Reviewerly/exHarmony huggingface dataset:
Leveraging Software Quality Attributes in NoSQL Database Systems

The increasing interest in Not Only Structured Query Language (NoSQL) database systems has pruned debates among technical and business personnel in many organisations, about its quality and value to their businesses. Also, existing work has been silent on how software engineers can gain more benefits from the diverse nature of NoSQL technologies, from software quality attributes perspective. Thus, the objectives of this study were to: (1) understand the factors that influence leveraging of software attributes to business activities; and (2) examine how software quality attributes can be leveraged with NoSQL database systems. In achieving the objectives, the qualitative methods were employed. The interpretivist approach was followed in the analysis of the data. Based on the analysis, seven factors were found to influences the quality attributes in the leveraging of the NoSQL database systems in organisations. Based on the interpretation of the findings, a leveraging model was proposed. The model is intended to provide a guide on how software quality attributes can be leveraged with NoSQL database systems in organisation. This includes to facilitate NoSQL technology selection.
A deep reinforcement learning method based on attentional memories

In continuous visual decision-making scenarios, the environment is often partially observable rather than globally observable, and traditional deep reinforcement learning methods are often unable to effectively learn to discover hidden information in partially observable environments. In this work, a continuous visual decision-making algorithm AMU-DQN for partially observable environments is proposed by combining the proposed attentional memories unit(AMU) which can integrate temporal and spatial features in historical sequences and deep Q-network. Due to the special mechanism of the AMU recurrent layer, the AMU-DQN algorithm has the ability to learn favorable hidden information from the historical observation information sequence in a partially observable environment, forming a special attention and memory ability. Extensive simulation experiments show that AMU-DQN exhibits super-high performance in both convergence speed and reward convergence peak.
Code2tree: A Method for Automatically Generating Code Comments

Source code comments can improve the efficiency of software development and maintenance. However, due to the heterogeneity of natural language and program language, the quality of code comments is not so high. So, this paper proposes a novel method Code2tree, which is based on the encoder-decoder model to automatically generate Java code comments. Code2tree firstly converts Java source code into abstract syntax tree (AST) sequences, and then the AST sequences are encoded by GRU encoder to solve the long sequence learning dependency problem. Finally, the attention mechanism is introduced in the decoding stage, and the quality of the code comment is improved by increasing the weight of the key information. We use the open dataset java-small to train the model and verify the effectiveness of Code2tree based on common-used indicators BLEU and F1-Score.
Keyword Extraction for Medium-Sized Documents Using Corpus-Based Contextual Semantic Smoothing

Keyword extraction refers to the process of selecting most significant, relevant, and descriptive terms as keywords, which are present inside a single document. Keyword extraction has major applications in the information retrieval domain, such as analysis, summarization, indexing, and search, of documents. In this paper, we present a novel supervised technique for extraction of keywords from medium-sized documents, namely Corpus-based Contextual Semantic Smoothing (CCSS). CCSS extends the concept of Contextual Semantic Smoothing (CSS), which considers term usage patterns in similar texts to improve term relevance information. We introduce four more features beyond CSS as our novel contributions in this work. We systematically compare the performance of CCSS with other techniques, when implemented over INSPEC dataset, where CCSS outperforms all state-of-the-art keyphrase extraction techniques presented in the literature.
DiffeoRaptor: diffeomorphic inter-modal image registration using RaPTOR

Diffeomorphic image registration is essential in many medical imaging applications. Several registration algorithms of such type have been proposed, but primarily for intra-contrast alignment. Currently, efficient inter-modal/contrast diffeomorphic registration, which is vital in numerous applications, remains a challenging task.We proposed a novel inter-modal/contrast registration algorithm that leverages Robust PaTch-based cOrrelation Ratio metric to allow inter-modal/contrast image alignment and bandlimited geodesic shooting demonstrated in Fourier-Approximated Lie Algebras (FLASH) algorithm for fast diffeomorphic registration.The proposed algorithm, named DiffeoRaptor, was validated with three public databases for the tasks of brain and abdominal image registration while comparing the results against three state-of-the-art techniques, including FLASH, NiftyReg, and Symmetric image Normalization (SyN).Our results demonstrated that DiffeoRaptor offered comparable or better registration performance in terms of registration accuracy. Moreover, DiffeoRaptor produces smoother deformations than SyN in inter-modal and contrast registration. The code for DiffeoRaptor is publicly available at https://github.com/nimamasoumi/DiffeoRaptor .
<i>Wh</i>-Indefinites

Chapter 20 investigates the longstanding and controversial topic of the indefinite reading of so-called wh-words in Korean. The chapter reviews arguments regarding their historical development, typological observation, grammatical properties, and processing strategies.
A Hybrid Genetic, Differential Evolution Optimization Algorithm

This chapter presents a heuristic evolutionary optimization algorithm that is loosely based on the principles of evolution and natural genetics. In particular, this chapter describes an evolutionary algorithm that is a hybrid of a genetic algorithm and a differential evolution algorithm. This algorithm uses an elitist, ranking, random selection method, several mutation methods and both two level and three level Taguchi crossover. This algorithm is applied to 13 commonly used global numerical optimization test functions, including a spherical, three hyper-ellipsoid, the sum of different powers, Rastrigin’s, Schwefel’s, Griewank’s, Rosenbrock’s valley, Styblinski-Tang, Ackley’s Path, Price-Rosenbrock, and Eggholder’s functions. This algorithm is applied 1000 times to each of the 13 test functions, and the results shows that this algorithm always converges to each of the 13 test function’s global minimum.
Certificateless Remote Data Integrity Auditing with Access Control of Sensitive Information in Cloud Storage

With the spread of cloud storage technology, checking the integrity of data stored in the cloud effectively is increasingly becoming a concern. Following the introduction of the first remote data integrity audit schemes, different audit schemes with various characteristics have been proposed. However, most of the existing solutions have problems such as additional storage overhead and additional certificate burden. This paper proposes a certificateless remote data integrity auditing scheme which takes into account the storage burden and data privacy issues while ensuring the correctness of the data audit results. In addition, the certificateless design concept enables the scheme proposed in this paper to avoid a series of burdens brought by certificates. The scheme designed in this paper provides a data access control function whereby only users who hold a valid token generated by the data owner can access the target data from the cloud. Finally, this paper provides a detailed security proof to ensure the rationality of the results. A theoretical analysis and subsequent experimental verification show that the proposed scheme is both effective and feasible.
Research on Structured Extraction Method for Function Points Based on Event Extraction

Software size is a significant input for software cost estimation, and the implementation of software size estimation dramatically affects the results and efficiency of cost estimation. Traditionally, the software size estimation is implemented by strictly trained experts and is more labor-intensive for large software projects, which is relatively expensive and inefficient. Function Point Analysis is a widely used method for software size estimation, supported by several international standards. We propose a structured and automated function point extraction method based on event extraction in natural language processing to address the problem of complex and inefficient manual recognition for function point recognition. This approach has been validated in 10 industrial cases. Experimental results show that our method can identify more than 70% of the function points, which significantly improves the efficiency of Function Point Analysis implementation. This paper could be a guide on the application of artificial intelligence techniques to software cost estimation.
Explicit and Implicit Trust Modeling for Recommendation

Recommendation has become an inseparable component of many software applications, such as e-commerce, social media and gaming platforms. Particularly in collaborative filtering-based recommendation solutions, the preferences of other users are considered heavily. At this point, trust among the users comes into the scene as an important concept to improve the recommendation performance. Trust describes the nature and the strength of ties between individuals and hence provides useful information to improve the recommendation accuracy, particularly against data sparsity and cold start problems. The Trust notion helps alleviate the effect of these problems by providing additional reliable relationships between the users. However, trust information, specifically explicit trust, is not straightforward to collect and is only scarcely available. Therefore, implicit trust models have been proposed to fill in the gap. The literature includes a variety of studies proposing the use of trust for recommendation. In this work, two specific sub-problems are elaborated on: the relationship between explicit and implicit trust scores, and the construction of a machine learning model for explicit trust. For the first sub-problem, an implicit trust model is devised and the compatibility of implicit trust scores with explicit scores is analyzed. For the second sub-problem, two different explicit trust models are proposed: Explicit trust modeling through users’ rating behavior and explicit trust modeling as a link prediction problem. The performances of the prediction models are analyzed on a set of benchmark data sets. It is observed that explicit and implicit trust models have different natures, and are to be used in a complementary way for recommendation. Another important result is that the accuracy of the machine learning models for explicit trust is promising and depends on the availability of data.
Theoretical Investigations on Shadow Band Correction Factors for Diffuse Radiation under Isotropic Conditions without Approximation

The precise measurement for irradiance is important for the performance evaluations of solar systems, because both beam and diffuse radiation measurements must be known to calculate the efficiency of a solar system. One of the widely used instruments for diffuse radiation measurement is a pyranometer with a ring. Hence, investigations on shadow band correction factors are necessary. The factors are normally calculated by the expressions of Drummond and Burek et al. under isotropic conditions. However, their calculation results are approximate. While the latitude φ, the inclination angle β and the declination δ are satisfied with the condition of |−tan(φ−β)tanδ|&gt;1, the factors cannot be calculated by their expressions. In this paper, the expressions for the factors under isotropic conditions were obtained by coordinate transformations and discussed in two different coordinate systems without approximation. Our expressions can give precise correction factors in any case and overcome the disadvantages of their expressions. While the sensor is placed horizontally, the maximum relative deviation between the results by the expression in Drummond’s paper and by our expressions is less than 2.3%. When the sensor is placed on a sloped surface, the results by the expressions in Burek’s paper and by our expressions will have considerable differences in some ranges of slope angles and latitudes. The maximum relative deviation is more than 22%.
Resolving the confusion of the authorship attribution of a Bengali book

The present paper aims to determine whether the Bengali book Londoner Naksa ebong France Bhraman (Wondrous Capers at London and Travelling in France) was written by the geologist Pramathanath Bose (P.N. Bose). To find it out, two well-established style markers often used in authorship attribution studies; namely, function words and punctuation marks, are used here. The result shows that possibly this book was penned by the geologist P.N. Bose. As a corollary, it may also be added that this approach may be used in future authorship attribution studies involving Bengali writings.
Towards improving the security of image steganography via minimizing the spatial embedding impact

With the continuous advances in computer technology and electronics, image steganography, an important branch of information hiding aimed at hiding secret information in innocent digital images for covert communication, has also been developed significantly in recent years. Recent studies on image steganography have shown that non-additive distortion steganography schemes, e.g., Synch, CMD and Dejoin-J, can improve the security of additive steganographic distortion functions. These schemes are basically designed based on some heuristic experiences, whose essence can be attributed to implicitly reducing the spatial embedding impact. In view this, we develop a general Intra-block Modification Optimization (IbMO) strategy to improve the security of both spatial and JPEG image steganography via directly minimizing the spatial embedding impact, wherein the metric for spatial embedding impact is explicitly defined as the summation of absolute difference of noise residuals between cover and stego image. Note that, to apply it to JPEG image steganography, the IbMO strategy will be integrated into a newly proposed BBC-based Intra-block Joint Modification (IbJM) scheme with five-element crossing neighborhood. Experimental results indicate that the proposed IbMO strategy can obviously improve the security performances of additive distortion steganography schemes. Specifically, the IbMO strategy significantly outperforms Synch and rivals CMD on HiLL and MiPOD. The newly proposed IbJM scheme also shows better overall performance with less computational overhead than DeJoin-J on JUNIWARD, GUED, and BET, and its security performance can be further improved after integration with IbMO strategy.
Web Application with Built-in Remote Support Capability by Means of Voice Communication and Screen Sharing

We present how to implement a web application that has the capability of allowing the user to request a remote supporter for help by means of voice communication and screen sharing within the same application without using any other tools. Encountered by any troubles in operating the web application, the user only has to press a button on the same web application to call the remote supporter. Then the user’s web application establishes a WebRTC connection with the supporter’s web application via a signaling server. Once the communication is established, the user’s web application converts the current screen shot into an image by the html2canvas method and sends the image to the remote supporter’s application by a regular time interval. The user talks about the trouble with reference to the user screen. The remote supporter understands the trouble by listening to the user and watching the user’s screen shot displayed on the remote supporter’s application. The remote supporter tells the instructions while showing the supporter’s mouse pointer overlapped on the user’s web application window.
MVPC—A Classifier with Very Low VC Dimension

In real-world problems, patterns found within the training data can be different from those found in the test data (dataset shift); obtaining reliable results with handcrafted (low-level) features becomes difficult even with linear kernel SVM. Such problems necessitate the creation of classifiers with low variance and high generalization as a driving function. In this chapter, we show the variance of a class of majority vote classifiers named Majority Vote Point (MVP) classifier, to be lower than that of linear classifiers, on account of lower VC dimension.
Single-shot intraocular lens surface measurement with the GelSight topography system

The intraocular lens (IOL) industry is continuously evolving with more complex surface designs that require affordable and timely surface measurements of extended depth of focus (EDOF) and diffractive multifocal lenses. Current systems to measure grating profiles, such as AFM, SEM, or optical profilometers are expensive, need intensive training, and are sometimes destructive. Furthermore, the fields of view of these systems are typically limited, so measuring the full aperture of the lens requires repeated measures and stitching of the result. The system developed allows for quick profile measurements with easy to obtain equipment that will help examine and develop diffractive multifocal IOLs. This study integrates a 3D GelSight camera (GelSight, Inc, Waltham, Massachusetts), a stepper motor, and an Arduino board with driver board to automate the measurement of IOL gratings. The GelSight 1.0X camera has a height resolution of 1.0 μm and is provided with software that will be used to select and export data from areas of interest. Post processing will be required to analyze the data from the GelSight, but can be customized to the user’s needs. Using simple Arduino code and a stepper motor to move the camera onto the sample allows for a hands-free measurement technique that promotes accuracy and repeatability. Automation allows for beginners to quickly use the newly proposed system for many profile measurement applications with little setup time. The system will benefit the development of IOLs as a quick and easy check for the production process of these advanced lens designs.
Exploring the perception of changes in student competency and its implications: Focusing on the 2019 free semester system

본 연구는 자유학기제가 2016년도 이후 모든 중학교로 확대되어 운영되고, 2018년도 이후 자유학년제로 확대되는 환경의 변화를 바탕으로, 자유학기 경험 전후로 학생들의 역량이 어떻게 변화하는지를 탐색하고자 한다.연구 결과는 크게 2 가지로 도출되었다. 먼저, 자기관리 역량은 평균이 증가하였지만 통계적으로 유의하지 않았으며 지식정보처리 역량, 창의적 사고 역량, 심미적 감성 역량, 의사소통 역량은 평균이 증가하였고 통계적으로 유의하였다. 아울러 공동체 역량은 평균이 감소하였으며 통계적으로 유의하였고, 6개 역량을 모두 평균한 값의 차이를 비교하면 3.70점에서 3.73점으로 0.03점만큼 증가하였으며 이러한 차이는 통계적으로 유의하였다. 이를 종합하면 중학교 1학년의 자유학기 경험은 지식정보처리 역량, 창의적 사고 역량, 심미적 감성 역량, 의사소통 역량 변화에 긍정적으로 연관되어 있다고 추정된다. 다음으로, 자유학기를 마친 후 중학교 2학년 기간 중에 학생역량에 의미 있는 변화가 있었는지를 확인하기 위하여 t 분석을 시행하였고, 2학년이 마무리되는 12월에 시행된 조사 결과 학생들의 역량 평균은 영역에 따라 3.55~3.99의 점수를 보였다. 결국, 의사소통 역량이 가장 높았으며(M=3.99), 심미적 감성 역량이 가장 낮게 나타났다(M=3.59). 이는 학생들이 교육 프로그램에 얼마나 흥미를 느끼고 적극적으로 참여하는지가 역량의 변화에 유의하게 연관되어 있는 것으로 볼 수 있다.This study aims to explore how students' competencies change before and after the free semester experience based on the change in the environment where the free semester system has been expanded to all middle schools since 2016, and the free school year system has been expanded since 2018. The results of the study were mainly derived in two ways. First, the average of self-management competency increased, but it was not statistically significant. The average of knowledge information processing capacity, creative thinking capacity, aesthetic emotional capacity, and communication capacity increased and were statistically significant. In addition, the average of community competency decreased and was statistically significant. Comparing the difference in the average value of all six competency, it was statistically significant. Taken together, it is estimated that the free semester experience of the first year of middle school is positively related to knowledge information processing competency, creative thinking competency, aesthetic emotional competency, and communication competency change. Next, t-analysis was conducted to check whether there was a meaningful change in student competency during the second year of middle school after the free semester ended. In the end, the communication competency was the highest (M=3.99), and the aesthetic emotional competency was the lowest (M=3.59). It can be seen that the degree of interest and active participation of students in the educational program is significantly related to the change in competency.
A Network Learning Model for College Information Education Using Scientific Computing

The development of network technology has further promoted the informatization of colleges and universities; the network teaching resources have been continuously enriched; the network teaching management has been constantly standardized; and the development of scientific computing technology also occurred. Network learning has become a major learning method and is accepted by more and more people. In order to build an intelligent network learning model, this paper starts from data mining, index data processing, and evaluation system design from the construction of index data as the main line. Based on the analysis of the existing network learning model index system, this paper proposes a boundary change network learning index system and uses data mining technology to mine the student status and student learning activity information recorded in the web page and the background database. In this way, the key factors which can evaluate students' online learning effect are extracted and quantified. Then, based on the basic principles and algorithms of fuzzy logic, artificial neural network, and fuzzy neural system, artificial neural network and adaptive fuzzy neural system are used for network learning modeling, respectively, and the experimental simulation and performance analysis of the two models are carried out. The experimental results show that the test accuracy rates of the two models based on BP neural network and adaptive fuzzy neural network are 68.6% and 91.4%, respectively. The model based on the adaptive fuzzy neural network is better in the field of network learning, is feasible in theory, and has reliable results in practice, and can be used as an intelligent computing model for developing network learning systems. It has a good promotion value and realizes the intelligence and automation of online learning, thereby further improving the online learning platform and promoting the construction and use of online courses.