Reviewerly/exHarmony huggingface dataset:
Ensemble learning approach to classify user defined functions in Java programs

Source code of a computer program is vital for any development in information technology industry. Many experienced IT professionals reuse old source code functionalities in order to speed up the development process and utilize the resources. However, there are no any efficient approaches to identify the source code functionality name. Topic modelling is one of the statistical approaches to extract the topic of the semantic function by considering function names, comments and identifiers of a source code. These approaches tend to heavily depend on the programming best practices rather than the source code algorithms. In response to these observations, the author performed research in this work on extracting source code using java parser library and classifying source code functionality names using the Ensemble learning model. To implement the ensemble method, the author merged Decision Tree and Nave Bayes algorithms and use the majority voting mechanism to obtain the result of the ensemble approach. The data was collected from git hub repository and author built an algorithm to extract programming language keywords of the source code in order to feed the model. This is a novel method to topic modeling and the first effort at creating a machine learning model to classify the source code's semantic function names.
Dynamic Processes in Professional and Business Etiquette

The research focuses on the study of the dynamics of etiquette norms in the innovative communicative space. The paper considers the factors which may condition changes in etiquette norms and stimulate formation of new norms and rules. The contribution explains the importance of taking into account theoretical foundations of etiquette studies while considering innovative norms, and lays special emphasis on comparison of traditional and innovative norms. The article reveals the role of socio-cultural context in the process of implementation of etiquette norms, and outlines the factors which determine the variability of their functioning. The study highlights specific features of innovative etiquette norms: the focus of attention is on etiquette norms regulating behaviour in the online communicative environment – with special reference to business interaction, in particular, business interaction in the format of zoom conferences. The paper suggests classification of innovation norms, outlines etiquette gaps and ways of their elimination. The publication also explains the importance of practical valiability of the study, and accounts for the need in optimization of business etiquette training – with special reference to up-to-date conditions of professional interaction.
Analysis of Social Innovator Attributes in Bahrom Character Education

Recently, there has been a growing interest in social innovation serving as a new approach to social problem solving, and universities have gained much attention as social innovation actors. Since its establishment, Seoul Women’s University has been implementing Bahrom Character Education curricula as required courses, with the educational goal of “raising talents contributing to social value realization.” The purpose of this study is to examine the historical changes of Bahrom Character Education, to analyze the social innovator attributes from the curricula, and to investigate its implications on the social innovation education practices of today’s universities.&lt;/br&gt;To meet this end, the six major publications and research reports on ‘Bahrom Character Education’ were selected and analyzed. The findings convinced us that (1) each stage of Bahrom Character Education reflected the needs of the era, (2) the educational philosophy of “raising talents contributing to society” were embedded in the curricula, and that (3) action-oriented social problem solving courses helped the students to contribute to authentic social changes. Moreover, thematic analysis highlighted seven social innovator attributes: ‘collaborative self-understanding’, ‘collaborative self-management’, ‘sense of community’, ‘empathy and communication’, ‘comprehensive problem solving ability’, ‘citizenship’, and ‘leadership’, all of which shows that the curricula of Bahrom Character Education include key characteristics of social innovation education.&lt;/br&gt;The current study suggests that universities should make step-by-step as well as long-term growth goals for social innovation education and that the whole campus should establish social innovation education-friendly environments. However, in order to accomplish this, all university members should come to a consensus about the matter, and further provide psycho-social, administrative and financial support at the school level.
Data Structures for a Generic Software System using the Composite Design Pattern

A well-designed generic software system can be reused in many contexts by efficiently handling variability. In this paper, data structures and their application in a software architecture is presented that apply the basic idea of the Composite design pattern to keep it as simple as possible, but also as generic as possible. The application of these data structures is shown throughout a layered architecture so that software developers can follow and apply the concepts. The benefit of such an architecture is that it (1) only makes use of familiar concepts, and it is easy to read and understand by knowing especially one design pattern and (2) results in a generic software system, usable in different domains.
A Fused Inference Design for Pattern-Based Sparse CNN on Edge Devices

Weight pruning approaches for Convolution Neural Networks (CNN) has been well developed in the past years. Compared with traditional unstructured and structured pruning, the new state-of-the-art sparse convolution pattern (SCP) based pruning uses certain patterns that lead to both high pruning rate and low accuracy loss. This paper introduce a novel inference scheme to accelerate the execution of SCP-pruned models on IoT devices with limited resources. This inference scheme applies and combines ideas from direct sparse convolution and layer fusion. To fully utilize the power of modern IoT processors, the inference is also mapped to all available cores and optimized with SIMD instructions. The experimental results show good performance improvement as well as scalability of our scheme on an edge device.
An Optimized Course-Offering Approach Based on Multi-Agent Negotiation

Today, many educational institutions still use man-ual methods for Course Offering Determination (COD) problems, leading to many problems such as increased workload and time conflict. The COD problem is a vital research topic because the preferences of both students and the educational institute as well as various resource constraints should be satisfied. By adopting the advantages of the Multi-Agent System (MAS), this paper proposes a course-offering approach, which is responsible for selecting the courses to be offered in the upcoming semester for a group of students. In the course-offering approach, the paper designs three student preference models to transform students' personal expectations and learning plans into their preference for courses. To aggregate students' preferences, the course-offering approach introduces a voting mechanism based on Single Transferable Vote (STV). Besides, the research also uses a bilateral single-issue negotiation model to improve the overall satisfaction of students and the educational institution.
The Principles of 3D Photogrammetry Systems Used in Human Motion Capture and Postural Assessment

There are many different systems capable of objectively measuring and analyzing various motion parameters and signals. The focus of this chapter is on describing the measurements of kinematic parameters, e.g. spatial position, velocity and acceleration. To this end we introduce a photogrammetric approach where spatial three-dimensional (3D) measurements are derived from 2D camera images. The principle how a camera models 3D world into 2D image is described with the camera model parameters, and the computation of parameters through the camera calibration procedure is presented. Since 3D kinematic systems operate on a stereo principle, it requires imaging of the scene with minimum of two cameras and their spatial relationship is given in the form of epipolar geometry. We further turn our focus to the usage of 3D kinematic systems which is frequently defined by certain measurement protocol, tailored to analyzing a specific movement. Several popular possibilities where 3D kinematic system is jointly used with other types of devices are pointed out. Additionally, an inverse dynamics approach is briefly described and explained, allowing a derivation of internal kinetic data (joint forces and torques) combining an output of a 3D kinematic system with inertial body segment parameters. At the very end, several topics related to the research challenges and the anticipated development of 3D reconstruction systems are presented.
StreamChain: Building a Low-Latency Permissioned Blockchain For Enterprise Use-Cases

Permissioned blockchains are a promising technology for secure decentralized data management in business-to-business use-cases. In contrast to Bitcoin and similar public blockchains, which rely on Proof-of-Work for consensus and are deployed on thousands of geo-distributed nodes, business-to-business use-cases (such as supply chain management and banking) require significantly fewer nodes, cheaper consensus, and are often deployed in controlled environments with fast networking and low latency. However, permissioned blockchains often follow the architectural thinking behind their WAN-oriented public relatives, which results in end-to-end latency several orders of magnitude higher than necessary. In this work, we propose a fundamental shift in permissioned blockchain design, eliminating blocks in favor of processing transactions in a streaming fashion. This results in a drastically lower latency without reducing throughput or forfeiting reliability and security guarantees. To demonstrate the wide applicability of our design, we prototype StreamChain based on the Hyperledger Fabric, and show that it delivers latency two orders of magnitude lower than Fabric, while sustaining similar throughput. This performance makes StreamChain a potential alternative to traditional databases and, thanks to its streaming paradigm, enables further research, for instance, around reducing latency through relying on modern programmable hardware accelerators.
Cohesive Summary Extraction From Multi-Document Based On Artificial Neural Network

Increasing growth in the volume of digital data from documents performed the difficulty of accessing important information. The solution is using automatic summarization systems that aim to extract important information in a short time. Usually, these systems work to extract a single summary from a single document or multi-documents. However, extracting a summary from a multi-document may encounter some obstacles. This work focuses on how to overcome these obstacles and extract an appropriate and cohesion summary by presenting and suggesting four important contributions with a qualitative leap in the process of extracting the important information that seeks to extract a candidate summary that matches the sentences of the golden summary. The first suggestion is a set of features to extract important sentences and easy to understand, the second, build a Backpropagation Multi-layer Perceptron Neural Network (BMPNN) based on these features input to extract the score for each sentence, the third, using the Random oversampling method and its effective role in rebalancing the data during the training process in BMPNN, and finally, solving the problem of reordering sentences in the candidate summary according to the importance of the sentence depending on one of the features. The results of the evaluation Rouge-1, Rouge-2, and Rouge-L measures showed that the candidate's summary is very close to the golden summary in terms of matching sentences, and it achieved very good results.
EMO-GAT: An Evolutionary Multi-Objective Method for Adversarial Training

Adversarial examples are an effective method that can be leveraged to attack machine learning systems. The primary defensive method, adversarial training, is a simplistic approach when applied without optimization. In this paper we propose a novel method, evolutionary multi-objective optimization genetic adversarial training (EMO-GAT), that enables the development of a more robust model by performing evolutionary adversarial training using multiple types of adversarial examples. We demonstrate that EMO-GAT outperforms both traditional adversarial training and genetic adversarial training (GAT).
Are small swarms effective in PSO optimization tasks?

This manuscript studies the optimal size of a swarm in PSO-type algorithms. As the popularity of PSO algorithms has increased and they have been applied to an ever-increasing number of applications, some key aspects appear to be less than fully explored. One of the questions is what the optimal size of a swarm is. This is expressed alternatively as a question of how small a swarm can be before it fails to properly search for a solution and conversely whether a very large swarm can better search for a solution and still make efficient use of the computing resources required. In the present manuscript a pilot study is presented, that investigates a standard PSO algorithm and attempts to address the issue of optimizing the swarm size. The results obtained are analysed via statistical tests to determine their significance.
Black-Box Universal Adversarial Attack on Text Classifiers

Adversarial examples reveal the fragility of deep learning models. Recent studies have shown that deep learning models are also vulnerable to universal adversarial perturbations. When the input-agnostic sequence of words concatenated to any input instance in the data set, it fools the model to produce a specific prediction in [9] and [10]. Despite being highly successful, they often need to obtain the gradient information of the target model. However, under more realistic black box conditions, we can only manipulate the input and output of the target model, which brings great difficulties to the search for universal adversarial disturbances. Therefore, to explore whether universal adversarial attacks can be realized under black-box conditions, we study a universal adversarial perturbation search method based on optimization. We conducted exhaustive experiments to prove the effectiveness of our attack model by attacking the Bi-LSTM and BERT models on sentiment classification tasks.
StarGAT: Star-Shaped Hierarchical Graph Attentional Network for Heterogeneous Network Representation Learning

Many real-world graphs can be viewed as Heterogeneous Networks or Heterogeneous Information Networks (HINs) for that they comprise a diversity of node types and relation types. Due to the efficient representation ability of Graph Neural Network and the idea of random walk, many recent studies apply graph representation learning to HINs and achieve satisfactory results. However, these works either treat different node types in a metapath equally, which is inconsistent with the original graph semantic information for that different node types should have different statuses, or only consider the first-order (node-level) and second-order (metapath-level, a.k.a. link-level) information aggregation while ignoring the higher-order relations. To tackle these two problems, we propose a novel Star-Shaped Hierarchical Graph Attentional Network (StarGAT) model to boost representation learning in HINs. Specifically, we assume nodes in HINs can be categorized into a star-shaped structure including one center node type and a bunch of auxiliary node types in a specific task; and we encode node-level, link-level and motif-level attentions in a hierarchical manner to capture richer semantic information. Extensive experiments on three datasets illustrate the model effectiveness.
Biased Eavesdropping Particles: A Novel Bio-inspired Heterogeneous Particle Swarm Optimisation Algorithm

The present study proposes a new bio-inspired heterogeneous PSO (particle swarm optimisation) algorithm called BEPSO (biased eavesdropping PSO). The primary search behaviour of the BEPSO algorithm is inspired by eavesdropping behaviour observed in nature, and incorporates a cognitive bias that enables particles to control decisions on cooperation. The algorithm divides the swarm into two distinct particle groups competing to search for a resource. Particles recognise their group members as conspecifics and the other group as heterospecific. When a particle discovers a better position, a contact signal is emitted to attract surrounding conspecifics to the newly discovered position. In nature, multiple species living in the same environment commonly eavesdrop on contact calls originally unintended for them. This can help them to use less energy in searching for resources and potentially increase their fitness. Similarly, in this study, surrounding heterospecifics also eavesdrop on, and exploit, the signal calls originally intended for different conspecific particles. This signaller-recipient interaction experience builds positive or negative bias among particles over time, and particles' final cognitive bias is used as a decision mechanism to exploit signal information. The performance of the proposed algorithm was tested by conducting three distinct experiments on the CEC'17 and CEC'05 benchmark test suites at 30 and 50 dimensions. The results were compared against the results of 12 baseline metaheuristics and 12 state-of-the-art PSO variants. The proposed algorithm outperformed all 24 comparison algorithms in all experiments conducted.
Teaching Methods of Reading for the Creation of Questions －&amp;amp;lt;Reading Masterpiece&amp;amp;gt; Focused on Cases of Greek Tragedy Class

This study is aimed to reveal that the method used for the creation of questions by students taking a classic education class named &lt;Reading Masterpiece&gt; is important in two ways: 1) facilitating the difficulty with which students read classical literature, and 2) establishing individual perspectives when reading by oneself. The experience of voluntary reading is more important than reading a lot. The class focused on the process used by students to create questions regarding the texts they’re required to read, it is necessary for us to structure classes according to these levels in reading in order to raise the level of questions the students are able ask. To make secondary and tertiary reading possible at least in part, the class information, the details regarding the actual assignment of the class and a small group activity were given and conducted respectively according to the students’ reading levels.&lt;/br&gt;The procedure was carried out according to reading levels following the establishment of the goals for each work assigned. Even though students have their own unique perspecitves regarding the goals of every text assigned to them, we asked them to come up with their own perspectives in their approach to these texts. Furthermore, we had them focus on the types of questions pertaining to the first two works they were requried to read, and by pointing out the possibility of various interpretations regarding their next two works. Finally, we had them compare their work to the last two texts that they read.&lt;/br&gt;Of course, the process of creating questions is important in every work, but this study aimed at encouraging students to develop their own individual questions. This is because it is more important for instructors to provide an environment where in students can voluntarily utilize reading that encourages them to come up with their own perspectives rather than simply giving them a number of opportunities to read. For reading itself provides students the chance of engaging in tertiary reading. Students' presentations and discussions confirmed that the power of spontaneity builds up from the moment the learner recognizes that they have achieved it by themselves by asking questions and developing it into their own perspective.
End-to-end non-autoregressive fast text-to-speech

서론 TTS(text-to-speech)는 주어진 문자열을 음성으로 출력하는 모 든 시스템을 의미한다.Yarrington(2007)은 TTS를 세 종류로 구 별한다.Articulatory synthesis는 인간의 성도(vocal tract)를 모방 한 조음 모델에서 음성을 출력한다.Formant synthesis는 음소별로 formant를 조정해서 음성을 출력한다.Concatenative synthesis는 사전에 녹음된 음소를 결합해서 음성을 출력한다.근래에는 인 공신경망(artificial neural network)의 발전에 힘입어 각양각색의 인공신경망을 이용한 TTS가 등장하고 있다.보통의 인공신경망 TTS는 autoregressive하다는 특성을 가진 다. 순환신경망(recurrent neural network, RNN)이 사용되기 때문 이다.여기서 순환신경망은 시계열 데이터를 처리하는 인공신 경망이고
A Regional SAR Dark Spot Detection Method Based on GMTRJ Algorithm

Abstract Generalized Multiple‐Try Reversible Jump (GMTRJ) is a recent algorithm which makes it possible to improve the accurate tuning of the jump proposals in the Reversible Jump Markov Chain Monte Carlo algorithm. In this paper, the GMTRJ algorithm is combined the regular tessellation and the Bayesian paradigm to propose a regional synthetic aperture radar (SAR) dark spot detection method. First, the regular tessellation and the Bayesian paradigm are applied to build a regional SAR dark spot detection model; then the GMTRJ algorithm is designed to simulate from the detection model to obtain the optimal regional detection result. As the blocks may cross the boundaries between dark spot and ocean background regions, a refined operation is designed to improve the detection accuracy of boundaries. We present test results demonstrating the feasibility and advantages of the GMTRJ algorithm on simulating from the detection model, and illustrating the feasibility of the refined SAR dark spot detection operation on improving the detection accuracy of boundaries.
Automated Reasoning on Machine Learning Model of Legislative Election Prediction

Prediction models using machine learning have been utilized in various fields, including the general election prediction model. However, we still need more insight into the model result through explainable AI. To reason the result, in this in-use paper, here we compare two approaches: using ontology reasoner Protégé and Silas (a machine learning tool empowered with automated reasoning). Using the data set of the Indonesia legislative election in 2019, we build the prediction model, followed by extracting the formula from the decision tree then reasoning the model predicates. The result shows that to some extent we can have a better understanding of the reasonable result from the machine learning prediction model.