Reviewerly/exHarmony huggingface dataset:
Learning Sign-Constrained Support Vector Machines

Domain knowledge is useful to improve the generalization performance of learning machines. Sign constraints are a handy representation to combine domain knowledge with learning machine. In this paper, we consider constraining the signs of the weight coefficients in learning the linear support vector machine, and develop two optimization algorithms for minimizing the empirical risk under the sign constraints. One of the two algorithms is based on the projected gradient method, in which each iteration of the projected gradient method takes O(nd) computational cost and the sublinear convergence of the objective error is guaranteed. The second algorithm is based on the Frank-Wolfe method that also converges sublinearly and possesses a clear termination criterion. We show that each iteration of the Frank-Wolfe also requires O(nd) cost. Furthermore, we derive the explicit expression for the minimal iteration number to ensure an ε-accurate solution by analyzing the curvature of the objective function. Finally, we empirically demonstrate that the sign constraints are a promising technique when similarities to the training examples compose the feature vector.
Distributed Outsourced Privacy-Preserving Gradient Descent Methods among Multiple Parties

The Internet of Things (IoT) is one of the latest internet evolutions. Cloud computing is an important technique which realizes the computational demand of largely distributed IoT devices/sensors by employing various machine learning models. Gradient descent methods are widely employed to find the optimal coefficients of a machine learning model in the cloud computing. Commonly, the data are distributed among multiple data owners, whereas the target function is held by the model owner. The model owner can train its model over data owner’s data and provide predictions. However, the dataset or the target function’s confidentiality may not be kept in secret during computations. Thus, security threats and privacy risks arise. To address the data and model’s privacy mentioned above, we present two new outsourced privacy-preserving gradient descent (OPPGD) method schemes over horizontally or vertically partitioned data among multiple parties, respectively. Compared to previously proposed solutions, our methods improve in comprehensiveness in a more general scene. The data privacy and the model privacy are preserved during the whole learning and prediction procedures. In addition, the execution performance evaluation demonstrates that our schemes can help the model owner to optimize its target function and provide exact prediction with high efficiency and accuracy.
Sector classification for crowd-based software requirements

Requirements engineering (RE) is the process of defining, documenting, and maintaining software requirements. Crowd-based RE (CrowdRE) involves large scale user participation in requirements engineering tasks. It improves the quality of software requirements and helps in reducing the cost. Manual extraction of useful insights from a large body of unstructured, and noisy natural language data produced during CrowdRE is an expensive, error prone and time consuming task. Thus, automated techniques are required for processing the CrowdRE data. We focus on the problem of automatic classification of crowd-based software requirements into sectors. We propose three different approaches for sector classification of crowd-based software requirements. These approaches are based on supervised machine learning (ML) models, neural networks, and bidirectional encoder representations from transformers (BERT), respectively. We apply our classification approaches to a large-sized requirements document, i.e. a CrowdRE dataset with around 3000 crowd-generated requirements for smart home applications. To evaluate the quality of our classification algorithms we use the publicly available ground truth data for computing precision, recall, and F-score. We compare the performance of several classification algorithms and our detailed experiments indicate that these algorithms can be very useful for categorizing crowd-based requirements into sectors.
Comparative Study of SMOTE and Bootstrapping Performance Based on Predication Methods

Recently, there has been a renewed interest in smart health systems that aim to deliver high quality healthcare services. Prediction methods are very essential to support these systems. They mainly rely on datasets with assumptions that match the reality. However, one of the greatest challenges to prediction methods is to have datasets which are normally distributed. This paper presents an experimental work to implement SMOTE (Synthetic Minority Oversampling Technique) and bootstrapping methods to normalize datasets. It also measured the impact of both methods in the performance of different prediction methods such as Support vector machine (SVM), Naive Bayes, and neural network(NN) The results showed that bootstrapping with native bays yielded better prediction performance as compared to other prediction methods with SMOTE.
Distributed Energy Trading Network: Decentralized and Secured

This paper tries to build a system, providing maximum participation and maximum resiliency for the existing power system using existing technologies only but in an innovative approach. The proposed ecosystem consists of distributed power generation nodes, consumers, smart grids, and microgrids to make peer-to-peer energy transactions easy, both physically and economically. The proposal can be divided into two parts, viz. physical and digital. A user cannot send power to another customer in need with few exceptions in a traditional system. This work has shown how each user can be connected to the smart grid via one or many micro-grids. Moreover, their trade will be automated on the digital side by Ethereum, a public blockchain that supports smart contracts. To make things more efficient and easy, we introduced a token-based economy, empowered by a smart contract. In this way, the resiliency of the whole ecosystem will be improved dramatically.
Complexity Construction of Intelligent Marketing Strategy Based on Mobile Computing and Machine Learning Simulation Environment

Mankind’s research on marketing has a history of hundreds of years, and it has been fruitful in continuous summary and research. Now the theory of marketing has gradually penetrated into the minds of every company and even individual. A successful marketing strategy is the inevitable result of scientific planning and effective implementation. However, the current marketing strategy has gradually failed to meet the needs of corporates. In order to find the best solution for corporate marketing strategy, we built a simulation environment based on mobile computing and machine learning and compared the differences by simulating several companies of the same size in this city (corporate efficiency and revenue and expenditure under the marketing strategy). The results of the study found that intelligent marketing based on machine learning is more suitable for enterprises than general marketing strategies. The efficiency of enterprises has increased by about 20%, and the income of enterprises has increased by more than 30% compared with traditional marketing strategies. This shows that the intelligent marketing strategy based on mobile computing and machine learning to build a simulated environment plays an extremely important role in the peculiarities.
TIEVis: a Visual Analytics Dashboard for Temporal Information Extracted from Clinical Reports

Clinical reports, as unstructured texts, contain important temporal information. However, it remains a challenge for natural language processing (NLP) models to accurately combine temporal cues into a single coherent temporal ordering of described events. In this paper, we present TIEVis, a visual analytics dashboard that visualizes event-timelines extracted from clinical reports. We present the findings of a pilot study in which healthcare professionals explored and used the dashboard to complete a set of tasks. Results highlight the importance of seeing events in their context, and the ability to manually verify and update critical events in a patient history, as a basis to increase user trust.
Point of Interest Recommendation Based on Graph Convolutional Neural Network

Abstract The rapid development of the mobile Internet makes location-based social networks (LBSNs) play an increasingly important role in practical applications. Among them, point of interest(POI) recommendation is a research hotspot in the current context. As a kind of graph data, social network can naturally express the data structure in real life. In view of the current POIs recommendation research ignoring the diversity of graph data, we proposed a POI recommendation based graph convolutional neural network (PBGCN) model, which used the check-in information, popularity characteristics of interest points, and users’ social behaviors to recommend interest points through graph convolutional neural networks(GCN). Compared with other latest recommendation methods, our model has improved accuracy. This proves the feasibility of GCN in point of interest recommendation.
Method G: Uncertainty Quantification for Distributed Data Problems Using Generalized Fiducial Inference

It is not unusual for a data analyst to encounter datasets distributed across several computers. This can happen for reasons such as privacy concerns, efficiency of likelihood evaluations, or just the sheer size of the whole dataset. This presents new challenges to statisticians as even computing simple summary statistics such as the median becomes computationally challenging. Furthermore, if other advanced statistical methods are desired, then novel computational strategies are needed. In this article, we propose a new approach for distributed analysis of massive data that is suitable for generalized fiducial inference and is based on a careful implementation of a “divide-and-conquer” strategy combined with importance sampling. The proposed approach requires only small amount of communication between nodes, and is shown to be asymptotically equivalent to using the whole dataset. Unlike most existing methods, the proposed approach produces uncertainty measures (such as confidence intervals) in addition to point estimates for parameters of interest. The proposed approach is also applied to the analysis of a large set of solar images. Supplementary materials for this article are available online.
Research on User Portrait Based on Tag Word Embedding

User portrait technology can bring huge commercial value to enterprises. Based on movie recommendation, this article extracts user tags from movie information based on the movie's viewing history, then designs and constructs user vectors, uses K-means to cluster users, and uses the clustering results to train the classifier. This article uses the traditional machine learning method Naive Bayes, BP neural network and deep learning method BiLSTM are used as the classifier. The construction of user vector adopts three methods, namely constructing user vector only by word vector, constructing user vector only by TF-IDF, and combining word vector and TD-IDF to construct user vector, and designed six sets of experiments for comparison. Experimental results show that the method of combining word vector and TF-IDF has achieved good results overall, and can further improve the quality of the recommendation system.
Service Selection With Package Bundles and Compatibility Constraints

With the rapid proliferation of strategic alliances between service providers, enterprises cooperate towards service quality improvement and provide lower cost service bundles. This article presents a novel solution to the minimum cost service bundle selection problem for workflows in the presence of singleton subscription costs and service bundle offerings and compatibility requirements. Given a workflow specifying a set of tasks and a set of candidate services for each task, with a set of compatibility constraints between services, the selection problem has the objective of selecting the most suitable service offering(s) for each task. In this article, we analyze the selection problem in the presence of service bundle offerings. We present a novel multi-partite hyper-graph visualization of the selection problem and analyze its hardness. Additionally we present a novel combination of ILP and abstraction refinement as a potential solution, that is shown to expedite a naïve ILP based solution. We present experiments to substantiate this claim.
Sentimental Analysis of YouTube Videos

YouTube is the second most popular social media platform with two billion users. Every minute around thousand hours of videos are uploaded. On an average people watch one billion hours of YouTube videos per day. Every youtuber want their video to get popular and make best possible efforts. However, video can crawl at the top of search with help of clickbait, etc. which compromises the content of video. There are videos whose relevancy and quality are top-notch but cannot make to top five or ten. People watching such videos interact by commenting, liking and subscribing. Especially in education category, viewers interested in watching long marathons or tutorial series have to make choice wisely to avoid wastage of time. To get favorable videos on top list, the sentiments of comments, no. of likes, views, comments is considered. The objective is to provide well analyzed and relevant educational videos to the budding students by reducing valuable search time.
Evaluation Metrics of Spatial and Spatiotemporal Data Mining Techniques

In this era of big data, copious amount of spatial and spatiotemporal data is being generated in various domains like transportation, climate science, earth science, neuroscience, public safety, ecology, social science, etc. Since, inferring knowledge from these data is difficult, automation of knowledge discovery (KDD) process is necessary. Mining useful knowledge from spatial or spatiotemporal data is referred as spatial data mining (SDM) or spatiotemporal data mining (STDM), respectively. There are various techniques or algorithms available for knowledge discovery, wherein the quality and correctness of each technique is evaluated by some metrics or measures. In this article, several measures used for analysing SDM and STDM techniques are listed and classified based on the type of patterns obtained: co-location pattern, incremental co-location patterns, co-occurrence pattern and sequential patterns.
Web Automation in Health Care

Today's business applications are widely structured on web-based systems that involve information which is dense and demands extensive processing. Many of the tasks involved are repetitive, tedious that take up lot of time and money. Tasks which are periodical like form filling, screen scraping, data extraction and transfer between applications, website testing and periodical report generation are the major tasks that are readily benefitted from web automation. Our project aims at automation of web using RPA technology in healthcare industry, where a software robot or bot that mimics a human worker, logging into applications, entering data, calculating and completing tasks, and logging out.
Image local fuzzy enhancement method based on Improved Wavelet Transform

In order to improve the application effectiveness of the image as the experimental contrast index, it is necessary to enhance the fuzzy region in the image. However, the existing enhancement methods have the problems of low scale feature extraction accuracy and unsatisfactory enhancement effect. Therefore, the improved wavelet transform is applied to the image fuzzy enhancement. Firstly, the wavelet basis function of wavelet transform is constructed, and the threshold is re selected to complete the reconstruction of wavelet transform. Based on the improved wavelet transform method, the fuzzy dot matrix and plane feature matrix of the local fuzzy region in the image are constructed. Finally, the image quality index and the mean value of the gradient are calculated to enhance the local fuzzy regions in the image. The experimental results show that the proposed method can effectively extract the scale features of the image, and the enhancement effect is significantly better than the literature comparison method, indicating that the proposed fuzzy enhancement method has strong applicability.
Sequence labeling model based on hierarchical features and attention mechanism

Abstract Sequence labeling is a basic task in natural language processing, which is of great help to processing text information. Conventional sequence labeling approaches heavily rely on hand-crafted or language-specific features, which requires a lot of time. Therefore, most of the existing methods are based on the BiLSTM-CRF model, but how to use a neural network to extract useful information for each unit or segment in the input sequence becomes the main factor limiting the efficiency. Several BiLSTM-CRF based models for sequence labeling have been presented, but the major limitation is how to use neural networks for extracting useful representations for each unit or segment in the input sequence. In response to this problem, this paper proposes a sequence labeling algorithm based on hierarchical features and attention mechanism, which uses a hierarchical structure to integrate character-level and word-level information, and applies different attention mechanisms to these two layers of information. According to the structural characteristics of different levels, excavate more potential information. Finally, the previously captured and guided features are used for sequence tag prediction using CRF. Finally, the proposed model is subjected to comparative experiments and the results obtained are analyzed.
Heuristic Bilingual Graph Corpus Network to Improve English Instruction Methodology Based on Statistical Translation Approach

The number of sentence pairs in the bilingual corpus is a key to translation accuracy in computational machine translations. However, if the amount goes beyond a certain degree, the increasing number of cases has less impact on the translation while the construction of translation systems requires a considerable amount of time and energy, thus preventing the development of a statistical translation by the computer. This article offers a number of classifications for measuring the amount of information for each pair of sentences, using the Heuristic Bilingual Graph Corpus Network (HBGCN) to form an improved method of corpus selection that takes the difference between the first amount of information between the pairs of sentences into account. Using a graphic-based selector method as a training set, they achieve a close translation result through our experiments with the whole body and achieve better results than basic results for the following based on the Document Inverse Frequency (DIF) ranking approach.
Transform Based Approach for Handwritten Character and Numeral Recognition: A Comprehensive Approach

Handwritten Character Recognition is one of the emerging fields of the Pattern recognition which has huge application in this current digital age of world. Here we have completely focused on the variant and invariant nature of the data image. Here we have attempted to design an automated handwritten Characters and Numerals recognition of Odia script one the oldest regional language. Here we have made an attempt to solve the ambiguity among the Odia handwritten character, numerals. Here all the variation was reported with transformation-based approach such as Discrete Cosine Transformation, Wavelet Transformation, Curvelet Transformation has been listed up here. Apart from it we have also achieved dimension reduction to the desired feature vector processed through PCA. In the simulation set up we have maintained certain training testing ratio, along with we performed classification using Neural Network, RBF- Neural Network, Support vector Machine etc. and achieve good recognition rate.
Modifiers and Membership Functions in Fuzzy Sets

In fuzzy theory, modalities (like possibly and necessarily) and hedges (like quite, very, and extremely) are the most commonly examined unary operators. Here, we introduce two reasonable approaches for defining these unaries; by repeating the arguments of many-variable operators and by using compositions of negations. This way, hedges and also modalities can be viewed as a part of a logical system. We show that membership functions, which play a substantial role in the overall performance of fuzzy representation, can also be defined using a generator function. In the literature, the membership functions are usually chosen independently of the logical operators of the system. Parameters are normally fine-tuned based on pure experimental results. Now, we make a suggestion of how modifiers and membership functions can be linked to the logical operators of the system. This unified framework will be useful and aid better interpretability of neural computations in Chap. 9.
The Development of Mobile Education Resource Database Under the Concept of Ubiquitous Learning

In order to provide learners with anytime, anywhere access to education and teaching resources and learning environment, this paper designs and implements a mobile education resource management platform based on Android system and the research of domestic and foreign education and teaching resource database, as well as the ubiquitous learning theory. The server side of the mobile learning system adopts Java and MySQL database for system platform data storage, while the client-side development is based on MVC software framework. The communication protocol uses OKHTTP framework networking request, data storage uses shares preferences data storage, file storage and network storage. The function of the server and the client are designed in detail. From the perspective of system construction, the overall design of the whole database and the implementation method of the front-end display platform are introduced. The system running test results show that the learners can browse resources through their mobile terminal devices. The platform achieves the expected design goals, and it is conducive to improving the quality of mobile teaching.
A Privacy-Preserving Route Leak Protection Mechanism Based on Blockchain

Route Leak Protection (RLP) is proposed to prevent route leaks in Border Gateway Protocol (BGP). However, the Autonomous System (AS) cannot detect the RLP information that may have been tampered, due to the lack of full view of all the inter-domain routing policies. In addition, RLP may leak the commercial relationship with other ASes and routing policies that ASes want to keep confidential. In this paper, we present a privacy-preserving route leak protection mechanism called RLPchain based on the blockchain and trusted execution environment. In RLPchain, each AS maintains a global confidential and tamper-proofing inter-domain routing policy repository, detecting and preventing inter-domain routing leaks. Moreover, a compatible deployment scheme is designed with only minor changes to the current BGP system. Finally, we implement the RLPchain and evaluate its performance on Microsoft Azure. Experimental results show that RLPchain has great performance in preventing routing leaks without trading off the privacy of routing policies.