Reviewerly/exHarmony huggingface dataset:
Concerto: a framework for combined concrete and abstract interpretation

Abstract interpretation promises sound but computable static summarization of program behavior. However, modern software engineering practices pose significant challenges to this vision, specifically the extensive use of frameworks and complex libraries. Frameworks heavily use reflection, metaprogramming, and multiple layers of abstraction, all of which confound even state-of-the-art abstract interpreters. Sound but conservative analysis of frameworks is impractically imprecise, and unsoundly ignoring reflection and metaprogramming is untenable given the prevalence of these features. Manually modeling framework behaviors offers excellent precision, at the cost of immense effort by the tool designer. To overcome the above difficulties, we present Concerto, a system for analyzing framework-based applications by soundly combining concrete and abstract interpretation. Concerto analyzes framework implementations using concrete interpretation, and application code using abstract interpretation. This technique is possible in practice as framework implementations typically follow a single path of execution when provided a concrete, application-specific configuration file which is often available at analysis time. Concerto exploits this configuration information to precisely resolve reflection and other metaprogramming idioms during concrete execution. In contrast, application code may have infinitely many paths of execution, so Concerto switches to abstract interpretation to analyze application code. Concerto is an analysis framework, and can be instantiated with any abstract interpretation that satisfies a small set of preconditions. In addition, unlike manual modeling, Concerto is not specialized to any specific framework implementation. We have formalized our approach and proved several important properties including soundness and termination. In addition, we have implemented an initial proof of concept prototype of Concerto for a subset of Java, and found that our combined interpretation significantly improves analysis precision and performance.
Transductive Bounds for the Multi-Class Majority Vote Classifier

In this paper, we propose a transductive bound over the risk of the majority vote classifier learned with partially labeled data for the multi-class classification. The bound is obtained by considering the class confusion matrix as an error indicator and it involves the margin distribution of the classifier over each class and a bound over the risk of the associated Gibbs classifier. When this latter bound is tight and, the errors of the majority vote classifier per class are concentrated on a low margin zone; we prove that the bound over the Bayes classifier’ risk is tight. As an application, we extend the self-learning algorithm to the multi-class case. The algorithm iteratively assigns pseudo-labels to a subset of unlabeled training examples that have their associated class margin above a threshold obtained from the proposed transductive bound. Empirical results on different data sets show the effectiveness of our approach compared to the same algorithm where the threshold is fixed manually, to the extension of TSVM to multi-class classification and to a graph-based semi-supervised algorithm.
Distributed Representation of Words in Cause and Effect Spaces

This paper focuses on building up distributed representation of words in cause and effect spaces, a task-specific word embedding technique for causality. The causal embedding model is trained on a large set of cause-effect phrase pairs extracted from raw text corpus via a set of high-precision causal patterns. Three strategies are proposed to transfer the positive or negative labels from the level of phrase pairs to the level of word pairs, leading to three causal embedding models (Pairwise-Matching, Max-Matching, and AttentiveMatching) correspondingly. Experimental results have shown that Max-Matching and Attentive-Matching models significantly outperform several state-of-the-art competitors by a large margin on both English and Chinese corpora.
Image Noise Level Estimation for Rice Noise Based on Extended ELM Neural Network Training Algorithm

The estimation of image noise level is a critical task for image denoising or super-resolution reconstruction. Mathematical methods like patch-based or model-based methods, suffer from the sensitivity of the selection of homogeneous regions or the selection of a proper statistic model, leading to inaccurate estimation, especially in signal-dependent noise cases, such as Rice noise. Ordinary, fully connected networks often suffer from the over-fitting problem, restricting their usage for realistic images. This article proposes a deep-learning-based algorithm by building a deep neural network, and train it by using the evolutionary genetic algorithm and extreme learning machine (ELM) algorithm extended into Hinton’s dropout framework. By combining the evolutionary genetic algorithm and the proposed extended ELM algorithm, comparative results are obtained, showing higher accuracy and better stability than several state-of-the-art algorithms.
Optimal Power and Performance Management for Heterogeneous and Arbitrary Cloud Servers

We investigate optimal power and performance management for heterogeneous and arbitrary cloud servers in a data center. In particular, we study the problems of power-constrained performance optimization and performance-constrained power optimization in a data center with multiple heterogeneous and arbitrary servers. These problems are essential to find optimal server speeds, such that: 1) the average task response time is minimized, and that the total power consumption does not exceed certain power constraint or 2) the total power consumption is minimized, and that the average task response time does not exceed certain performance constraint. Each server is treated as a G/G/1 queuing system, whose task interarrival times and task execution requirements can have arbitrary probability distributions. Furthermore, these servers are entirely heterogeneous in terms of task interarrival time, task execution requirement, and power consumption models. The main contributions of this paper are summarized as follows: 1) we formulate the average task response time as well as the total power consumption in a data center with multiple heterogeneous and arbitrary servers as the functions of server speeds; 2) we define our optimization problems by finding optimal server speeds, since the server speeds determine both the average task response time and total power consumption; 3) we develop algorithms to find the optimal solutions and demonstrate numerical data; and 4) we also develop several closed-form heuristic solutions and compare their quality with that of the optimal solution. Our approach provides an analytical way of studying the power-performance tradeoff at the data center level.
Requirement-driven Taxonomy Development – A Classification of Blockchain Technologies for Securities Post-Trading

In recent years, blockchain and distributed ledger technology (DLT) and its disruptive potential has been one of the most discussed topics in the field of information systems.Driven by the prospect of cost savings and efficiency gains, financial markets are at the core of these discussions.However, in the increasingly convoluted and constantly evolving market of technology providers and platforms, organizations struggle to find a solution that fulfills the specific requirements of their application scenario.To evaluate the suitability of different blockchain-based platforms for securities post-trading, we develop a new methodology to create a technology classification that takes the demands of a specific application context into account.The resulting requirement-based taxonomy sheds light on factors that impede the adoption of blockchain-and DLT-based post-trading, highlights future research challenges, and offers a valuable tool to induce communication between involved stakeholders.
Creativity Level of Hearing Impaired and Hearing Students of Federal College of Education

This study investigated creativity Level of Hearing Impaired and Hearing Students of Federal College of Education in Oyo.Specifically, the study investigated the creativity level of hearing and hearing-impaired students.Also, a relationship between gender and onset of hearing loss and students' creativity level were found out.A descriptive survey design was adopted for this study.A purposive sampling technique was used to sample a total of 248 second year students Nigerian Certification in Education (NCE II) of the School of Education who participated in the study.A researcher-designed questionnaire with a reliability coefficient of 0.76 was used to collect data for the study.The Findings showed that the creativity level of hearing-impaired students is significantly higher compared to their hearing peer.It was revealed that female hearing impaired students significantly show a higher level of creativity than their male peer.Also, post-lingual hearing-impaired students are significantly higher in creativity level than their pre-lingua peers.Based on these findings, it was recommended that mainstreaming approach of providing education services adopted by Federal College of Education in Oyo should be strengthened and adopted in all institution where hearing and hearingimpaired attend.
Research on College English Writing Teaching under the Background of Big Data: Taking Leshan Normal University as an Example

In recent years, online education has been in the ascendant in China’s education market. Among many competitors in this market, the Pigai system, an intelligent online English essay marking system based on big data analysis, is standing out from the crowd. More and more universities, colleges, even middle schools are taking advantage of it. This paper mainly introduces the reform of writing teaching model by utilizing this system in Leshan Normal University. The experiment of the teaching reform has shown that Pigai can shorten the working hours for teachers, develop students’ habit of autonomous study and improve their writing motivation and good language expression ability.
No-Reference Image Quality Assessment with Local Gradient Orientations

Image processing methods often introduce distortions, which affect the way an image is subjectively perceived by a human observer. To avoid inconvenient subjective tests in cases in which reference images are not available, it is desirable to develop an automatic no-reference image quality assessment (NR-IQA) technique. In this paper, a novel NR-IQA technique is proposed in which the distributions of local gradient orientations in image regions of different sizes are used to characterize an image. To evaluate the objective quality of an image, its luminance and chrominance channels are processed, as well as their high-order derivatives. Finally, statistics of used perceptual features are mapped to subjective scores by the support vector regression (SVR) technique. The extensive experimental evaluation on six popular IQA benchmark datasets reveals that the proposed technique is highly correlated with subjective scores and outperforms related state-of-the-art hand-crafted and deep learning approaches.
Data Prefetching and Eviction Mechanisms of In-Memory Storage Systems Based on Scheduling for Big Data Processing

In-memory techniques keep data into faster and more expensive storage media for improving performance of big data processing. However, existing mechanisms do not consider how to expedite the data processing applications that access the input datasets only once. Another problem is how to reclaim memory without affecting other running applications. In this paper, we provide scheduling-aware data prefetching and eviction mechanisms based on Spark, Alluxio, and Hadoop. The mechanisms prefetch data and release memory resources based on the scheduling information. A mathematical method is proposed for maximizing the reduction of data access time. To make the mechanisms applicable in large-scale environments, we propose a heuristic algorithm to reduce the computational time. Furthermore, an enhanced version of the heuristic algorithm is also proposed to increase the amount of prefetched data. Finally, we perform real-testbed and simulation experiments to show the effectiveness of the proposed mechanisms.
Full-Range Leadership as a Predictor of Extra Effort in Online Higher Education: The Mediating Effect of Job Satisfaction

Online higher education has rapidly expanded in the United States and displays a great opportunity for growth.Coupled with the growth of e-learning is the need for adjunct faculty to satisfy the need for additional online classes.Despite the importance of online adjunct faculty, little research has been performed to determine their work experiences.This quantitative, correlational study investigated the predictive relationship between the perceived use of transformational, transactional, and laissez-faire leadership behaviors on the extra effort of adjunct faculty who facilitate online classes at a for-profit university in the United States.In a further investigation, the researcher investigated the variable of job satisfaction to determine if it mediated the relationship between leadership style and extra effort.The researcher used the Multifactor Leadership Questionnaire and Spector's Job Satisfaction survey to collect data used in inferential analysis.The researcher performed a stepwise multiple regression and a Baron and Kenny mediation analysis to answer the research questions.The results showed perceived transformational leadership behaviors displayed a statistically significant positive predictive relationship with extra effort, and job satisfaction was a partial mediator between the relationship of transformational leadership and extra effort.The results suggest transformational leadership is beneficial to the extra effort put forth by the sample of adjunct faculty who teach online classes.
IoT Devices Signals Processing based on Multi-dimensional Shepard Local Approximation Operators in Riesz MV-algebras

In this article we continue the study started in [8] to use Riesz MValgebras for IoT devices signals processing. The Shepard local approximation operators introduced in [8] were defines such that to approximate single variable functions. In real industrial usage, the signals coming from IoT devices may be influenced by mode than a parameter, and thus we introduce generalized Shepard local approximation operators that can approximate multi-dimensional functions and some numerical experiments are considered.
A Critical appraisal on Password based Authentication

There is no doubt that, even after the development of many other authentication schemes, passwords remain one of the most popular means of authentication.A review in the field of password based authentication is addressed, by introducing and analyzing different schemes of authentication, respective advantages and disadvantages, and probable causes of the 'very disconnect' between user and password mechanisms.The evolution of passwords and how they have deep-rooted in our life is remarkable.This paper addresses the gap between the user and industry perspectives of password authentication, the state of art of password authentication and how the most investigated topic in password authentication changed over time.The author's tries to distinguish password based authentication into two levels 'User Centric Design Level' and the 'Machine Centric Protocol Level' under one framework.The paper concludes with the special section covering the ways in which password based authentication system can be strengthened on the issues which are currently holding-in the password based authentication.
Automated User Authentication in Wireless Public Key Infrastructure for Mobile Devices Using Aadhar Card

Identification and authentication play a significant role in security controls.The manual process of user identification and authentication is time-consuming and tedious.The proposed research work aims to overcome this drawback and achieve user identification and authentication through an automated method using the Aadhar card.Aadhar project is developed by the Unique Identification Authority of India by merging biometrics and digitization.The details are stored in the form of a quick response code along with a 12-digit Aadhar card number.A model is built to capture the information from the Aadhar card.The mobile phones are used to obtain the data from the Aadhar card.The user validation is through twostep process using One Time Password and email address.The wireless public key infrastructure issues a digital certificate to the valid user.The proposed authentication and symmetric key exchange algorithm are formally verified and analyzed using automated validation of Internet security protocols and applications.The result of simulation proves that the proposed scheme is secure and safe.The experimental results show that time consumption for user authentication is acceptable.The acceptance of the proposed automated user identification and authentication model is authorized using a modified technology acceptance model.The result proves that there exists a strong relationship between the perceived usefulness of items and the Attribute Of Use (AOU).It also exhibits that the association between the AOU and user satisfaction is influential.The model demonstrates that the association between the Perceived Ease Of Use and attribute towards use is weak.The influence of exogenous variables over endogenous variables exhibits that the proposed model outperforms the traditional manual system and the target users are inclined towards it.The proposed model also finds its application in services which requires user authentication.
A comprehensive experimental comparison of the aggregation techniques for face recognition

In face recognition, one of the most important problems to tackle is a large amount of data and the redundancy of information contained in facial images. There are numerous approaches attempting to reduce this redundancy. One of them is information aggregation based on the results of classifiers built on selected facial areas being the most salient regions from the point of view of classification both by humans and computers. In this study, we report on a series of experiments and offer a comprehensive comparison between various methods of aggregation of outputs of these classifiers based on essential facial features such as eyebrows, eyes, nose, and mouth areas. For each of them, we carry the recognition process utilizing the well-known Fisherfaces transformation. During the comparisons of the vectors representing the features of images (faces) after the transformations, we consider 16 similarity$/$dissimilarity measures for which we select the best aggregation operator. The set of operators to compare was selected on a basis of the comprehensive literature review regarding aggregation functions.
Sentiment Analysis of Japanese Tourism Online Reviews

Abstract Purpose Online reviews on tourism attractions provide important references for potential tourists to choose tourism spots. The main goal of this study is conducting sentiment analysis to facilitate users comprehending the large scale of the reviews, based on the comments about Chinese attractions from Japanese tourism website 4Travel. Design/methodology/approach Different statistics- and rule-based methods are used to analyze the sentiment of the reviews. Three groups of novel statistics-based methods combining feature selection functions and the traditional term frequency-inverse document frequency (TF-IDF) method are proposed. We also make seven groups of different rules-based methods. The macro-average and micro-average values for the best classification results of the methods are calculated respectively and the performance of the methods are shown. Findings We compare the statistics-based and rule-based methods separately and compare the overall performance of the two method. According to the results, it is concluded that the combination of feature selection functions and weightings can strongly improve the overall performance. The emotional vocabulary in the field of tourism (EVT), kaomojis, negative and transitional words can notably improve the performance in all of three categories. The rule-based methods outperform the statistics-based ones with a narrow advantage. Research limitation Two limitations can be addressed: 1) the empirical studies to verify the validity of the proposed methods are only conducted on Japanese languages; and 2) the deep learning technology is not been incorporated in the methods. Practical implications The results help to elucidate the intrinsic characteristics of the Japanese language and the influence on sentiment analysis. These findings also provide practical usage guidelines within the field of sentiment analysis of Japanese online tourism reviews. Originality/value Our research is of practicability. Currently, there are no studies that focus on the sentiment analysis of Japanese reviews about Chinese attractions.
Dynamic generation of multi-qubit entanglement in the ultrastrong-coupling regime

Abstract We propose a dynamic evolution protocol for generating multi-qubit GHZ states in the ultrastrong-coupling regime of circuit QED. By varying the time length of sequences, the protocol works for any coupling strength g / ω r ≥ 0.25. The time for generating the GHZ states in our protocol can be in the subnanoseconds. By taking into account realistic parameters of circuit QED, the degeneracy of fidelity due to decoherence can be as low as 0.02%.
A Proposed Internet of Everything Framework for Disease Prediction

Social networks and Internet of things are two paradigms when integrated a new paradigm Internet of Everything is established that has its impact on revolutionizing various fields such as engineering, industry and healthcare. Social networks became nowadays of the most important web services on which people heavily rely, thus became a major source for information extraction for rational decision making considering individuals as social or socio sensors. Furthermore, people using sensors especially biological sensors enabled the use of internet of things technology in building intelligent healthcare systems. One of the challenges facing the design of such systems is the design of an intelligent recommender system that is able to deal with such big data. For that, this paper proposes a framework to develop an enhanced intelligent expert advisor-based health monitoring and disease awareness system. The proposed framework enables the researchers to design advisory systems that are able to observe physiological signals through the use of different bio sensors and integrate it with historical medical data together with the massive data collected from social networks to provide accurate alerts and recommendations for many ailments inspected. The proposed Framework is designed to facilitate generic, dynamic and scalable process of integrating different types of social networks and bio sensors.
FDI, banking crises and growth: direct and spill over effects

This study suggests a new decomposition of the effect of foreign direct investment (FDI) on the long-term growth of developing countries. It reveals that FDI not only has a direct positive effect on growth, but also increases it by reducing the recessionary effect resulting from a banking crisis. However, these advantages are conditioned by the FDI threshold, which in turn depends on the ‘absorption capacity’ of the host country.