Reviewerly/exHarmony huggingface dataset:
Simultaneous two-way classical communication and measurement-device-independent quantum key distribution with coherent states

Simultaneous quantum and classical communication integrates both continuous variable quantum key distribution and classical coherent optical communication by using the same communication infrastructure. Given its compelling benefits, we proposed a protocol relying on both two-way classical communication and on measurement-device-independent quantum key distribution, in which the superposition-modulation-based coherent states depend on the information bits of both the secret key and on the classical communication ciphertext, which are measured by an untrusted relay node. The proposed scheme strikes a beneficial balance between its level of security and its grade of practicability. Explicitly, on the one hand, the secret key obtained is secure against all attacks on the detectors, and it is eminently suitable for bidirectional classical communication in the metropolitan network as a benefit of its relay-based configuration. Our results show a convincing bit error rate vs secret key rate trade-off for transmission over dozens of kilometers in the quantum channel, hence striking an excellent integrity (bit error rate) vs security trade-off.
Rank-Based Multi-task Learning for Fair Regression

In this work, we develop a novel fairness learning approach for multi-task regression models based on a biased training dataset, using a popular rank-based non-parametric independence test, i.e., Mann Whitney U statistic, for measuring the dependency between target variable and protected variables. To solve this learning problem efficiently, we first reformulate the problem as a new non-convex optimization problem, in which a non-convex constraint is defined based on group-wise ranking functions of individual objects. We then develop an efficient model-training algorithm based on the framework of non-convex alternating direction method of multipliers (NC-ADMM), in which one of the main challenges is to implement an efficient projection oracle to the preceding non-convex set defined based on ranking functions. Through the extensive experiments on both synthetic and real-world datasets, we validated the out-performance of our new approach against several state-of-the-art competitive methods on several popular metrics relevant to fairness learning.
Modeling and Recovery of Graph Signals and Difference-Based Signals

In this paper, we consider the problem of representing and recovering graph signals with a nonlinear measurement model. We propose a two-stage graph signal processing (GSP) framework. First, a GSP representation is obtained by finding the graph filter that best approximates the known measurement function. The new GSP representation enables performing tractable operations over graphs, as well as gaining insights into the signal graph-frequency contents. Then, we formulate the signal recovery problem under the smoothness constraint and derive a regularized least-squares (LS) estimator, which is obtained by applying the inverse of the approximated graph filter on the nonlinear measurements. In the second part of this paper, we investigate the proposed recovery and representation approach for the special case of graph signals that are influenced by the differences between vertex values only. Difference-based graph signals arise, for example, when modeling power signals as a function of the voltages in electrical networks. We show that any difference-based graph signal corresponds to a filter that lacks the zero-order filter coefficient, and thus, these signals can be recovered up to a constant by the regularized LS estimator. In our simulations, we show that for the special case of state estimation in power systems the proposed GSP approach outperforms the state-of-the-art estimator in terms of total variation.
A Fraud Detection Method for Low-Frequency Transaction

The effectiveness of transaction fraud detection methods directly affects the loss of users in online transactions.However, for low-frequency users with small transaction volume, the existing methods cannot accurately describe their transaction behaviors for each user, or lead to a high misjudgment rate.So we propose a new method for individual behavior construction, which can make the behavior of low-frequency users more accurate by migrating the current transaction group behavior and transaction status.Firstly, we consider the user's only historical transactions, combined with the optimal risk threshold determination algorithm, to form the user's own transaction behavior benchmark.Secondly, through the DBSCAN clustering algorithm, the behavior characteristics of all current normal samples and fraud samples are extracted to form the common behavior of the current transaction group.Finally, based on historical transaction records, the current transaction status is extracted using a sliding window mechanism.The combination of the three constitutes a new transaction behavior of the user.On this basis, a multi-behavior detection model based on new transaction behavior is proposed.According to the result of each behavior, Naive Bayes model is used to calculate the probability that current transaction belongs to fraud, and finally determine whether current transaction is fraud.Experiments prove that the method proposed in this paper can have a good effect on low-frequency users, which can accurately identify fraud transactions and has a low misjudgment rate for normal transactions.
On the Design of a Blockchain-as-a-Service-Based Health Information Exchange (BaaS-HIE) System for Patient Monitoring

The digitization of health records has massively increased Health Information Exchange (HIE) activities among different practitioners, but it has lagged behind Electronic Health/Medical Records (EHRs/EMRs) adoption for numerous reasons, including confidentiality, interoperability, integrity, and privacy-related concerns. In this paper, we present a Blockchainas-a-Service based solution for HIE (BaaS-HIE). In particular, our design work involves the use of a private Blockchain and smart contracts as access control manager to medical records. In order to maintain high level of performance for applications, thus that such applications could be economically viable, all of the health data are encrypted and stored into a decentralised InterPlanetary File System (IPFS) and the hash of the assets URI is stored in blockchain. Our experimental results demonstrate the feasibility of the proposed approach in offering a decentralized and fine-grained accessibility mechanism for the patient and the doctor in a given healthcare system.
Democratising systems of innovations based on Blockchain platform technologies

Purpose Blockchain is expected to have a significant impact on Systems of Innovation as the new General Purpose Technology. The purpose of this study is to investigate how Blockchain can revolutionise the Systems of Innovation by investigating its overall structure, actors and relationships. Design/methodology/approach This study used the systematic mapping method to explore and integrate the Blockchain and Systems of Innovation literature for the creation of a new conceptual model of Blockchain-enabled Systems of Innovation. In that scope, 37 Blockchain-related and 32 Systems of Innovation-related papers, besides two major books in the field of Blockchain, have been reviewed and then integrated based on the Systems Thinking approach. Findings The key findings for Blockchain-enabled Systems of Innovation are that there is (1) an increased distribution of networks and collaborations, (2) increased trust through the use of reputation systems, (3) an emerging new nature of platform characteristics, (4) a democratisation of entrepreneurship by the new funding landscape and (5) an increased significance of technological drivers, such as energy. Research limitations/implications The study shows new Systems of Innovation-related research implications. Accordingly, a new type of actor, relationship and attribute has been introduced where the boundaries of the role definitions are blurred and more distributed. This is where larger organisations can expect to lose their central position. The different types of actors are replaced by a network of actors as a result of the distributed new Blockchain-based system. The threshold for the Bottom of the Pyramid is expected to be reduced, leading to a more democratised innovation system. Practical Implications Blockchain appears to reduce the effects of distrust in collaborative innovation practices with its consensus mechanisms and the new Blockchain-enabled Systems of Innovation is expected to revolutionise the interactions in the future. Originality/value There are very few studies that have been found to integrate innovation management practices with Blockchain. This is the first Blockchain-based Systems of Innovation study enabling the fundamental revision of its structure, types of relationships and actors.
Learning to Sample: An Active Learning Framework

Meta-learning algorithms for active learning are emerging as a promising paradigm for learning the "best" active learning strategy. However, current learning-based active learning approaches still require sufficient training data so as to generalize meta-learning models for active learning. This is contrary to the nature of active learning which typically starts with a small number of labeled samples. The unavailability of large amounts of labeled samples for training meta-learning models would inevitably lead to poor performance (e.g., instabilities and overfitting). In our paper, we tackle these issues by proposing a novel learning-based active learning framework, called Learning To Sample (LTS). This framework has two key components: a sampling model and a boosting model, which can mutually learn from each other in iterations to improve the performance of each other. Within this framework, the sampling model incorporates uncertainty sampling and diversity sampling into a unified process for optimization, enabling us to actively select the most representative and informative samples based on an optimized integration of uncertainty and diversity. To evaluate the effectiveness of the LTS framework, we have conducted extensive experiments on three different classification tasks: image classification, salary level prediction, and entity resolution. The experimental results show that our LTS framework significantly outperforms all the baselines when the label budget is limited, especially for datasets with highly imbalanced classes. In addition to this, our LTS framework can effectively tackle the cold start problem occurring in many existing active learning approaches.
Eye Gaze Accuracy in the Projection-based Stereoscopic Display as a Function of Number of Fixation, Eye Movement Time, and Parallax

Eye gaze accuracy is one of the most commonly used parameter to evaluate the eye tracker performance. The current study applied stepwise multiple regression to predict the significant predictors for eye gaze accuracy (AC). 7 male and 3 female were recruited to perform multi-directional tapping task in the projection-based stereoscopic display under 3 different levels of parallax and 6 different levels of index of difficulty (ID). Tobii X2 eye tracker was used to measure the selected four eye movement parameters which consist number of fixation (NF), fixation duration (FD), time to first fixation (TFF), and eye movement time (EMT). The results indicated that NF was found to be the best predictor for AC followed by EMT and parallax. The R2 value of 0.247 indicating that the 24.7% of the variability of the data was explained by the model. Practitioner Summary: The result of multiple regression can be a valuable theoretical foundation for evaluating an eye tracker. The results could be very beneficial for human factors engineers and virtual reality developers especially for predicting eye gaze accuracy.
Movie Recommendation System Using Semi-Supervised Learning

In the current digital era, with the huge amount of technologies and tremendous amount of information being available at disposal over the Internet, a huge amount of data is made available to users. This results in a condition known as "information overload". Due to these, it is difficult for a person to search and access information for taking decisions to arrive at an effective conclusion. To perorate this nut, there are filtering systems for information, known as the recommendation system or recommendation engine, considered here in paper, that help a person in identifying significant and possible services or products of interest based on the preferences given by him/her. This results in searching through lots of results to find the one that the user actually needs. This can be in cases like searching for books, music, videos, job postings, etc. A recommendation system is hence needed to help recommend items to users which are more relevant and accurate and fulfils the user's needs and requirements. A movie recommendation system is used to recommend movies which match the user taste's and preferences. Several approaches exist to implement this system - popularity-based recommendation system, simple recommendation system, content-based filtering, collaborative-based filtering, metadata-based filtering, demographic-based filtering approach. In this paper we use the following approaches - simple recommendation system, content-based filtering approach, collaborative-based filtering approach.
Next Word Prediction in Hindi Using Deep Learning Techniques

Natural Language Generation (NLG) focuses on the generation of natural, human-interpretable language. This study proposes a novel methodology to predict the next word in a Hindi sentence. By predicting the next word in a sequence, the number of keystrokes of the user can be reduced. Two deep learning techniques namely Long Short Term Memory (LSTM) and Bi-LSTM have been explored for the task of predicting next word and accuracy of 59.46% and 81.07% was observed for LSTM and Bi-LSTM respectively. This approach may be used for various NLG tasks like story auto-completion, sentence autocompletion, etc.
A framework for risk management in Scrum development process

Since the beginning of 2000, the software industry has turned a corner to adopting agile lightweight methods that are subject to change at a more efficient pace; in addition, risk management is generally not explicitly addressed in agile projects. So agile teams generally do not use an intentional risk management approach. Although agile methodologies tend to manage project risk implicitly, it is important to have an appropriate risk management mechanism in agile methodologies.Therefore, in this paper a framework for integrating risk management in the agile development projects is proposed while considering Scrum as an agile method and the PMBOK as a project risk management guide. The main goals of this framework are to improve the risk management mechanism in Scrum and to increase the Scrum project’s success rate
A novel approach of image encryption using pixel shuffling and 3D chaotic map

Abstract – In recent years, with rapid development in information and communication technologies, sharing digital images in social media have facilitated. However, since images privacy matters in our society, keeping images protected became a big challenge we have to deal with. In this paper, we propose a new image encryption method using Image Pixel Shuffling and 3D Chaotic map. First, the plain image is encoded using pixel shuffling, then the result is XORed with a key, and finally, the 3D chaotic map is performed on it. The security will be increased by using three steps to encrypt the image. Our results show that the proposed approach has a good performance and it is better than those of some notable image encryption algorithms. We got entropy value 7.9901 of cipher image, NPCR of 99.6628%, and UACI of 33.6781%. MATLAB was used to implement the proposed image encryption approach.
LimbMotion

Wearable-based human-computer interaction is a promising technology to enable various applications. This paper aims to track the 3D posture of the entire limb, both wrist/ankle and elbow/knee, of a user wearing a smart device. This limb tracking technology can trace the geometric motion of the limb, without introducing any training stage usually required in gesture recognition approaches. Nonetheless, the tracked limb motion can also be used as a generic input for gesture-based applications. The 3D posture of a limb is defined by the relative positions among main joints, e.g., shoulder, elbow, and wrist for an arm. When a smartwatch is worn on the wrist of a user, its position is affected by both elbow and shoulder motions. It is challenging to infer the entire 3D posture when only given a single point of sensor data from the smartwatch. In this paper, we propose LimbMotion, an accurate and real-time limb tracking system. The performance gain of LimbMotion comes from multiple key technologies, including an accurate attitude estimator based on a novel two-step filter, fast acoustic ranging, and point clouds-based positioning. We implemented LimbMotion and evaluated its performance using extensive experiments, including different gestures, moving speeds, users, and limbs. Results show that LimbMotion achieves real-time tracking with a median error of 7.5cm to 8.9cm, which outperforms the state-of-the-art approach by about 32%.
DEDUCE: Diverse scEne Detection methods in Unseen Challenging Environments

In recent years, there has been a rapid increase in the number of service robots deployed for aiding people in their daily activities. Unfortunately, most of these robots require human input for training in order to do tasks in indoor environments. Successful domestic navigation often requires access to semantic information about the environment, which can be learned without human guidance. In this paper, we propose a set of DEDUCE <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> - Diverse scEne Detection methods in Unseen Challenging Environments algorithms which incorporate deep fusion models derived from scene recognition systems and object detectors. The five methods described here have been evaluated on several popular recent image datasets, as well as real-world videos acquired through multiple mobile platforms. The final results show an improvement over the existing state-of-the-art visual place recognition systems.
An Internet-of-Things Solution to Assist Independent Living and Social Connectedness in Elderly

Social isolation has been identified as a major risk in elderly people living alone because of their association with cognitive decline, depression, and other mental health-related issues. Ambient Assisted Living (AAL) is identified as a key technology to facilitate independent living and maintain social connnectedness between elderly, their families, and caregivers. AAL combines Internet of Things, Smart Homes, and machine learning to produce a smart solution that encourages independent, safe, and socially active life for elderly people within their own home. In this article, we propose, develop, implement, and validate a novel Internet-of-Things-based solution that uses passive (i.e., non-obstructive methods) sensing for real-time monitoring of elderly in their homes. The significance of the proposed solution is in the use of machine learning and statistical models to automatically build a personalised model by learning the normal behavioural pattern for the person from deployed sensors in the house. It then uses this model to detect significant changes in the behavioural pattern, should they occur, that could be a consequence of possible health deterioration. We evaluate the performance of the proposed solution via real-world in-home trials installed in six elderly people’s home for a period from 1.5 to 4 months. A discussion and analysis of the in-home trial outcomes and feedback from elderly who participated in the trials conclude the article.
From Joint Feature Selection and Self-Representation Learning to Robust Multi-view Subspace Clustering

In era of big data, we have easier access to the data with multi-view representations from heterogeneous feature spaces, where each view is often unlabeled, partial and even full of noises. These unique challenges and properties motivate us to develop a novel robust multi-view subspace clustering framework (RMSC), which learns a consensus affinity matrix with the ideal subspace structure, by extending our joint feature selection and self-representation model (JFSSR). Concretely, RMSC learns the consensus graph across diverse views with exactly k connected components (k is the number of clusters), which is encoded by a block diagonal self-representation matrix. Besides, we emphasize l2;1-norm minimization on the loss function to reduce redundant and irrelevant features, and implicitly assign an adaptive weight to each view without introducing additional parameters. Lastly, an alternating optimization algorithm is derived to solve the nonconvex formulated objective. Extensive empirical results on both synthetic data and real-world benchmark data sets show that RMSC consistently outperforms several representative multiview clustering approaches.
Lossless digital image watermarking in sparse domain by using K‐singular value decomposition algorithm

The crucial hurdle faced by the watermarking technique is to maintain the steadiness corresponding to several attacks while assisting a sufficient level of security. In this study, a robust lossless sparse domain-based watermarking approach combined with discrete cosine transform (DCT) is introduced to hide the secret message in the selected significant sparse elements of the host image. The proposed method takes advantage of a sparse representation-based dictionary learning process. To enhance the security of the original image, the authors first apply the DCT on a secret message. These DCT coefficients with some regularised parameters will be inserted into the selected significant sparse coefficients. At the extraction stage, the secret message is extracted from those significant sparse coefficients by employing the sparse domain orthogonal matching pursuit algorithm. Finally, the inverse DCT is applied to extract the secret message without any information loss. To show the effectiveness of the proposed method, different commonly used attacks are simulated. Simulation results in terms of peak signal-to-noise ratio, structural similarity, normal correlation, and feature similarity indicate that the proposed method can recover the hidden secret message accurately against seven different types of attacks including speckle, Gaussian, salt and pepper, rotate, crop, fold, and blur attack.
An Attention-Based End-to-End Model for Multiple Text Lines Recognition in Japanese Historical Documents

This paper presents an attention-based convolutional sequence to sequence (ACseq2seq) model for recognizing an input image of multiple text lines from Japanese historical documents without explicit segmentation of lines. The recognition system has three main parts: a feature extractor using Convolutional Neural Network (CNN) to extract a feature sequence from an input image; an encoder employing bidirectional Long Short-Term Memory (BLSTM) to encode the feature sequence; and a decoder using a unidirectional LSTM with the attention mechanism to generate the final target text based on the attended pertinent features. We also introduce a residual LSTM network between the attention vector and softmax layer in the decoder. The system can be trained end-to-end by a standard cross-entropy loss function. In the experiment, we evaluate the performance of the ACseq2seq model on the anomalously deformed Kana datasets in the PRMU contest. The results of the experiments show that our proposed model achieves higher recognition accuracy than the state-of-the-art recognition methods on the anomalously deformed Kana datasets.