Reviewerly/exHarmony huggingface dataset:
A New Multisignature Scheme with Public Key Aggregation for Blockchain

A multi signature scheme allows a group of signers to produce a joint signature on a common message, which is more compact than a collection of distinct signatures from all signers. Given this signature and the list of signers' public keys, a verifier is able to check if every signer in the group participated in signing. Recently, a multisignature scheme with public key aggregation has drawn a lot of attention due to their applications into the blockchain technology. Such multisignatures provide not only a compact signature, but also a compact aggregated public key, that is both the signature size and the public key size used to verify the correctness of the signature are independent from the number of signers. This is useful for a blockchain because of its duplication over a distributed network, and thus it is required to be as compact as possible. In this paper, we introduce a new multisignature scheme with such a feature. Our scheme is proven secure under the Decisional Diffie-Hellman assumption. In addition, in the presence of rogue key attacks, the security of our scheme is proven in the plain public key model.
Loyal Consumers or One-Time Deal Hunters: Repeat Buyer Prediction for E-Commerce

Merchants sometimes run big promotions (e.g., discounts or cash coupons) on particular dates (e.g., Boxing-day Sales, "Black Friday" or "Double 11 (Nov 11th)", in order to attract a large number of new buyers. Unfortunately, many of the attracted buyers are one-time deal hunters, and these promotions may have little long lasting impact on sales. To alleviate this problem, it is important for merchants to identify who can be converted into repeated buyers. By targeting on these potential loyal customers, merchants can greatly reduce the promotion cost and enhance the return on investment (ROI). It is well known that in the field of online advertising, customer targeting is extremely challenging, especially for fresh buyers. With the long-term user behavior log accumulated by Tmall.com, we get a set of merchants and their corresponding new buyers acquired during the promotion on the "Double 11" day. Our goal is to predict which new buyers for given merchants will become loyal customers in the future. In other words, we need to predict the probability that these new buyers would purchase items from the same merchants again within 6 months. A data set containing around 200k users is given for training, while the other of similar size for testing. We extracted as many features as possible and find the key features to train our models. We proposed merged model of different classification models and merged lightGBM model with different parameter sets. The experimental results show that our merged models can bring about great performance improvements comparing with the original models.
On the Classification of Kathakali Hand Gestures Using Support Vector Machines and Convolutional Neural Networks

Indian classical dance such as Kathakali is composed of complex hand gestures, body moments, facial expressions and background music. Due to the complexities involved in its hand-gesture language, it is often difficult to understand kathakali mudras. In this paper, we generated a dataset for Kathakali hand gestures and explore ways to recognize Kathakali dance mudras performed by artistes with the help of machine learning and deep learning techniques. There are 24 classes of hand gestures that are used to convey the story by the performer in Kathakali. We proposed a Support Vector Machine (SVM) model and Convolutional Neural Network (CNN) model which classify the images into 24 different classes. We compared the performance of machine learning algorithms and deep learning algorithms. Our results show that deep learning algorithms gave up to 74% accuracy. To the best of our knowledge, this is the first attempt to generate a dataset of Kathakali hand gestures, explore data pre-processing techniques for machine learning techniques and applying deep learning techniques for classification of Kathakali hand gestures.
Fuzzy cultural algorithm for solving optimization problems

Abstract Cultural algorithm demonstrates incompetence in solving problems of multi-extremal optimization. This is due to the large dimensionality of the data and causes premature convergence. To solve this problem, a fuzzy cultural algorithm is proposed.
Bayesian Active Learning for Choice Models With Deep Gaussian Processes

This paper proposes an active learning algorithm and models which can gradually learn individual's preference through pairwise comparisons. The active learning scheme aims at finding individual's most preferred choice (e.g. an airline itinerary) with minimized number of pairwise comparisons. The pairwise comparisons are encoded into probabilistic models based on assumptions of choice models and deep Gaussian processes. More specifically, this paper develops two novel probabilistic models assuming correlated Gumbel noises and latent utility functions. One is based on shallow Gaussian priors and the other assumes deep Gaussian priors. In the active learning algorithm, the next-to-compare decision is determined by an original acquisition function. The proposed algorithm and models have been benchmarked using functions with multiple local optima and one public airline itinerary dataset. In both experiments, nests are designed to capture correlated Gumbel noises. The experiments indicate the effectiveness of our active learning algorithm and models. The deep Gaussian models are proven to find the best choice with a lower number of pairwise comparisons than the shallow one. In both experiments, deep Gaussian models outperform the shallow model. The shallow model is recommended when the choice set is large and less computational time is required (e.g. in one experiment, deep Gaussian models require approximately 60%-70% more time on average).
An Investigation of the Effects of Missing Data Handling Using ‘R’-Packages

The data in real-world scenario often consists of missing values which leads to difficulty in analysis. Though there has been an emergence of various algorithms handling the issue, it is, in fact, troublesome to implement from industry perspective. Based on this issue, the 'R'-software with various available packages for missing data handling can be a fruitful solution, which is hardly reported in any previous study. Hence, the availability of such packages demands analysis to compare their performances and check their suitability for a given dataset. A comparative study is performed using the 'R'-packages, namely missForest, Multivariate imputation by chained equations (MICE), and AMELIA-II. Two classifiers, support vector machine (SVM) and logistic regression (LR), are used for prediction. The packages are compared with regard to imputation time, effects on variance, and the efficiency. The experimental results reveal that the performances depend on the dataset size and the percentage of missing values in data.
Expertise Roles and Skills Required by the Software Development Industry

In today's dynamic work environments, where software systems are mainly dominant, the identification of the professional roles and responsibilities required by the software development industry (SDI) is crucial to meeting the demand for qualified labor in the market. From this perspective, the purpose of this study is identification of expertise roles, knowledge, and skills required by the SDI by investigating software development-focused job postings. The methodology of the study is based on semantic content analysis of the job postings using Latent Dirichlet Allocation (LDA), a generative model for probabilistic topic modeling. As a result of this analysis, 30 topics demonstrating knowledge and skills required by the SDI were discovered. Besides, 20 distinct expertise roles indicating job descriptions essential for SDI were identified. The findings of this study indicate that the SDI requires a wide spectrum of expertise roles, knowledge and skills. Each of software development jobs contains specific knowledge and skills that define the scope of expertise roles in the SDI.
Effectiveness of Literacy &amp; Numeracy Drive (LND): A Students' Perspective

This paper discussed the effectiveness of English language software Literacy & Numeracy Drive (LND) used in the public sector primary schools of Punjab province, Pakistan. A mobile based application named as “Literacy & Numeracy Drive (LND)” is currently in use to teach students of Grade 03 on a tablet for learning English, Urdu and Mathematics. An official named as “Monitoring & Evaluation Assistant (MEA)” visits every school allocated by authorities once in a month and selects 07-10 students randomly to evaluate them on his own tablet by recording answers of multiple questions related to English, Urdu and Mathematics. After the evaluation, MEA uploads the results of respective school on official portal. A quantitative research was adopted to conduct this study in which 300 participants from different schools of district Sheikhupura were selected to measure the effectiveness of LND. The results reveal that LND application was not found effective due to the issues in terms of nonavailability of technology at home, language, content, assessment, usability and design. Further, students recommended game based learning consisting of interactive interface, phonics, animations, relevant content and assessment.
Optimal Steering of Stochastic Mobile Robots that Undergo Collisions with their Environment

The paper introduces a state-feedback closed-loop control approach with integrated collision exploitation. We represent the system's kinematics in the form of a drift-diffusion stochastic differential equation, and follow a stochastic switching framework to model the transition between states of free motion and in collision with the environment. We formulate an optimal steering problem and compute the control input related to state feedback. Collisions are found beneficial in terms of increasing task success probability when steering a robot from an initial to a target spatial distribution. In certain cases, collisions may help reduce the control energy for the task when compared to optimal steering with collision avoidance. We provide a mathematical basis to explain this finding, perform several parametric analyses in simulation to validate the theoretical analysis, and conduct realistic physics simulations to quantify the impact of realistic constraints (bounded control input, physical impact of collision, and friction) on control energy and success probability. Further, we validate the proposed approach experimentally with an omni-directional collision-resilient wheeled robot.
Transmission of Classical Information Over Noisy Quantum Channels–A Spectrum Approach

Based upon the Fluctuation-Dissipation Theorem of Nyquist-Callen-Welton and the spectrum balancing relationship of Kubo-Martin-Schwinger for operator-valued stochastic noise processes, a two-parameter Quantum Noise-Energy Spectral Density (QN-ESD) is constructed and Fourier-Laplace transformed to obtain its associated Quantum Noise-Autocorrelation Function (QN-ACF). By means of a Taylor series expansion, the quantum thermal noise correlations are linked to Number Theory's Reimann-Zeta function. From this perspective, a panoply of spectral components and their QN correlations are characterized for three disjoint regions located across the Electromagnetic Spectrum (EMS). The second-moment characterizations and frequency boundaries for each region are specified as a function of two frequency dependent design parameters, viz., a normalized temperature dependent frequency and a quantum receiver cutoff frequency. These boundaries define where the quadrature noise correlation projections of the QN-ESD become noticeably asymmetric-in-frequency. For the operator-valued white quantum noise region, various communication system performance metrics are characterized and their limits are presented and compared via interconnecting parameter relations to their classical communications (CC) counterparts. These metrics include Shannon's non-additive, but regularized, zero-error capacity in bits/photon-pair, bits/photon, bits/channel-use and bits/sec, the spectral efficiency in bits/sec/Hz and the error-correcting code rate. For long coded messages, Shannon's capacity quadrature approaches 2π√5/ ln(2) ≈ 20 bits/photon-pair or (10 bits/photon) while for single-photon per channel use transmission it approaches 3 bits/photon-pair. Results are graphically illustrated for the quantum noise spectral density, quantum noise correlations, Shannon's Î-Q̂ capacities and spectral efficiency.
Visualization of correlations in hybrid discrete—continuous variable quantum systems

Abstract In this work we construct Wigner functions for hybrid continuous and discrete variable quantum systems. We demonstrate new capabilities in the visualization of the interactions and correlations between discrete and continuous variable quantum systems, where visualizing the full phase space has proven difficult in the past due to the high number of degrees of freedom. Specifically, we show how to clearly distinguish signatures that arise due to quantum and classical correlations in an entangled Bell-cat state. We further show how correlations are manifested in different types of interaction, leading to a deeper understanding of how quantum information is shared between two subsystems. Understanding the nature of the correlations between systems is central to harnessing quantum effects for information processing; the methods presented here reveal the nature of these correlations, allowing a clear visualization of the quantum information present in these hybrid discrete-continuous variable quantum systems. The methods presented here could be viewed as a form of quantum state spectroscopy.
A Visual Analytics Approach for Interactive Document Clustering

Document clustering is a necessary step in various analytical and automated activities. When guided by the user, algorithms are tailored to imprint a perspective on the clustering process that reflects the user’s understanding of the dataset. More than just allow for customized adjustment of the clusters, a visual analytics approach will provide tools for the user to draw new insights on the collection. While contributing his or her perspective, the user will also acquire a deeper understanding of the data set. To that effect, we propose a novel visual analytics system for interactive document clustering. We built our system on top of clustering algorithms that can adapt to user’s feedback. In the proposed system, initial clustering is created based on the user-defined number of clusters and the selected clustering algorithm. A set of coordinated visualizations allow the examination of the dataset and the results of the clustering. The visualization provides the user with the highlights of individual documents and understanding of the evolution of documents over the time period to which they relate. The users then interact with the process by means of changing key-terms that drive the process according to their knowledge of the documents domain. In key-term-based interaction, the user assigns a set of key-terms to each target cluster to guide the clustering algorithm. We have improved that process with a novel algorithm for choosing proper seeds for the clustering. Results demonstrate that not only the system has improved considerably its precision, but also its effectiveness in the document-based decision making. A set of quantitative experiments and a user study have been conducted to show the advantages of the approach for document analytics based on clustering. We performed and reported on the use of the framework in a real decision-making scenario that relates users discussion by email to decision making in improving patient care. Results show that the framework is useful even for more complex data sets such as email conversations.
Improving firefly algorithm-based logistic regression for feature selection

One of the major problems with data classification is when it has a high dimensional, small sample size and contains irrelevant and redundant features. Binary firefly algorithm is one of the nature-inspired metaheuristic algorithms which was designed to solve the discrete optimization problem, such as feature selection. However, the binary firefly algorithm version needs a transfer function that changes search space from continuous to the discrete. In this paper, several transfer functions are investigated and explored. To validate the efficiency, and to which extent that functions impact the results, an extensive experiment was carried out on three chemometrics datasets while the logistic regression method and firefly algorithm were used to classify data and select features. The experimental results show that V2 function has consistency in feature selection and it performs high classification with better performance to the iterations.
Real-Time Multi-Target Multi-Camera Tracking with Spatial-Temporal Information

Video security monitoring has always been an important mission for safety reason. In an entire surveillance system, there are usually several cameras distributed sparsely to cover a wide range of public areas (e.g., school, shopping mall or infrastructure). Tracking person through this cameras network is challenging due to different camera perspectives, illumination changes and pose variations. Several algorithms for Multi-Target Multi-Camera tracking (MTMCT) have been proposed in offline method which has delay in getting result. Addressing the need for real-time computation of people tracks through multi camera, the paper proposes an online tracking algorithm. The contributions include (1) online real-time framework which can be used in practical application, (2) extend a single camera multi object tracking (MOT) algorithm to be suitable for multi-camera tracking and (3) use spatial-temporal information to strengthen cross camera person recall performance. The proposed algorithm has been benchmarked against the literature review of MTMC algorithms.
Tag-Based Verifiable Delegated Set Intersection Over Outsourced Private Datasets

Verifiable delegated set intersection over outsourced private datasets (VDPSI) enables two parties to outsource their private datasets and delegate the computation of set intersection to the cloud while being able to check the correctness of the result. In this process, the cloud learns nothing about the datasets and the intersection result. However, the existing VDPSI schemes suffer from three substantial shortcomings that limit their use: i) the whole dataset consists of only one subset, ii) they are designed for the static data, and iii) they cannot support other operations. To resolve these problems, we introduce a novel primitive called tag-based VDPSI (TVDPSI), which is designed for the multi-subset case where each subset is associated with one single tag for data classification. To protect privacy, the data is encrypted before being resided to the cloud. The tag is implicitly hidden in each encrypted element. As a result, the cloud cannot learn which data belongs to the same subset beyond the intersection set. Besides, the cloud cannot calculate the intersection except under the permissions of data owners. To the best of our knowledge, TVDPSI is the first VDPSI scheme supporting dynamic update and count operations. The detailed performance evaluation and simulation show that our protocol is more practical in cloud computing.
Analysis of classification and Naïve Bayes algorithm k-nearest neighbor in data mining

Abstract Naïve Bayes is a prediction method that contains a simple probabilistic that is based on the application of the Bayes theorem (Bayes rule) with the assumption that the dependence is strong. K-Nearest Neighbor (K-NN) is a group of instance-based learning, K-NN is also a lazy learning technique by searching groups of k objects in training data that are closest (similar) to objects on new data or testing data. Classification is a technique in Data mining to form a model from a predetermined data set. Data mining techniques are the choices that can be overcome in solving this problem. The results of the two different classification algorithms result in the discovery of better and more efficient algorithms for future use. It is recommended to use different datasets to analyze comparisons of naïve bayes and K-NN algorithms. the writer formulates the problem so that the research becomes more directed. The formulation of the problem in this study is to find the value of accuracy in the Naïve Bayes and KNN algorithms in classifying data.
Recent Trends and Advances in Deep Learning-Based Sentiment Analysis

Sentiment analysis is a fundamental branch of natural language processing. It is an essential task of identifying and extracting sentiment in opinionated data from sources such as social media, product feedback or blogs. Deep learning-based approaches have exceeded human-level performance in areas such as computer vision and speech recognition. Deep learning is widely accepted as the most promising in machine learning. In this chapter, we survey and analyse the current trends and advances in deep learning-based sentiment analysis approaches for document-level, sentence-level and aspect-based sentiment analysis for short and long text. A detailed discussion of deep learning architectures for sentiment analysis is provided. The studied approaches are classified into coarse-grain (including document and sentence level), fine-grain (includes target and aspect level) and cross-domain. Lastly, we provide a summary and in-depth analysis of the surveyed studies, for each of the aforementioned categories. The overwhelming number of studies explored convolutional neural networks (CNNs), long short-term memory (LSTM), gated recurrent unit (GRU) and attention mechanism. For coarse-grain sentiment analysis, LSTM and CNN-based models compete on performance, but it is CNNs that offer reduced model complexity and training overhead. Fine-grain sentiment analysis requires a model to learn complex interactions between target/aspect words and opinion words. Bi-directional LSTM and attention mechanisms offer the most promise, although CNN-based models have been adept at aspect extraction. The efforts in cross-domain sentiment analysis are dominated by LSTM and attention models. Our survey of cross-domain approaches revealed the use of multitask learning, adversarial training and joint training for domain adaptation.
Integrating Multiple Models Using Image-as-Documents Approach for Recognizing Fine-Grained Home Contexts

To implement fine-grained context recognition that is accurate and affordable for general households, we present a novel technique that integrates multiple image-based cognitive APIs and light-weight machine learning. Our key idea is to regard every image as a document by exploiting "tags" derived by multiple APIs. The aim of this paper is to compare API-based models' performance and improve the recognition accuracy by preserving the affordability for general households. We present a novel method for further improving the recognition accuracy based on multiple cognitive APIs and four modules, fork integration, majority voting, score voting, and range voting.