Reviewerly/exHarmony huggingface dataset:
Semantic composition of cloud services

As cloud services are reaching a considerable maturity level, several academics and researchers are exploiting this paradigm to benefit from its advantages. More specifically, with SaaS services' advent, a tremendous number of enterprises rely on their composition as an alternative to their Information Technology infrastructure. While composing SaaS services, achieving the Business Process (BP) goals and objectives through this cloud model is becoming easier and even beneficial; composing multiple SaaS services to support the BP execution is not a trivial practice regarding the complex nature of the BP. Indeed, BP is composed of a set of activities with various functional requirements, data that should be exchanged between each SaaS service, and roles imposing that only authorized actors can perform a specific activity. This paper proposes a comprehensive framework for business process-based SaaS composition that covers the semantic matching between BP activities and SaaS labels and the allocation of BP activities to SaaS services using a genetic algorithm while considering the data and the activities access control issues.
Initialization Method of Batch Uniformization Auto Encoder by Principal Component Analysis

A batch uniformization autoencoder (BU-AE) often performs poorly in anomaly detection problems if the initial weights of the network are determined by a random number-based method. This paper proposes a principal component initialization method with anomalous data (PCI-AD) for BU-AE, where the initial weights are obtained by principal component analysis; the PCI-AD is trained to reduce the reconstruction error of normal data and increase that of the anomalous data. Since these weights are equivalent in concept to the objective function of BU-AE, BU-AE with PCI-AD provides stable performance. To demonstrate the effectiveness of PCI-AD, the proposed method was compared through several computational experiments with He's initialization, which is a random number-based initialization method. In the experiment using CIFAR-10, it was confirmed that the use of PCI-AD improved the value of AUC by up to 17% compared to He's initialization.
A BTM-Based Adaptive Objectionable Short Text Filtering Framework

Many methods are available for objectionable text filtering, such as URL-based filtering, keyword-based filtering, and intelligence-based analysis filtering approaches. URL-based filtering cannot filter the contents of objectionable short text. Keyword-based filtering faces the overblocking issue. Intelligence-based analysis filtering is inefficient and ineffective when filtering objectionable short text. In this paper, a biterm topic modelling- (BTM-) based adaptive objectionable short text filtering framework is proposed. We propose a feature extraction algorithm for objectionable short text and establish a sensitive word feature dataset using the descriptions of applications on the Internet. Then, we construct a judgment standard to automatically select the <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>K</mi> </math> value of the BTM topic model that can induce self-adaptation. The feature dataset constructed in this paper can effectively reflect the characteristics of objectionable short text. The proposed filtering framework can effectively identify objectionable short text and has a higher filtering rate than other approaches.
Sustainability Accounting, Sustainability Reporting, and Investor Response: An Empirical Study in a Sample of Iraqi Commercial Banks

In recent years, sustainability in commercial banks has become one of the performance indicators. Sustainability currently contributes to preserving the environment, achieving social welfare, developing the economy, and activating governance. Therefore, this research investigates whether the Iraqi commercial banks listed on the Iraq Stock Exchange by adopting the sustainability accounting standard contribute to improving disclosure, protecting the rights of investors and society as a whole. The Iraqi Middle East Investment Bank (IMEIB) was chosen as a case study for the research. The research obtained its data from the annual audited financial statements of the IMEIB over the period (2015-2016). The study reached some critical conclusions. The most important was that compliance with sustainability accounting standards by the commercial banks listed on the ISE makes it easier to know the indicators related to sustainability aspects by management, investors, or the public. Moreover, some commercial banks operate through their commitment to international and local sustainability standards to maximize their income. In contrast, other banks integrate their regular activities with social, economic, and environmental goals to contribute to achieving sustainable development, attaining social welfare, developing the economy, and protecting the environment.
Periocular features as a window on cognitive processing: from memory to understanding the semantic complexity of an image

Although it is well understood that changes in periocular features are linked to our brain activity, there is a paucity of research to analyze them in order to classify various cognitive processes. The present study investigates the effects on the periocular region of visual stimuli eliciting two different mental tasks: visual memory recall and understanding the semantic complexity of an image. The aim is to understand whether a subject is looking at an image with clear semantic content or not, or when he already knows the image that is shown to him. Based on these observations, the paper presents a study with the aim of demonstrating that the information that can be extracted from blinks, pupils and, gaze movements can potentially be used to classify people with respect to these two cognitive processes. Because there is a dearth of specialised research in this field, the encouraging results achieved in this study imply the necessity to generate specific datasets for this purpose.
Investigating Neural Network Architectures, Techniques, and Datasets for Autonomous Navigation in Simulation

Neural networks (NNs) are becoming an increasingly important part of mobile robot control systems. Compared with traditional methods, NNs (and other data-driven techniques) produce comparable-if not better-results while requiring less engineering knowhow. Training NNs, however, still requires exploration of a significant number of architectural, optimization, and evaluation options. In this study, we build a simulation environment, generate three image datasets using distinct techniques, train 652 models (including replicates) using a variety of architectures and paradigms (e.g., classification, regression, etc.), and evaluate the navigation ability of the model in simulation. Our goal is to explore a large number of model possibilities so that we can select the most promising for future study with a physical device. Training datasets leading to the best performing models were those that included a significant amount of noise from seemingly inefficient actions. The most promising models explicitly incorporated “memory” wherein previous actions were included as an input in the next step. Such models performed as good or better than conventional convolutional NNs, recurrent NNs, and custom architectures including two camera frames. Although trained models perform well in an environment matching the distribution of the training dataset, they fail when the simulation environment is altered in a seemingly insignificant manner. In robotics research it is often taken for granted that a model with good validation characteristics will perform well on the underlying task, but the results presented here show that there can often be a loose relationship between validation metrics and performance.
Patterns for Assignment and Passing Objects Between Contexts in Programming Languages

The paper analyses different types of assignment and passing objects to and from functions applied in programming languages. The considered kinds of assignment are described as patterns. The patterns are identified according to their goal and the opportunities they provide to the programmer. The described patterns are the Value Assignment, the Referential Assignment, the Partial Assignment, Destructuring, and Unboxing. The known uses of the patterns describe the relationship between them and the low-level types directly supported by programming languages. The patterns are intended for programming language designers and people interested in how similar approaches are implemented in different languages.
An Autobiographical Case Study on University’s Writing Class Operated in a Synchronous Online Manner －Focusing on the Perception and Reflection of Instructors through the Subject of Writing and Classical Reading at the University of Suwon

The purpose of this study is to analyze autobiographical cases in which the university of Suwon's essential liberal arts subject called “Writing and Classical Reading,” was conducted in a synchronous online manner. Through this process, the instructors shared the experiences, perceptions, and reflections that they discovered while practicing these writing classes in the changed educational environment. In addition, the instructors discussed the direction of synchronous online writing classes should take and the new issues required of them within this new environment.&lt;/br&gt;Since the outbreak of COVID-19, a series of new studies have accumulated around the keyword “online class” from 2020 to the present, and these new discourses on education have led to various opinions and suggestions on how educational subjects should come up with educational countermeasures amid this pandemic. As a result of synthesizing these new data, I confirmed that instructors were paying attention to the difficulties of ‘interaction’ in the online environment. Therefore, in this study, the instructor's perceptions, reflections, and new practices experienced while operating the class were presented, with a focus on the changed educational environment and the resulting interaction patterns between instructors and learners.&lt;/br&gt;As a result of this study, I was able to divide the instructor's perception and reflection into interactions in the technical environment, interactions pertaining to the learner's psychological aspect, and interactions regarding the instructor's practical aspect. In each of these interactions, the new practices attempted by the instructor could be summarized as “in-depth classroom practices,” practices for high context environments, “vivid voices” and relational communication environments, and “shared identities” and practices for the environment of practical knowledge.&lt;/br&gt;In the future, many researchers will need to respond quickly to the changing trends of the times and share their experiences and opinions with other instructors involved in various educational subjects. At the same time, instructors will need to continue to implement new educational practices in their classrooms. Thus, I hope that this research paper contributes to this process in a beneficial and significant way.
School Meals Based on the Pedagogical Model: “Cool Canteen - Canteen Is Cool!”

The chapter analyses the modern approaches to the organization of school meal based on the food pedagogy model. We proved its effectiveness for achieving sustainable development goals using the results of selection, analysis, logical generalization of information presented in the online resources Elsevier, PubMed, ResearchGate, eLIBRARY.RU. It is confirmed that the implementation of the model in practice requires special measures both at the institutional level and at the level of educational institutions themselves. Collaboration and interdisciplinary approach are the base for the development of those measures.
Research on the Application of Three-Dimensional Laser Scanner Method for Whole Life Cycle Capacity Measurement and Safety Monitoring of Storage Tanks

The use of large storage tanks to store oil and products is the most used oil storage method at home and abroad, while large storage tanks are also the main measurement apparatus for trade settlement of petrochemical products. In order to solve the problem of capacity measurement and safety inspection for the whole life cycle of storage tanks, this paper proposes a method of capacity measurement and deformation monitoring for the whole life cycle based on 3D laser scanning technology, which breaks through the previous problem of low data collection and difficulty in truly reflecting the actual storage tanks. Tests show that the use of real-world replication technology can accurately measure the whole-life capacity of storage tanks and tank deformation, improving the efficiency of inspection and ensuring the safety of storage tank facilities.
Curriculum Learning for Age Estimation from Brain MRI

Abstract Age estimation from brain MRI has proved to be considerably helpful in early diagnosis of diseases such as Alzheimer’s and Parkinson’s. In this study, curriculum learning effect on age estimation models was measured using a brain MRI dataset consisting of normal and anomaly data. Three different strategies were selected and compared using 3D Convolutional Neural Networks as the Deep Learning architecture. The strategies were as follows: (1) model training performed only on normal data, (2) model training performed on the entire dataset, (3) model training performed on normal data first and then further training on the entire dataset as per curriculum learning. The results showed that curriculum learning improved results by 20 % compared to traditional training strategies. These results suggested that in age estimation tasks datasets consisting of anomaly data could also be utilized to improve performance.
Students’ Perception of Media Literacy through University Liberal Arts －Focused on Cases of “Media Literacy and Creative Communication”

This article aims to examine students’ perception of media literacy through university liberal arts, focusing on cases involving ‘media literacy &amp; creative communication’, which is defining the direction of media literacy education. In order to achieve the object of this study, researchers examined the case of media literacy education already being implemented in university general education.&lt;/br&gt;Based on the results of this study, the following may be noted: First, this study presented the media literacy curriculum systematically, and an analysis was made based on various theoretical studies. Secondly, the student’s perception of media literacy in the beginning of the lecture was checked through a Preliminary survey. Most students were not fully aware of the concept of media literacy, had low understanding of media, and were lacking in their ability to cope with problems of digital media, such as “fake news” and the practice of the “deep fake.” Thirdly, through the focus group interview(FGI) we learned that students’ attitude and perceptions were changed to an enhanced extent after 15 weeks of being enrolled in the curriculum. Most students’ understanding, analytical skills, and critical ability of media seem to have grown significantly. Finally, this study analyzed the effects found among students from their studies after being enrolled in the curriculum for 15 weeks, and presented plans to activate ‘media literacy and creative communication’ among the students as a effective curriculum model of liberal arts. Through this it is hoped that students can develop core competencies of media, such as an ability to properly approach media, and to improve their critical understanding capabilities, as well as their socio-cultural participation capabilities.
ARUSH:Automated Runtime Universal Scanner for Hybrid Cloud computing

With the shift of leading companies to the cloud, so comes the risk of data leaks and breaches because of many possible misconfigurations in the setup of various cloud computing environments. Though many solutions have come up, each is platform-specific, and none of them targets multiple or hybrid cloud platforms all together. This work proposes one such solution where three major public cloud platforms are supported, and the main focus is on the storage services provided by them. Since there is no such open-source solution that supports multiple cloud platforms, the program in this work is supposed to provide such a solution. Each cloud platform provides native SDK libraries for issuing API calls to the platform. These API calls aim to query the currently employed configuration for the storage services at the very least if not change. The solution can combine all the libraries into a single driver program. The program can facilitate security management of storage accounts across multiple as well hybrid cloud services and opens possibilities for automation and routine security and audit checks.
Savior: An Interactive Game Device for Environmental Protection

This paper presents Savior, an interactive device based on the thinking on the environmental problems, which people can interact with through their behavior. Pictures of the world map are displayed on the electronic screen, this screen is supposed to show both terrible scenes and beautiful scenes of the earth. 3D printed models are embedded with different Arduino sensors. This interactive installation is also an interesting game installation which is attractive to people and it can involve people in the interactive game. A preliminary evaluation is conducted and indicates that Savior raised people's awareness of environmental protection. Meanwhile, it has the potential of guiding people to protect the environment in the real life and change nowadays’ negative living condition on earth. In this process, users are like the savior of the earth, they changed our earth from a terrible condition to a beautiful and prosperous world. Our contribution in this project is that we raised people's awareness of environmental protection by visualizing data of environmental problems and combine games with art installations.
Pelaksanaan Eksekusi Benda Bergerak Yang Tidak Dibebani Jaminan Fidusia Di Perkoperasian

Guarantee execution is one of the ways when the debtor is negligent in carrying out his obligations which are carried out in accordance with applicable legal procedures. Furthermore, two basic problems are formulated to be studied, namely: 1). How is the execution of movable objects that are not burdened with Fiduciary Guarantees? And 2). What legal efforts can be made by the creditor if the execution of movable objects that are not burdened? this section also describes the purpose and benefits of writing and the originality of research as the work of the real writer. This study uses an empirical juridical approach, data obtained through library research and direct research in the field. Based on the results of the study, it can be concluded that. Judging from the law enforcement theory, the execution of collateral objects with fiduciary guarantees at the Cooperative, namely when the debtor is declared default, the cooperative will seize the object of collateral, takeover or disbursement of collateral objects under the hand, and auction the guarantee object based on the Selling Power. And the creditor's legal efforts according to the theory of legal protection cannot be implemented, namely the cooperative as the creditor will take a non-litigation path such as negotiation and mediation. And if the non-litigation path does not produce results, the cooperative will take a litigation route, namely to file a lawsuit to the court on the basis of a default made by the debtor.
Analytical Study on the Role of Scale Factor Parameter of Differential Evolution Algorithm on Its Convergence Nature

Differential evolution (DE), a global optimization algorithm, is the latest addition to the repository of evolutionary algorithm (EA). In spite of its potential in solving wide range of optimization problems, researchers are continuously working on its further improvement. The three control parameters of DE are population size (NP), mutation scale factor (F), and crossover rate (Cr). Analyzing the impact of these control parameters in DE's performance and their control mechanisms is the popular research directions in DE's literature. The objective of this paper is to observe and analyze the performance of DE algorithm for different values of one of its parameters, mutation scale factor (F), and to present the empirical validations on the superiority of a parameter adaptive mechanism of F, on an image segmentation problem. A detailed survey of DE literature pertaining to its parameter study and parameter setting is presented, and the research gaps are identified. Then, extending the study reported by Dhanalakshmy et al. (Dhanalakshmy D.M., Jeyakumar G., Shunmuga Velayutham C: Analytical Study and Empirical Validations on the Impact of Scale Factor Parameter of Differential Evolution Algorithm. In proceedings of International Conference on Pattern Recognition and Machine Intelligence -Lecture Notes in Computer Science vol. 11941, pp 328–336, (2019).), this paper presents empirical evidences to demonstrate the impact of F on DE's convergence nature. The observations and inferences are presented with categorical analysis based on different values of F, using a well-formulated experimental setup with 14 benchmarking functions and 8 variants of DE. The performance measurements were kept the same as used in (Dhanalakshmy D.M., Jeyakumar G., Shunmuga Velayutham C: Analytical Study and Empirical Validations on the Impact of Scale Factor Parameter of Differential Evolution Algorithm. In proceedings of International Conference on Pattern Recognition and Machine Intelligence -Lecture Notes in Computer Science vol. 11941, pp. 328–336, 2019). It is followed by solving the image segmentation problem with classical DE algorithm and the DE algorithm with adaptive F (DE_aF) (Dhanya M Dhanalakshmy, M.S. Akhila, C.R. Vidhya, G. Jeyakumar: Improving the Search Efficiency of Differential Evolution Algorithm by Population Diversity Analysis and Adaptation of Mutation Step Sizes. International Journal on Advanced Intelligence Paradigms, 15(2):119–145, 2020) and comparing their performances.
Multi-task Transfer with Practice

Adapting feedback-driven deep reinforcement learning (DRL) algorithms to real-world problems requires developing robust systems that balance generalization and specialization. DRL algorithms powered by deep neural network function approximation tend to over-fit and perform poorly in new situations. Multi-task learning is a popular approach to reduce over-fitting by increasing input diversity, which in turn improves generalization capabilities. However, optimizing for multiple tasks often leads to distraction and performance oscillation. In this work, transfer learning paradigm Practice is introduced as an auxiliary task to stabilize distributed multi-task learning and enhance generalization. Experimental results demonstrate that the DRL algorithm supplemented with the state dynamics information produced by Practice improves performance.
Word Sense Disambiguation for Lexicon-based Sentiment Analysis in Hindi

The amount of data from users in Hindi language is tremendously increasing on social media, blogs, online forums due to which Sentiment analysis of Indian languages has turned out to be a predominant research area. Lexicon based analysis is one of the techniques that can be used for analyzing sentiments. While using the lexicon-based sentiment analysis, ambiguous words could be an issue. This paper proposes a graph based Lesk approach to handle the word sense disambiguation issue and tries to enhance and improve the lexicon approach. Experiments performed to evaluate the performance of the proposed algorithm shows that the performance of lexicon-based sentiment analysis is increased significantly by using the graph based Lesk approach.