Reviewerly/exHarmony huggingface dataset:
Dynamic Monitoring and Assessment for Digital Transformation in Higher Education

The development of information technology applied to college institutions is experiencing rapid growth.Information technology can solve problems faced in educational institutions.Universities are increasing the use of information and digital technologies for improved service, data management, and accurate reporting.Monitoring and assessment are an essential part of college development.The parameters used in monitoring and assessment use a parameter on the strategic plan of the college.The comparative study of the strategic plan is targeted at two universities that have different characteristics, namely public universities and Islamic state universities.The method used in building dynamic monitoring and assessment uses machine learning.Parameters in monitoring and assessment include Tri Dharma Higher Education such as research, education, and community development.Based on these problems can be formulated implementation of dynamic monitoring and assessment for digital transformation in Higher Education as well as prototype models of dynamic monitoring and assessment based on machine learning.The results obtained show that parameter testing yields an average of 78%.This indicates that the level of accuracy using machine learning can implement dynamic monitoring and assessment in digital transformation at higher education.
Unbalanced data classification based on oversampling and integrated learning

A lot of unbalanced data exist in the real world, but most of traditional classification algorithms assume that the data is balanced and the misclassification cost is the same with all classes. Therefore, traditional classification algorithm should be modified. This paper proposed an improved unbalanced data classification algorithm: WSMOTEBoost which based on SMOTE and AdaBoost integrated learning. Add two weight improvements, in the level of data processing, considering the SMOTE adding boundary and the central sample. The sampling weight of fundamental sample was comprehensively determined using the Euclidean distance and the weight of iterative sample in AdaBoost, so that the samples with high value will sampled more. In terms of classification algorithm, cost-sensitive learning is introduced, sensitive factors are used to measure the classification cost, and the error rate of specific classes is used to improve the original Adaboost sample weight iteration, it made AdaBoost more concerned with the minority class that were misclassified. Experiments on three open unbalanced data sets of UCI show that the proposed method has more efficiency compared to AdaBoost and SMOTE Boost algorithms in F1 value and AUC. It is more suitable for unbalanced data classification.
Creativity-oriented software development

This Article describes principles and a practice to support individual creative activity in software development. It shows how significant the visual means of development are, by which any insufficient information is supplemented, and an impact on the person’s short term memory is reduced. The framework conditions of a dominant idea of a creative human factor are determined, namely, personal experience, comfort and availability heuristic. For the purpose of localization in a single visual data context, a creative and contextual form is proposed that serves for various possible information elements to be placed therein and further recorded, and this gives rise to a creative insight of another solution. An example of how such software system is implemented, and a sample for an algorithm developed by it are presented. A description is given for an Eureka software model that supports a developer’s creative process activity. The scientific novelty of this Article is methodological principles and a practice to support an individual creativity comfort in software development. In circumstances where data losses are neutralized, and thus the total volume of appropriate information is increased, the reduction of an impact on the developer’s short term memory is achieved, which leads steadfastly to acceleration of solutions and to improvement of their quality.
Reducing Social Media Users’ Biases to Predict the Outcome of Australian Federal Election 2019

Several online social networking sites (OSNs) are being used as a medium of expressing any ideas, opinions, thoughts towards any regular or special issues and also for support or even oppose any social or political matters at the same time. At the age of this modern technology, in any country, people would like to post their views regarding any political party during the election period on such emerging OSNs in order to demonstrate their stands upon them. In this paper, we incorporated the tweets relevant to the Australian federal Election 2019, with a view to serve our primary purpose of predicting the outcome of it. We aggregated two efficacious techniques to extract the information from a large Twitter dataset to count a virtual support for each corresponding political group and propose an approach of reducing users' biases in OSNs to predict outcome of the election more efficiently. Our investigation finds close relevance with the original results of the election published by the Australian Electoral Commission.
Image Generation Containing Invisible QR Code by Convolutional Neural Network

The authors aim to use CNN to embed and restore arbitrary information in images. To achieve this goal, we propose a model consisting of two CNNs with different roles. In the proposed method, it is used as an information medium for embedding a QR code. The QR code error correction function is expected to restore the embedded information without error. Previous research has shown that embedding a QR code in a sharp image does not restore correctly. In this paper, we modified the model structure to address this issue. In the experiment, we trained the proposed CNN model. Using the model after training, I embedded the QR code image in the image and restored it. As a result, we were able to embed the QR code image without degrading the quality of the input image. Blur the image with the embedded QR code. Then, there is a problem that the embedded QR code cannot be restored. We will solve this problem in the future.
Performance Analysis of Dynamic Bijective S-BOX Construction Algorithms

Dynamic substitution boxes (S-boxes) are a vital building block of many cryptosystems. The general process of the dynamic construction of a bijective S-box consists of two main components: a random number generator, and a construction algorithm. In this paper, we study the effect of the choice of the construction algorithm on various aspects of the dynamic S-box, including its randomness and it cryptographic strength. We identify the different algorithms found in the literature and present a comparative analysis of their computational complexity and effective key space. Moreover, we study the cryptographic characteristics of the resulting S-boxes. We conclude with some guidelines to help cipher designers choose the S-box construction method most suited for their design objectives.
Thinking Fast and Slow: A CBR Perspective

In a path-breaking work, Kahneman characterized human cognition as a result of two modes of operation, Fast Thinking and Slow Thinking. Fast thinking involves quick, intuitive decision making and slow thinking is deliberative conscious reasoning. In this paper, for the first time, we draw parallels between this dichotomous model of human cognition and decision making in Case-based Reasoning (CBR). We observe that fast thinking can be operationalized computationally as the fast decision making by a trained machine learning model, or a parsimonious CBR system that uses few attributes. On the other hand, a full-fledged CBR system may be seen as similar to the slow thinking process. We operationalize such computational models of fast and slow thinking and switching strategies, as Models 1 and 2. Further, we explore the adaptation process in CBR as a slow thinking manifestation, leading to Model 3. Through an extensive set of experiments on real-world datasets, we show that such realizations of fast and slow thinking are useful in practice, leading to improved accuracies in decision-making tasks.
Effect of empowering leadership on knowledge sharing: mediating roles of psychological empowerment and psychological capital

Current study aims to find out the effect of empowering leadership on knowledge sharing behaviour directly and through the mediating effects of psychological empowerment and psychological capital. Dyadic data were collected from supervisor-subordinate dyads. The available responses for analysis, after screening, were 223. The respondents for this research hailed from diverse sectors such as leather factories, sports goods industry, and surgical goods manufactures. Data were analysed by applying SEM in AMOS. Results confirmed our proposed model. Apart from the direct linkage, the mediating effects were also validated. These findings advance literature in the streams of empowering leadership, knowledge sharing, psychological capital, and psychological empowerment. Important implications, theoretical and practical, have also been discussed at the end.
Disentanglement of Human Facial Features and the Inspection of Feature Extractor’s Quality Based on TL-GAN

Abstract Recent research on the Generated Adversarial Network models has made great progress in the task of generating images on human demand. Among them, Transparent Latent-space GAN tries to solve the problem by analyzing the latent space and finding the feature axes inside it. This model views the problem from a novel perspective, and it is easy to be carried out. However, more thorough and detailed experiments have shown that the original method on axes disentanglement of the model has certain drawbacks, which may lead to ethical problems, meaning that the model may pick up some pre-existing prejudice of racial or gender discrimination. This paper puts forward a new training mode aiming at reducing the bias existing in the generated images and simultaneously managing the disentanglement. Also, a more thorough examination of the model was conducted and it is found that the architecture of the model may be generalized to some universal functions. Among them, the quality evaluation of the feature extractor is the most practical and useful one. This finding, in turn, may be helpful for distinguishing a more accurate, effective, and robust feature extractor, thus improving the performance of the TL-GAN model in the first place.
Adaptive Noise Injection for Training Stochastic Student Networks from Deterministic Teachers

Adversarial attacks have been a prevalent problem causing misclassification in machine learning models, with stochasticity being a promising direction towards greater robustness. However, stochastic networks frequently underperform compared to deterministic deep networks. In this work, we present a conceptually clear adaptive noise injection mechanism in combination with teacher-initialisation, which adjusts its degree of randomness dynamically through the computation of mini-batch statistics. This mechanism is embedded within a simple framework to obtain stochastic networks from existing deterministic networks. Our experiments show that our method is able to outperform prior baselines under white-box settings, exemplified through CIFAR-10 and CIFAR-100. Following which, we perform in-depth analysis on varying different components of training with our approach on the effects of robustness and accuracy, through the study of the evolution of decision boundary and trend curves of clean accuracy/attack success over differing degrees of stochasticity. We also shed light on the effects of adversarial training on a pre-trained network, through the lens of decision boundaries.
Blockchain and Internet of Things in Higher Education

Building the innovative blockchain-based architecture across the Internet of Things (IoT) platform for the education system could be an enticing mechanism to boost communication efficiency within the 5 G network. Wireless networking would have been the main research area allowing people to communicate without using the wires. It was established at the start of the Internet by retrieving the web pages to connect from one computer to another computer Moreover, high-speed, intelligent, powerful networks with numerous contemporary technologies, such as low power consumption, and so on, appear to be available in today's world to connect among each other. The extension of fog features on physical things under IoT is allowed in this situation. One of the complex tasks throughout the area of mobile communications would be to design a new virtualization framework based on blockchain across the Internet of Things architecture. The goal of this research is to connect a new study for an educational system that contains Blockchain to the internet of things or keeping things cryptographically secure on the internet. This research combines with its improved blockchain and IoT to create an efficient interaction system between students, teachers, employers, developers, facilitators and accreditors on the Internet. This specified framework is detailed research's great estimation.
Research on Campus Attendance System Based on Face Recognition and Trajectory Tracking

Abstract The traditional Morning jog attendance in colleges and universities is limited to manual supervision by swiping card attendance, which is prone to queuing and sign-in on behalf of others. In addition, the lack of quantitative records of exercise data makes supervision difficult. Location-based attendance systems represented by Dingding time attendance, or simple face recognition attendance systems cannot achieve accurate attendance. The campus happy running system based on face recognition and trajectory tracking innovatively combines face recognition and trajectory tracking, and students achieve accurate attendance certification within the designated “geo-fence”. Practice shows that the system can meet the special needs of colleges and universities for Morning jog attendance and data recording, and it effectively improves the accuracy of identity verification.
RCNX: Residual Capsule NeXt

Contrary to the popular Convolutional Neural Network (CNN), which depends on the shift-invariance in the image, Capsule Networks depends on hierarchical model relations. This aspect of Capsule Networks keeps them in the machine learning domain despite their enormous size with only comparable accuracy to the CNNs. Capsules utilize an intricate algorithm to create a route by agreement, leading to their size and uniqueness. Recent developments in Capsule Networks have contributed to mitigating the problem of size and improving accuracy. We focus on modifying one of the Capsule Network, Residual Capsule Network (RCN), to a comparable size to modern CNNs, thus restating Capsule Network's importance. In this paper, Residual Capsule NeXt (RCNX) is proposed as an effective and more advanced version of RCN with a size of 1.5M parameters and an unprecedented improvement in the network's accuracy on the CIFAR-10 dataset to 89.3%. This accuracy and size exceed the famous embedded CNN model MobileNetV3.
Corago in LOD. The debut of an Opera repository into the Linked Data arena

The paper examines the adoption of the Semantic Web (SW) technologies and Linked Data (LD) principles to manage a knowledge base about opera. The Corago repository collects historical data and documentation about opera works, performances and librettos from the 16th to the 20th century. We experimented the use of semantic technologies to manage the repository’s knowledge catalogued following the Functional Requirements for Bibliographic Records (FRBR) relational model. Cultural Heritage Knowledge Bases (CHKB) as Corago could leverage SW and LD to overcome proprietary models and to introduce new information to better satisfy user’s requirements. Two well-established reference ontologies as CIDOC Conceptual Reference Model (CIDOC CRM) and FRBR Object Oriented (FRBRoo) are adopted to transpose contents form the legacy conceptual model to RDF. Through the process, we observed that formal semantics allow, not only to adequately represent the opera domain, but also enable to define the way information is being presented to users. This led to the definition of “reception pathways” which become themselves part of the knowledge about opera within the KB. This novel semantic approach is introduced with the Corago Semantic Model (Corago SM), a domain ontology dedicated to functional representation of opera’s historical data. Advancements have been tested with an experimental system and assessed through a questionnaire submitted to a panel of users.
KOLABORASI MENULIS KREATIF BUKU BACAAN ANAK DALAM MENGEMBANGKAN INDUSTRI KREATIF PADA KOMUNITAS LITERASI WADAS KELIR PURWOKERTO

In this era, writing is not enough to be done expressively with personal spontaneity, but need to be done collaboratively by considering aspects of the desired goals. One of them is when writing is done to fulfill economic goals as the basis of a community's creative industry. With the concept of a creative industry whose estuary is to produce economic products made from creative ideas, writing in Wadas Kelir Literacy Community (KLWK) is carried out with a planned collaborative working model based on creative industry standards of economic value. From here, the results of research that have been carried out are: first, writing collaboration begins with market research studies in an effort to identify trends in children's books that are being sought after by publishers and readers. Second, the formulation of themes and ideas is carried out in the discussion space and cooperation for the division of writing work. Third, the results of the writing work are reviewed and edited by a team of experts to identify the strengths and the weaknesses. Fourth, the book manuscript offer to publishers is carried out by a separate management who continues to carry out partnership work with publishers. Fifth, involved in efforts to distribute book sales through online stores that create trust in the community of publishers.
CodeShovel: A Reusable and Available Tool for Extracting Source Code Histories

Being able to accurately understand how source code evolved is fundamentally important for both software engineers and researchers. Our ICSE 2021 Research Paper CodeShovel: Constructing Method-Level Source Code Histories describes a novel approach for quickly uncovering these method histories. The approach, codified in the CodeShovel tool, is available for researchers and practitioners alike to use and extend. It is available both as a public web service that can be used interactively or through a REST API and as a stand-alone Java component. This document details how to install and use CodeShovel, although all pertinent details are available online enabling CodeShovel to be reused as desired.
Big Data Retrieval Using Locality-Sensitive Hashing with Document-Based NoSQL Database

A locality-sensitive hashing (LSH) method in the document-based NoSQL database is proposed for enhancing the ability of arbitrary reads over the previous methodologies. The proposed hash index improves efficiency by reducing the amount of accessing data for search queries by creating buckets based on hyperplanes. The LSH hashes the input data where similar items with high probability maps to the same bucket. They attempt to decrease the volume of candidate data objects matched when reducing the missed nearest neighbors. The data space is divided with randomly chosen hyperplanes to decrease the volume of candidate objects. The values which are nearer to the boundaries (adjacent to the two sides of the hyperplane) are considered. The bucket label’s string length is equivalent to the amount of used hyperplanes. The effect of LSH for bucket size balancing and analysis of the non-indexed, hash index, and global-indexed dataset on MongoDB depicts the pre-eminence of the presented hash index.
Speech Recognition for Endangered and Extinct Samoyedic languages

Our study presents a series of experiments on speech recognition with endangered and extinct Samoyedic languages, spoken in Northern and Southern Siberia. To best of our knowledge, this is the first time a functional ASR system is built for an extinct language. We achieve with Kamas language a Label Error Rate of 15\%, and conclude through careful error analysis that this quality is already very useful as a starting point for refined human transcriptions. Our results with related Nganasan language are more modest, with best model having the error rate of 33\%. We show, however, through experiments where Kamas training data is enlarged incrementally, that Nganasan results are in line with what is expected under low-resource circumstances of the language. Based on this, we provide recommendations for scenarios in which further language documentation or archive processing activities could benefit from modern ASR technology. All training data and processing scripts haven been published on Zenodo with clear licences to ensure further work in this important topic.
A Brief Study on Quantum Computing

In the twentieth century, quantum physics is one of the most successful approaches in scientific progress. To theorize the concept of quantum computing Max Planck's idea of the existence of energy in individual units like matter was used. Since then, the idea of manufacturing quantum computers for everyday use is becoming more plausible with technological advances in quantum theory. A classical computer carries out logical operations using a bit which is either 1 or 0. In contrast to a classical computer, a quantum computer works with a quantum bit or qubit, which is not limited to two states only. Qubits can exist in a superposition of the two states creating parallelism. Qubit represents the state of atoms, ions, photons or electrons. These qubits which work together with a control device act as a computer memory.Quantum computers have the potential to become million times more potent than present-day supercomputers due to the presence of these multiple states simultaneously. Concepts and ideas of quantum computing have been demonstrated by different methods like Ion Trap, Quantum Dot methods etc. However, the actual realization of such a superior system is still in the future. A strategy must be formulated to maintain decoherence and other potential sources of error at a permissible level.Astonishingly, quantum computing took a long time to take off, even though the physicists have known about the world of subatomic particles. Even so, it took computer scientists another half-century to consider harnessing quantum effects for computation. Also, it was later discovered that quantum computers could solve quantum mechanical many-body problems that are impractical to solve on a classical computer. The foundations of the subject of quantum computation may have become well-published, but the knowledge is still growing. Hence here is a research paper based on the study of quantum computation.
An Error Analysis of Active and Passive Voice Towards English Text of University Students in South Jakarta

This research is aimed at analyzing univeristy students’ errors in using Passive Voice. Specifically, it is aimed for obtaining and identifying the common error in using passive voice and finding out what factors which trigger the students make such errors.  The data sources of this research were 38 students of fifth semester university students of in South Jakarta which were taken by random sampling. The researcher used the descriptive analysis method in this research to describe students’ errors and analyzed the data by using formula: P =  The data were taken from the test.  The findings showed that there are 229 errors made by students. The common error made by students in using Passive Voice was Misformation, which consists of 152 errors or 66.3%. According to Brown’s theory, it was interpreted that, intralingual transfer, interlingual transfer, context of learning and communicative strategies are causes of those errors.