Reviewerly/exHarmony huggingface dataset:
Secure Outsourcing Algorithm for Bilinear Pairings without Pre-Computation

The security is an important issue of affecting the wide applications for Internet of Things(IoT). The security of IoT is mainly based on cryptographic algorithms. Bilinear pairing, as a basic operation, is widely used in many cryptographic algorithms such as identity-based cryptographic algorithms. However, it is difficult to directly run bilinear pairing on the IoT devices because the bilinear pairing is very time-consuming. In this paper, we explore how to achieve securelatex XX.texly outsourcing bilinear pairings for IoT devices. We design an efficient algorithm that outsources the bilinear pairing in the one-malicious version of two untrusted program model for resource-limited IoT devices. Different from previous algorithms, the designed algorithm does not require client store large-scale pre-computation results, while still keeps high computational efficiency on the client side. The detailed security analysis and experiment results show the practicability of our design.
The Enhanced Graphic Pattern Authentication Scheme Via Handwriting identification

Today, Smartphone is a necessary device for people connected to the Internet world. But user privacy and security are still playing important roles in the usage of mobile devices. The user was asked to enter related characters, numbers or drawing a simple graphic on the touch screen as passwords for unlocking the screensaver. Although it could provide the user with a simple and convenient security authentication mechanism, the process is hard to protect against the privacy information leakage under the strict security policy. Nowadays, various keypad lock screen Apps usually provides different type of schemes in unlocking the mobile device screen, such as simple-customized pattern, swipe-to-unlock with a static image and so on. But the vulnerability could provide a chance to hijacker to find out the leakage of graphic pattern information that influences in user information privacy and security.This paper proposes a new graphic pattern authentication mechanism to enhance the strength of that in the keypad lock screen Apps. It integrates random digital graphics and handwriting graphic input track recognition technologies to provide better and more diverse privacy protection and reduce the risk of vulnerability. The proposed mechanism is based on two factor identification scheme. First of all, it randomly changes digital graphic position based on unique passwords every time to increase the difficulty of the stealer's recording. Second, the input track of handwriting graphics is another identification factor for enhancing the complex strength of user authentication as well.
Understanding the Deployment Cost of Cloud Computing Services for the Higher Education Institutions

Cloud Computing (CC) have been widely adopted in the educational institutions during the past decade, as the virtual infrastructures and shared architectures offer various work groups and parties to benefit from the solutions. With the emergence of cloud centric applications, various technologies have allowed institutions to gain a wide range of applications and services that are beneficial for both the educators and students. Furthermore, CC have allowed institutions to reduce storage costs and up-front commitment for computing infrastructure implementations. This study quantifies the components and parameters when deploying a HPC center and adopting a CC based facility. We investigate and identify the CAPital Expenses (CAPEX) and OPerating Expenses (OPEX) for the Total Cost of Ownership (TCO) of a HPC center within a higher educational environment. Furthermore, we discuss the software licensing models for CC which can be classified into the perpetual and subscription-based licensing model. In this study, we discuss the financial benefits and opportunities for utilizing a CC based computing infrastructure and HPC center. We propose a cost benefit framework for the in-house compute facility and CC that can help institutions and educational stakeholders when deploying a HPC facility or CC model.
A Context based Coverage Model for Abstractive Document Summarization

Automatic abstractive summarization is one of natural language processing fields that generates a sequence of words representing important information of the input document. The sequence-to-sequence model, which is widely used in abstractive summarization, has a repetition problem in which the same sub-pattern is repeatedly generated in decode phases. To solve this problem, various coverage models have been proposed in machine translation. In automatic summarization, unlike machine translation, the lengths of input and summary documents are very different. Because the summary document is a compressed form of important meaning of the input document. Due to the nature of automatic summarization, it is difficult to apply a word position-based coverage model in machine translation directly. For automatic summarization, we propose a context based coverage model to consider the coverage based on the compressed meaning of the input document. The context based coverage is defined as the accumulated weighted average of the encoded word meaning by the attention scores. This considers the meaning of words rather than the position of words in the input document. In the experiment with CNN/DailyMail dataset, the proposed model shows better performance than the previous researches.
Kinship verification using Compound Local Binary Pattern and Local Feature Discriminant Analysis

Automatic kinship verification system is designed to verify kinship relations between given pair of face images. Child adopts many characteristic such as similarity in appearance, likes and dislike, behavior, voice from his/her parents due to overlapping of genes. There are various existing algorithms that can verify whether a given pair of face images share kinship relation. This paper proposes a new method based on compound local binary pattern (CLBP) and local feature-based discriminate analysis (LFDA) to improve kinship verification accuracy. A well known texture feature extraction method is local binary pattern (LBP), but LBP performance deteriorates in flat images. To overcome this drawback, extraction of texture features from face images are calculated by compound local binary pattern (CLBP) technique. Extracted features mainly represent facial characteristics but may also contain some noises. Further, local feature-based discriminate analysis (LFDA) is used as a feature selection method to reduce these noises and choose the most relevant facial features. LFDA reduces inter-class similarity and increases intra-class similarity. Proposed method uses KNN classifier with 5-fold cross-validation. Kinship images are collected from KinFaceW-I and KinFaceW-II dataset. Best mean accuracy for proposed method on KinFaceW-I and KinFaceW-II are 82.825 % and 89.36% respectively. Experimental results also outperforms existing methods on these datasets.
Designing Technology and Healthcare Delivery Systems to Support Clinician and Patient Care Experiences: A Multi-Stakeholder Systems Engineering Co-Design Methodology

Healthcare technology significantly improved human health for historically predominant acute conditions of infection and trauma. Today, over 78% of healthcare expenditures address chronic conditions. Healthcare services organically emerged to address acute conditions and formed decentrally by specialty as different departments across many different geographic locations. However, today's chronic conditions require the utilization of decentralized services with no centralized source. The patient is left to coordinate their care, introducing safety concerns. The effect of technology on patient health outcomes is mediated by how these tools are integrated into the healthcare delivery system. Thus, patients experience poorly coordinated care, while clinicians experience increasing operational burden. Therefore, suboptimal care experiences suggest a need to evaluate healthcare technology and system design, developments, and implementations. To address the issue of technological integration across a decentralized socio-technical system, this paper presents a design methodology that combines principles from engineering systems, axiomatic design, healthcare systems architecture, coproduction, and user-centered design to develop healthcare delivery systems designed to enhance clinician and patient care experiences. This multi-stakeholder co-design process uses a systems thinking framework to incorporate technology that addresses stakeholder human values, universal access to technology and services, and designs for rather than reacts to the impact of technology.
Inducing stock market lexicons from disparate Chinese texts

Purpose The purpose of this paper is to propose a methodology to construct a stock market sentiment lexicon by incorporating domain-specific knowledge extracted from diverse Chinese media outlets. Design/methodology/approach This paper presents a novel method to automatically generate financial lexicons using a unique data set that comprises news articles, analyst reports and social media. Specifically, a novel method based on keyword extraction is used to build a high-quality seed lexicon and an ensemble mechanism is developed to integrate the knowledge derived from distinct language sources. Meanwhile, two different methods, Pointwise Mutual Information and Word2vec, are applied to capture word associations. Finally, an evaluation procedure is performed to validate the effectiveness of the method compared with four traditional lexicons. Findings The experimental results from the three real-world testing data sets show that the ensemble lexicons can significantly improve sentiment classification performance compared with the four baseline lexicons, suggesting the usefulness of leveraging knowledge derived from diverse media in domain-specific lexicon generation and corresponding sentiment analysis tasks. Originality/value This work appears to be the first to construct financial sentiment lexicons from over 2m posts and headlines collected from more than one language source. Furthermore, the authors believe that the data set established in this study is one of the largest corpora used for Chinese stock market lexicon acquisition. This work is valuable to extract collective sentiment from multiple media sources and provide decision-making support for stock market participants.
Analysis and Solution of Interactive Problems in Rural Synchronous Music Live Classroom Teaching

In the context of the information age, in order to further improve the teaching level and teaching effect in remote areas and promote the balanced development of education, synchronous live classes are used to connect remote and scattered rural primary schools and teaching sites, so as to share high-quality resources and make progress together. However, in the process of practical teaching, it is not good for students' face experience and classroom interaction, because there are lack of teachers, weak professional, much distal class and so on. After observing 34 synchronized music live classes of three music teachers, the author analyzed the interactive problems of music live class teaching in some rural areas, and put forward corresponding countermeasures to help relevant teachers to carry out synchronized music live class teaching.
A Legal Interpretation of Choreography Models

Model/driven smart contract development approaches are gaining in importance since one of the most popular realizations, blockchain/based smart contracts, are prone to coding errors. However, these modeling approaches predominantly focus on operational aspects of smart contracts, neglecting the legal perspective as manifested by deontic concepts such as obligations or permissions. In this paper, we explore an approach at connecting existing models to Legal Ontologies (LOs) on the example of choreography models, effectively interpreting them as legal contracts. We show how the execution of a choreography imposes sequences of legal states, and discuss consequences and limitations.
A Two-Phase Approach for Stance Classification in Twitter Using Name Entity Recognition and Term Frequency Feature

People can trace a course or derivation of subject from the tweet that the speaker is likely against the subject or target. The purpose of the task is to develop the automated systems to determine whether people can infer the twitter. Most of automatic stance classification systems generally need to recognize parts of related information that may not be present in the focus text. And it also needs to decide the tweet whether favor or against the topic depending on the condition if this tweet present the target or focus text. Name Entity Recognition (NER) is the one of the best method to classify the stance for neutral and non-neutral. If the stance is non-neural, the term frequency weighting feature is extracted to classify the tweet whether favors or against the target. The main purpose of this paper is to identify the tweet by using the combination of NLP and text mining based features. System Performance in SemEval 2016 Task A and Task B confirm the success of our approach to classifying the stance in Twitter. Our approach for stance classification is also suitable in other social data and opinion mining problems in social network.
An Easier and Efficient Framework to Annotate Semantic Roles: Evidence from the Chinese AMR Corpus

Semantic role labeling (SRL) is a fundamental task in Chinese language processing, but there are three major problems about the construction of SRL corpora. First, disagreements occurred in previous studies over the definition and number of semantic roles. Second, it is hard for static predicate frames to cover dynamic predicate usages. Third, it is unable to annotate the dropped semantic roles. Abstract Meaning Representation (AMR) is a new method which provides a better solution to the above problems. The researchers use 5,000 sentences in the Chinese AMR corpus to make a comparison between AMR and other SRL resources. Data analysis shows that within the framework of AMR, it is easier to annotate semantic roles based on simplified distinction between core and non-core roles. In addition, 1,045 tokens of dropped roles are annotated under this new framework. This study indicates that AMR offers a better solution for Chinese SRL and sentence meaning processing.
Word Recognition Techniques for Kannada Handwritten Documents

Word Spotting has been an evolving field of research in the past few decades. Starting off with the word spotting in printed text the evolution has now dawned upon the spotting of words in handwritten documents. Recognition of handwritten words is a challenging problem because of varied styles of characters and similarity of their shape. Word Spotting makes document indexing easier and furthers helps in interpreting the documents. In this work we compare and analyze various method by which word recognition can be done in Kannada handwritten documents. When a user gives a query in the form of an image [1], the word is recognized displaying its corresponding unique label. Various feature extraction techniques along with Deep Learning techniques have been tried out. We find that CNN with a spatial layer transformation outperforms the other techniques for Kannada Handwritten words.
Research for Personalized Learning Reasource Recommendation Model based on Academic Emotions

The separation of teachers and students in the online learning environment makes learners' academic emotions more prone to fluctuations. Usually the learners cannot get effectively assistance. This study monitors, mines and processes the learner's behavioral data from the two dimensions of concentration and interest, and constructs the learner's academic emotion model. This paper uses collaborative filtering recommendation algorithm to construct a recommendation model based on academic emotions. Learning resources is recommended to learners to help learners adjust their emotions and maintain positive academic emotions.
An Experiment of Human Presence and Movement using Impulse Radar and Machine Learning

Research and product development using machine-learning technology is ongoing worldwide. It is especially active in the field of predicting a person's activity. In this paper, a person's presence and movement are determined through machine learning by using radar signals. The preprocessing stage of receiving and processing radar signals produces a basic dataset. Next, we create a machine-learning model and conduct a prediction experiment with four test-sets that is created for this paper. Experiments conducted in this study showed a high level of classification accuracy. However, data sets, pre-treated only by classification of signals, seemed to be quite sensitive to the quality of the data when used as a feature of machine learning.
Model-based clustering with envelopes

Clustering analysis is an important unsupervised learning technique in multivariate statistics and machine learning. In this paper, we propose a set of new mixture models called CLEMM (in short for Clustering with Envelope Mixture Models) that is based on the widely used Gaussian mixture model assumptions and the nascent research area of envelope methodology. Formulated mostly for regression models, envelope methodology aims for simultaneous dimension reduction and efficient parameter estimation, and includes a very recent formulation of envelope discriminant subspace for classification and discriminant analysis. Motivated by the envelope discriminant subspace pursuit in classification, we consider parsimonious probabilistic mixture models where the cluster analysis can be improved by projecting the data onto a latent lower-dimensional subspace. The proposed CLEMM framework and the associated envelope-EM algorithms thus provide foundations for envelope methods in unsupervised and semi-supervised learning problems. Numerical studies on simulated data and two benchmark data sets show significant improvement of our propose methods over the classical methods such as Gaussian mixture models, K-means and hierarchical clustering algorithms. An R package is available at https://github.com/kusakehan/CLEMM.
A Dynamic Genetic-Based Context Modeling Approach in Internet of Things Environments

Internet of Things (IoTs) enables entities every day to communicate and collaborate with each other for providing information, data and services to inhabitants and users.IoTs consists of a large number of smart devices that can generate immense amount of data with different types.These sensors raw data needs to be modeled in a certain structure before filtering and processing to provision context information.This process is called context modeling.Context modeling provides definition of how context data are structured and maintained through context aware system.However, employing model for every context type through context aware application is static and is specified by the application developer.The main problem in IoTs is that the structure of context data changes overtime, therefore static modeling cannot be adaptable for modeling these changes.In this paper, a new dynamic approach for context modeling based on genetic algorithm and satisfaction factor is proposed.Firstly, the proposed approach uses genetic algorithm to find the best matching between a set of contexts and a set of available context models.Secondly, it uses a satisfaction factor to calculate the satisfaction degree for each context with each available context model and select the context model with high satisfaction degree as the structure model of this context, dynamically.In addition, flexibility indicator property and context based are defined to measure the performance of the proposed approach.The results of conducted simulations show that the proposed approach achieves higher performance than static approach for context modeling.
Model-Driven Liaison of Organization Modeling Approaches and Blockchain Platforms

In recent years, a number of relevant works actively studied connections between enterprise engineering modeling methods and blockchain models, however we may indicate the open problem of “a last mile”: lacking principles, algorithms and tools for automatic generation of blockchain-related platform-specific models based on the organizational platform-independent models. In our research the objective was specified to develop a set of mapping rules and a corresponding software tool which facilitate automation of mapping between concepts of the ArchiMate enterprise architecture model and the HyperLedger Composer blockchain platform. The article describes the solution proposed and its evaluation in the test settings. According to the test evaluation, the development process was simplified.
A Clifford Analytic Signal-Based Breast Lesion Segmentation Method for 4D Spatial-Temporal DCE-MRI Sequences

Dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) has been increasingly used for lesion detection in breast cancer diagnosis for its capability to provide spatial-temporal information.However, the massive and complex 4D spatial-temporal DCE-MRI data make the diagnosis process lengthy and error-prone.Moreover, normal fibroglandular tissue is occasionally enhanced through background parenchymal enhancement (BPE), which can degrade the performance of current algorithms.We propose a new method using a 3D Clifford analytic signal (CAS) approach for breast lesion segmentation of DCE-MRI data.A 2D temporal image is constructed from all the 2D DCE-MRI slices at different scanning time points on a given transverse plane, according to the CAS approach.Then, a 3D Clifford temporal image (CTI) is constructed by successively stacking temporal images.The proposed CTI can distinguish lesion regions both visually and quantitatively compared to the traditional DCE-MRI subtraction image.Finally, we employ a fully convolutional network (FCN) model for breast lesion segmentation using the CTI as one of the inputs.Experimental results on an independent public dataset (TCIA QIN breast DCE-MRI) and a private household breast DCE-MRI dataset (TBD) show that the proposed method can achieve superior performance over current methods, both qualitatively and quantitatively.
Analysis of Automatic Code Generation Tools based on Machine Learning

At present, there are many researches on automatic code generation methods based on machine learning. However, there is no analysis on automatic code generation tools based on machine learning. According to the role of existing code generation tools based on machine learning in practical development, this paper proposes the content and the method of analysis of automatic code generation tools based on machine learning. Based on the presented method, the corresponding analysis plug-ins API4ACGT are implemented. Finally, three existing automatic code generation tools based on machine learning are used to generate specific codes in two programming environments. The experimental results show that the proposed method and analysis plug-ins API4ACGT are effective.
Super Payment Channel for Decentralized Cryptocurrencies

Payment channel with penalizing double-spending has been widely applied in cryptocurrencies for achieving fast payment. However, current payment channel solutions suffer at least one of the following problems: 1) large overheads and communication costs are incurred to thwart double-spending; 2) payees must be defined before building payment channels; 3) a single payment channel does not support multiple payees; 4) no formally theoretical proofs are provided for the security of payment channels (as an open problem mentioned in CCS'15). In this paper, we fill these gaps by introducing a new fast payment protocol named super payment channel. We provide detailed construction based on digital signatures and accountable assertions with formal security proofs. We also implement our proposed protocol in different circumstances and demonstrate that our proposed protocol incurs a small overhead compared to ordinary Bitcoin transactions.