aforementioned issues. The security model of non-transferability and privacy of sender identity for the signcryption scheme is first proposed. This is followed by a concrete simulation of the proof. In addition, our CLSC scheme does not use bilinear pairing, which is significantly lowers its computational cost and makes it suitable for IoT devices in wireless sensor networks (WSNs).
Real-time FPGA-Based Outlier Detection using Autoencoder and LSTM

The world we live in and the one we are designing for tomorrow is based on monitoring physical systems, specifically the capability of continuously gathering fine-grained information from the physical world using sensor networks. The ultimate goal of sensor networks goes beyond monitoring and data collection; they concern timely data analysis and near real-time accurate decision making. Outlier detection is one of the primary motivating data analysis applications in sensor networks. This paper proposes an FPGA-based two-stage hardware architecture for real-time outlier detection in time series data. A fine-tuned Autoencoder network is used to extract the latent features in data and a Long short-term memory (LSTM) network to predict and detect outliers in real-time. The architecture is validated using an open-source meteorological dataset collected using a multi-hop wireless sensor network (WSN). Experimental results on resource-constrained Xilinx PYNQ-Z1 board show that the proposed architecture can efficiently analyze and detect outliers in real-time. The 1.13 ms average latency and 0.21 W power consumption make it suitable for resource-constrained computing platforms.