Reviewerly/exHarmony huggingface dataset:
Characterization of exact one-query quantum algorithms

The quantum query model is one of the most important models in quantum computing. Several well-known quantum algorithms are captured by this model, including the Deutsch-Jozsa algorithm, the Simon algorithm, the Grover algorithm, and others. In this paper, we characterize the computational power of exact one-query quantum algorithms. It is proved that a total Boolean function $f:{{0,1}}^{n}\ensuremath{\rightarrow}{0,1}$ can be exactly computed by a one-query quantum algorithm if and only if $f(x)={x}_{{i}_{1}}$ or ${x}_{{i}_{1}}\ensuremath{\bigoplus}{x}_{{i}_{2}}$ (up to isomorphism). Note that, unlike most work in the literature based on the polynomial method, our proof does not resort to any knowledge about the polynomial degree of $f$.
A Machine Learning Approach for URL Based Web Phishing Using Fuzzy Logic as Classifier

Phishing is the major problem of the internet era. In this era of internet the security of our data in web is gaining an increasing importance. Phishing is one of the most harmful ways to unknowingly access the credential information like username, password or account number from the users. Users are not aware of this type of attack and later they will also become a part of the phishing attacks. It may be the losses of financial found, personal information, reputation of brand name or trust of brand. So the detection of phishing site is necessary. In this paper we design a framework of phishing detection using URL.
On the inferential implications of decreasing weight structures in mixture models

Bayesian estimation of nonparametric mixture models strongly relies on available representations of discrete random probability measures. In particular, the order of the mixing weights plays an important role for the identifiability of component-specific parameters which, in turn, affects the convergence properties of posterior samplers. The geometric process mixture model provides a simple alternative to models based on the Dirichlet process that effectively addresses these issues. However, the rate of decay of the mixing weights for this model may be too fast for modeling data with a large number of components. The need for different decay rates arises. Some variants of the geometric process featuring different decay behaviors, while preserving the decreasing structure, are presented and investigated. An asymptotic characterization of the number of distinct values in a sample from the corresponding mixing measure is also given, highlighting the inferential implications of different prior specifications. The analysis is completed by a simulation study in the context of density estimation. It shows that by controlling the decaying rate, the mixture model is able to capture data with a large number of components.
Quantum catalysis-based discrete modulation continuous variable quantum key distribution with eight states

How to lengthen the maximum transmission of continuous variable quantum key distribution (CVQKD) has been a notorious hard problem in quantum communications. Here, we propose a simple solution to this problem, i.e., quantum catalyzing CVQKD for discrete modulation with eight states. The quantum catalysis, which can facilitate the conversion of the target ensemble, is used for not only tolerating more excess noise but also lengthening the maximum transmission distance. Security analysis shows that the zero-photon catalysis (ZPC), which is actually seen as a noiseless attenuation can be used as an elegant candidate for the performance improvement of discrete modulation (DM)-CVQKD. The numerical simulations show the ZPC-involved DM-CVQKD protocol outperforms the original DM-CVQKD in terms of maximum transmission distance as well as tolerable noise. Moreover, the ZPC-involved DM-CVQKD protocol can tolerate lower reconciliation efficiency and allow the lower detection efficiency to achieve the same performance.
DevsNet: Deep Video Saliency Network using Short-term and Long-term Cues

Recently, there have been various saliency detection methods proposed for still images based on deep learning techniques. However, the research on saliency detection for video sequences is still limited. In this study, we introduce a novel deep learning framework of saliency detection for video sequences, namely Deep Video Saliency Network (DevsNet). DevsNet mainly consists of two components: 3D Convolutional Network (3D-ConvNet) and Bidirectional Convolutional Long-Short Term Memory Network (B-ConvLSTM). 3D-ConvNet is constructed to learn short-term spatiotemporal information and the long-term spatiotemporal features are learned by B-ConvLSTM. The designed B-ConvLSTM can extract the temporal information not just from the previous video frames but also from the next frames, which demonstrates that the proposed model considers both the forward and backward temporal information. By combining the short-term and long-term spatiotemporal cues, the proposed DevsNet can extract saliency information for video sequences effectively and efficiently. Extensive experiments have been conducted to show that the proposed model can obtain better performance in spatiotemporal saliency prediction than the state-of-the-art models.
Optical Flow Estimation Based on the Frequency-Domain Regularization

Accurate optical flow estimation with the frequency-domain regularization is a challenging problem in computer vision. In this paper, we solve this issue by introducing a novel optical flow method related to the frequency domain that uses TV-wavelet regularization. Specifically, we regard TV-wavelet regularization as a filtering process. After wavelet transform for optical flow field, we firstly remove outliers by performing a threshold operation. Then, we make up for lost motion information (such as flow edges and important motion details) determined by these missing or damaged wavelet coefficients by adding TV-wavelet coefficients that are obtained from transform spectrum of the prior flow geometrical features, which are controlled by the image structures. By combining the advantages of total variation to recover geometric structures with the strengths of wavelet representation to remove outliers, the proposed method significantly outperforms the current frequency-domain optical flow methods in removing outliers, preserving sharp flow edges, and restoring important motion details. It also shows competitive optical flow evaluation results on the challenging MPI-Sintel, Kitti, and Middlebury datasets.
Weakly Supervised Deep Learning Approach in Streaming Environments

The feasibility of existing data stream algorithms is often hindered by the weakly supervised condition of data streams. A self-evolving deep neural network, namely Parsimonious Network (ParsNet), is proposed as a solution to various weakly-supervised data stream problems. A self-labelling strategy with hedge (SLASH) is proposed in which its auto-correction mechanism copes with the accumulation of mistakes significantly affecting the model's generalization. ParsNet is developed from a closed-loop configuration of the self-evolving generative and discriminative training processes exploiting shared parameters in which its structure flexibly grows and shrinks to overcome the issue of concept drift with/without labels. The numerical evaluation has been performed under two challenging problems, namely sporadic access to ground truth and infinitely delayed access to the ground truth. Our numerical study shows the advantage of ParsNet with a substantial margin from its counterparts in the high-dimensional data streams and infinite delay simulation protocol. To support the reproducible research initiative, the source code of ParsNet along with supplementary materials are made available at https://bit.ly/2qNW7p4.
A self controlled RDP approach for feature extraction in online handwriting recognition using deep learning

The identification of accurate features is the initial task for benchmarked handwriting recognition. For handwriting recognition, the objective of feature computation is to find those characteristics of a handwritten stroke that depict the class of a stroke and make it separable from the rest of the stroke classes. The present study proposes a feature extraction technique for online handwritten strokes based on a self controlled Ramer-Douglas-Peucker (RDP) algorithm. This novel approach prepares a smaller length feature vector for different shaped online handwritten strokes without preprocessing and without any control parameter to RDP. Thus, it also overcomes the shortcomings of the traditional chain code based feature extraction approach that requires preprocessing of data, and the original RDP algorithm that requires a control parameter as an input to RDP. We further propose a deep learning network of 1-dimensional convolutional neural networks (Conv1Ds) for recognition, which trains in few minutes due to the smaller dimension of the convolution combined with smaller length feature vectors. The proposed approach can be applied to different scripts and different writing styles. The key aim of the present study is to provide a script independent feature extraction technique that is well suited for smaller devices. It improves the recognition over the best reported accuracy in the literature which was achieved using hidden Markov models with directional features, from 87.67% to 95.61% on a Gurmukhi dataset. For Unipen online handwriting datasets the results are at par with the literature.
Practical Locally Private Heavy Hitters

We present new practical local differentially private heavy hitters algorithms achieving optimal or near-optimal worst-case error and running time -- TreeHist and Bitstogram. In both algorithms, server running time is $\tilde O(n)$ and user running time is $\tilde O(1)$, hence improving on the prior state-of-the-art result of Bassily and Smith [STOC 2015] requiring $O(n^{5/2})$ server time and $O(n^{3/2})$ user time. With a typically large number of participants in local algorithms ($n$ in the millions), this reduction in time complexity, in particular at the user side, is crucial for making locally private heavy hitters algorithms usable in practice. We implemented Algorithm TreeHist to verify our theoretical analysis and compared its performance with the performance of Google's RAPPOR code.
FlexAdapt: Flexible Cycle-Consistent Adversarial Domain Adaptation

Unsupervised domain adaptation is emerging as a powerful technique to improve the generalizability of deep learning models to new image domains without using any labeled data in the target domain. In the literature, solutions which perform cross-domain feature-matching (e.g., ADDA), pixel-matching (CycleGAN), and combination of the two (e.g., CyCADA) have been proposed for unsupervised domain adaptation. Many of these approaches make a strong assumption that the source and target label spaces are the same, however in the real-world, this assumption does not hold true. In this paper, we propose a novel solution, FlexAdapt, which extends the state-of-the-art unsupervised domain adaptation approach of CyCADA to scenarios where the label spaces in source and target domains are only partially overlapped. Our solution beats a number of state-of-the-art baseline approaches by as much as 29% in some scenarios, and represent a way forward for applying domain adaptation techniques in the real world.
Estimating Software Effort Using Neural Network: An Experimental Investigation

Software companies develop various softwares at the same time. It is very critical task that is to be managed by project managers. Completion of a project is purely dependent on various parameters such as time, cost and staff. By these parameters, project planning will be done by the project managers. Software effort estimation is a fundamental and emerging aspect for software companies in developing a software. If we estimate the effort properly, it will control the project cost as well as time. This paper is presented by comparing various models such as KNN, SVM, NN, RF and back propagation algorithm using feed forward neural network by using Orange data mining tool. The proposed models are evaluated using COCOMO’81 dataset having 63 projects and Desharnais dataset having 81 projects. Estimation results are evident that the back propagation-based approach is a suitable model as compared to the remaining considered approach.
Multiscale summarization and action ranking in egocentric videos

Useful information extraction from egocentric videos has evolved as an important research problem for both computer vision and multimedia communities. In this paper, we have addressed two problems, namely (i) generating multiscale summaries, i.e., multiple summaries of different lengths and (ii) priority-based ranking of various actions present in egocentric videos. A new algorithm, termed as Multiscale Egocentric Video Summarization and Action Ranking (MEVSAR), with agglomerative clustering as its backbone, is proposed to solve the above problems. Importantly, the MEVSAR algorithm follows an “analyze once, generate many” principle to generate multiple summaries in a single run and subsequently rank actions from the generated summaries. Experimental evaluation on two well-known publicly available datasets clearly demonstrate the merits of the proposed approach.
A concise peephole model based transfer learning method for small sample temporal feature-based data-driven quality analysis

Insufficient samples and low analysis efficiency are two main problems for data-driven quality analysis. To avoid negative transfer from source data to target data and improve transfer efficiency for temporal feature extraction, in this paper, a novel transfer learning model and algorithm with feature mapping, feature learning and domain adaptation was proposed based on concise peephole model (TLCPM). A feature mapping model based on deep convolution network was firstly presented by establishing deep convolution network to automatically learn and minimize the feature distance of source data and target data. The feature learning method was then constructed by concise peephole model to extract feature of temporal sample, which has less training parameters and more concise network structure than traditional peephole model. And domain adaptation combining one-layer fully connected network with TLCPM is used to realize the transfer learning ability of CPM model and minimize the probability output distribution distance. Finally, a bolt tightening test bench was set up and a tiny bolt-tightening data set was acquired to validate the proposed method. The results showed the TLCPM can be properly applied to analyze small sample temporal feature-based data and achieved good comprehensive performance. It took 0.5 min to obtain high test accuracy (95.14%), which was better than the results of other state of the art algorithms. Moreover, additional validation experiments were carried on. The results revealed that TLCPM also had better prediction accuracy on learning different categories of small samples.
Modelling and Executing Time-Aware Processes in Trustless Blockchain Environment

Blockchain is an emergent technology which enables the execution of collaborative business processes while ensuring trust by replacing central authority with cryptographic proof and distributed consensus. Thus, Blockchain technology can be used to find agreement between untrusted collaborating parties in business processes. However, temporal constraints of processes need more investigation. Indeed, Blockchain platforms do not offer means to represent nor to manage temporal constraints for business processes. Moreover, transaction completion time is not fixed, it can take from a few seconds to several minutes. In this paper, we include temporal constraints in smart contracts, which could mitigate the violation of time constraints, minimize the costly execution time, and avoiding thus financial penalties. To achieve this, we extend Caterpillar tool, which is the first open-source blockchain-based BPMN execution engine, to enable the automatic transformation of a large set of temporal constraints for business process model to smart contract code. We illustrate our approach with a use case, which we deploy in Ethereum Testnet.
Motion-To-BMI: Using Motion Sensors to Predict the Body Mass Index of Smartphone Users

Obesity has become a widespread health problem worldwide. The body mass index (BMI) is a simple and reliable index based on weight and height that is commonly used to identify and classify adults as underweight, normal, overweight (pre-obesity), or obese. In this paper, we propose a hybrid deep neural network for predicting the BMI of smartphone users, based only on the characteristics of body movement captured by the smartphone's built-in motion sensors without any other sensitive data. The proposed deep learning model consists of four major modules: a transformation module for data preprocessing, a convolution module for extracting spatial features, a long short-term memory (LSTM) module for exploring temporal dependency, and a fully connected module for regression. We define motion entropy (MEn), which is a measure of the regularity and complexity of the motion sensor, and propose a novel MEn-based filtering strategy to select parts of sensor data that met certain thresholds for training the model. We evaluate this model using two public datasets in comparison with baseline conventional feature-based methods using leave-one-subject-out (LOSO) cross-validation. Experimental results show that the proposed model with the MEn-based filtering strategy outperforms the baseline approaches significantly. The results also show that jogging may be a more suitable activity of daily living (ADL) for BMI prediction than walking and walking upstairs. We believe that the conclusions of this study will help to develop a long-term remote health monitoring system.
Knowledge Distillation from Bert in Pre-Training and Fine-Tuning for Polyphone Disambiguation

Polyphone disambiguation aims to select the correct pronunciation for a polyphonic word from several candidates, which is important for text-to-speech synthesis. Since the pronunciation of a polyphonic word is usually decided by its context, polyphone disambiguation can be regarded as a language understanding task. Inspired by the success of BERT for language understanding, we propose to leverage pre-trained BERT models for polyphone disambiguation. However, BERT models are usually too heavy to be served online, in terms of both memory cost and inference speed. In this work, we focus on efficient model for polyphone disambiguation and propose a two-stage knowledge distillation method that transfers the knowledge from a heavy BERT model in both pre-training and fine-tuning stages to a lightweight BERT model, in order to reduce online serving cost. Experiments on Chinese and English polyphone disambiguation datasets demonstrate that our method reduces model parameters by a factor of 5 and improves inference speed by 7 times, while nearly matches the classification accuracy (95.4% on Chinese and 98.1% on English) to the original BERT model.
Internet of Things and Blockchain: Integration, Need, Challenges, Applications, and Future Scope

With the increase in technological advancements in this era, smart devices and intelligent systems that are smart enough to efficiently link, communicate, and complete the required task are needed. Recent technologies such as Internet of Things (IoT), blockchain, and machine learning have led to far more development. IoT has emerged notably in recent times as it upholds endless numbers of applications from smart devices to smart buildings and smart cities covering it all and not only this but also in a variety of domains, helping in communication among the electronic devices that will use sensors for interacting with the environment. The pattern of IoT is flooring the way for the world where maximum number of devices that are interconnected and they collect information from the environment and then automate certain tasks. While dealing with smart devices, some factors such as data privacy, data security, authentication, strength against attacks, valid authorization, self-maintenance, and easy operation are needed to be considered. Features like these can be easily brought into the picture using blockchain technology which was born with the crypto currency called Bitcoin, created by an anonymous group named Satoshi Nakamoto. This chapter will cover the basics of IoT and blockchain and how to adopt blockchain specifically for IoT so that one can have a well-developed blockchain-based IoT (BIoT) system that can be effectively and efficiently be used, such devices are named as BIoT devices and they come under BIoT applications. One of the continuous encounters faced by us in IoT’s triumph is about how to protect and privatize the transactions on the devices each time. It will be rigid to refer a technology that will benefit and will have impactful effect on human life than the IoT and also there is no better technology than blockchain for authenticated and authorized transactions that forms a distributed ledger. This chapter will evaluate how blockchain helps in strengthening the security in the IoT. After unfolding the basics of blockchain, it explains the most pertinent BIoT applications focuses on the influence of customary cloud centering IoT uses.
CATA++: A Collaborative Dual Attentive Autoencoder Method for Recommending Scientific Articles

Recommender systems today have become an essential component of any commercial website. Collaborative filtering approaches, and Matrix Factorization (MF) techniques in particular, are widely used in recommender systems. However, the natural data sparsity problem limits their performance where users generally interact with very few items in the system. Consequently, multiple hybrid models were proposed recently to optimize MF performance by incorporating additional contextual information in its learning process. Although these models improve the recommendation quality, there are two primary aspects for further improvements: (1) multiple models focus only on some portion of the available contextual information and neglect other portions; (2) learning the feature space of the side contextual information needs to be further enhanced. In this paper, we introduce a Collaborative Dual Attentive Autoencoder (CATA++) for recommending scientific articles. CATA++ utilizes an article's content and learns its latent space via two parallel autoencoders. We employ the attention mechanism to capture the most related parts of information in order to make more relevant recommendations. Extensive experiments on three real-world datasets have shown that our dual-way learning strategy has significantly improved the MF performance in comparison with other state-of-the-art MF-based models using various experimental evaluations. The source code of our methods is available at: https://github.com/jianlin-cheng/CATA.
DIGICERT: A Secured Digital Certificate Application using Blockchain through Smart Contracts

Deception of Certificates is the major issue that is faced by the educational sector. In order to prevent forgery of certificates and to provide a secure certificate verification environment an authenticated system application is developed using blockchain with smart contracts. The immutability property of blockchain prevents the data stored from being manipulated and helps in implementing a verification system. The system generates a new block for every user in the blockchain. These blockchain are replicated across all the nodes involved in a Peer-to-Peer network and thus it improves the reliability of the system. Whenever a user is validated, their certificates are digitally provided by the system to the user along with a Quick Response code. This helps in sharing of certificates between the employers. The authenticity of certificates is improved by embedding the certificates with imperceptible watermark using the alpha blending process. This helps to avoid various attacks at different phases of certificate distribution.