Reviewerly/exHarmony huggingface dataset:
Modern entrepreneurial university: ontic grounds and political risks

Discussions around the entrepreneurial "university of excellence" that replaced the "university of modernity" usually touch on one essential aspect: the need to "preserve" in a commercial firm, such as a modern university, those "remnants" of liberal education that are considered socially valuable ("public good"), politically significant (education of a citizen), necessary in theoretical (the specific form of knowledge and the peculiarities of teaching it) and methodological (the importance of philosophy as a methodology of sciences, in addition, the only one providing integration as interdisciplinarity) attitude. The article analyzes the arguments put forward by Lyotard in favor of the statement that a modern university in a "postmodern situation" is no longer capable of being a "protected place" (Collini) for knowledge: the legitimization of knowledge now has different sources and procedures than knowledge in a classical modern university. The main methods of politicization are studied (U. Bek) knowledge and university, undertaken in order to provide humanitarian knowledge and education a place in an entrepreneurial university. It is concluded that criticism of the current state of humanitarian knowledge and education does not matter either for university policy or for the public discourse of the university in market democracies.
A Massive Data Retrieval Method for Power Information Collection Systems

With the development of smart grid and big data, massive data retrieval has received intensive attentions by practitioners and developers, especially for its using in power information collection applications. It is a tricky task for improving the retrieval efficiency of massive data. The main big data solutions are likely to store the data in Hbase, which is difficult to improve the performance when querying small data from a large volume of data. Existing methods for this problem are inclined to use phoenix to query Hbase, but they exhibit inefficient under multiple table joining. In this paper, to sovle the quick retrieval for massive data in power information collection systems, we present a method via using Elasticsearch and Hbase to augment the performance of massive data retrieval. Specifically, we store the Rowkey and index information in elasticsearch to help accelerate the querying of Hbase. Then an experiment in real use case is used to verify the effectiveness of presented method.
PEDAGOGICAL PROJECT FOR THE DEVELOPMENT OF CREATIVE THINKING OF YOUNGER SCHOOLCHILDREN IN MATHEMATICS LESSONS THROUGH THE USE OF PROBLEM SITUATIONS

The problem of developing creative thinking in younger schoolchildren encourages many researchers to look for more effective ways to solve it. In the article, the authors present the development of a pedagogical project for the development of creative thinking of students in primary school by using problem situations in educational activities. The effectiveness of the pedagogical project is confirmed by the results of the experiment. The pedagogical project proposed in the article can be used in the organization of the educational process aimed at the development of creative thinking of younger schoolchildren in mathematics lessons.
Securely and Efficiently Nearest Neighbor Query Scheme Based on Additive Secret Sharing

With the wide application of machine learning algorithms in medical diagnosis, we gradually confronted the problem of computing and storing large-scale data. Outsourcing cloud computing has become the most cost-effective option to address these challenges. However, privacy and security issues have always existed in outsourced computing. Therefore, in this article, we propose an efficient index nearest neighbor query scheme based on Secret Sharing (SS) and Secure Multi-Party Computation (MPC) with the dual-cloud model architecture. For secure and efficient queries, we have designed a comprehensive set of secure index generation algorithms and secure index query algorithms. The cloud server creates indexes for the outsourced data and saves them through index generation algorithms in the offline phase, and uses index query algorithms to complete secure and efficient nearest neighbor query tasks in the online phase where users participate. Security analysis proves that our scheme protects the security of outsourced data and the privacy of query data. Simulation results on real datasets also demonstrate that the proposed scheme has higher efficiency and lower communication overhead compared with existing schemes.
Extraction of latent structure of motion and language through transfer learning of language knowledge

We introduce the latent structure of human whole body motion and language through neural network. A word is represented as a numeric vector based on large scale corpus of action description. We design the latent structure of motion and natural language. This latent structure makes it possible to recognize motions as linguistic descriptions.
Diabetes Classification Analysis Using the Euclidean Distance Method Based on the K-Nearest Neighbors Algorithm

Diabetes is a chronic disease characterized by high blood sugar (glucose) levels. Diabetes can increase the risk of a number of eye problems, some of which can lead to vision loss. It is estimated that 9.1 million Indonesians suffer from diabetes. Based on age group, most people with diabetes are in the 55-74 year age range. However, this disease is also experienced by young people in their 20s to 40s. One way to detect the classification of diabetes in machine learning is to use a dataset as training data so that performance testing can be carried out with the right classification method. The method used in this study is the K-Nearest Neighbor (KNN) algorithm, which is a method for classifying objects based on learning data that is closest to the object. The results of this study are Pattern Recognition in determining the right K value so that it shows a good accuracy value. At this stage, the value of K = 19 shows a good accuracy value, which is about 76% with an error rate of K = 19, which is about 24%
Analisis Kebutuhan Pengembangan Multimedia Pembelajaran Autoplay Berbasis Teams Games Tournament pada Materi Sistem Endokrin (Hormon)

Learning in this pandemic era has many variations of advantages and disadvantages. For example, it can help teachers and students can continue to carry out learning, even though they are not face to face directly. It also makes it easy to collect assignments and manage time. However, along with the convenience provided earlier, there are also things that must be considered. For example, limited learning time due to reduced learning time so that the material presented is not optimal. Cooperative learning is one of the learning models that can provide opportunities for students to learn and work together in small groups. And one of the cooperative learning models that can be used in the learning process is the TGT (Teams Games Tournament) learning model. One of the learning media that can be developed in the Pandemic era is Autoplay. Autoplay is software in which there are various types of media such as images, sound, video and text into the presentations made, also on the Autoplay media there is a quiz making application that can be used when evaluating students with the TGT (Teams Games Tournament) learning model.
FRobs_RL: A Flexible Robotics Reinforcement Learning Library

Reinforcement learning (RL) has become an interesting topic in robotics applications as it can solve complex problems in specific scenarios. The small amount of RL-tools focused on robotics, plus the lack of features such as easy transfer of simulated environments to real hardware, are obstacles to the widespread use of RL in robotic applications. FRobs_RL is a Python library that aims to facilitate the implementation, testing, and deployment of RL algorithms in intelligent robotic applications using robot operating system (ROS), Gazebo, and OpenAI Gym. FRobs_RL provides an Application Programming Interface (API) to simplify the creation of RL environments, where users can import a wide variety of robot models as well as different simulated environments. With the FRobs_RL library, users do not need to be experts in ROS, Gym, or Gazebo to create a realistic RL application. Using the library, we created and tested two environments containing common robotic tasks; one is a reacher task using a robotic manipulator, and the other is a mapless navigation task using a mobile robot. The library is available in GitHub <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Understanding the Advantages of a Hybrid Setting Over the Virtual Setting in Serious Game Application

The conventions of Virtual Reality (VR) controllers trace their lineage from decades of controllers used within the predominantly 2D computer world. Such controllers are not always precise or deft enough to adequately train people through Serious Games (SG) Applications. Virtual Reality environments open up huge opportunities for training “in the round” and while efforts are being made to refine controllers for use in this 3D VR medium, we propose that there is a case to be made now for a mixed medium approach, combining the freedom (and safety) of VR training with tactile, real-world equipment, integrated into the software. While not all VR training requires this level of specialism, it is an avenue worth exploring when trainees are learning skills that could save lives, or where full precision, at speed, could mean the difference between life and death. This paper presents a preliminary study within the context of our own case study of the creation and testing of a SG, VR based training application for the correct use of fire extinguishers, and how to use them to control a fire swiftly and safety.
Pengembangan Sistem Informasi Banten Menggunakan Web Semantik

The culture of Bali is strongly impacted by Hindu traditions and culture, as evidenced by the numerous yadnya ceremonies that Balinese people perform. Bali is one of the Indonesian islands with a diverse range of civilizations. Banten, also known as upakara, is a strategy for aiding in the execution of a ritual in Bali. However, a current problem is that many Balinese people only have a basic understanding of this cultural legacy and are only able to recognize it. As a result, a Banten information system was created for this project, and it was based on an ontology and prototyped utilizing the prototyping approach. The Methodology technique is employed in the construction of the ontology model. The system that was created has both semantic browsing and semantic searching capabilities. Black-Box Testing was used to test the system's accuracy and operation. The system functions well as a result of the outcomes.
Unethical Internet Behaviour among Students in High Education Institutions: A Systematic Literature Review

The modern internet era has several advantages and disadvantages, including the advent of immoral Internet conduct in addition to better, quicker, and increased working capacity in less time.Even though the area of study on unethical Internet activity has advanced, systematic literature reviews from a comprehensive perspective on unethical Internet behaviour among university students are still lacking.As a result, this systematic literature will provide theoretical foundation that address the following research questions: RQ1-How are unethical Internet behaviours among university students classified; RQ2-What are the various theoretical lenses that are used in unethical Internet behaviour research; RQ3-What demographic and risk factors are involved in unethical Internet behaviour research; and RQ4-What are the challenges and research opportunities for unethical Internet behaviour research within university settings?To respond to a formulated set of research questions, a total of 64 publications that were published between 2010 and 2020 underwent a systematic review.The study illustrates how university students' unethical Internet activity is categorised.This study offers a comprehensive grasp of the factors that affect unethical Internet behaviour and an overview of the theories that have been utilised to explain and forecast unethical Internet behaviours in this sector.This study discusses literature gaps for future research to contribute to human ethical behavioural studies.
Chaining Electronic Seals

The European eIDAS Regulation introduces an electronic seal as a legal solution necessary for the transition to a fully digital document flow. An electronic seal has to authenticate the origin of the document and confirm the data contained in a digital document. A usage scenario of electronic seals – e.g. confirming financial operations, issuing invoices, etc. – may require much harder security measures than in the case of electronic signatures attributed to physical persons. Although we are going to use cryptographic digital signature schemes for both electronic signatures and electronic seals, in the case of electronic seals the impact of a security breach might be greater by an order of magnitude. One of the critical issues is maintaining control over the set of seals created by a device. The owner of the device must be sure that no seal has been created without his consent. On the other hand, it should be impossible to hide any seal created by the device, for example, for accounting control purposes. In this paper, we present effective solutions for chaining of digital signature. When a list of electronic seals is presented, one can check whether this is the list of all electronic seals created by a device over a given period of time. We also provide means for clone detection. Unlike the previous solution, the proposed schemes do not require sharing any secret key with the seal creation device and the proof is not “one-time” only.
SISTEM INFOMASI PENGELOLAAN GAJI KARYAWAN BANK SUMSEL BABEL KANTOR CABANG PEMBANTU LEMABANG

Salary is a sum of money given to someone, either an employee or an employee in return for services for the business or work that has been done to the company. In determining employee payroll activities, the company should have an integrated system that can assist employee payroll activities. Bank Sumsel Babel Sub-Branch Lemabang is a branch company engaged in banking, in terms of employee payroll activities so far it is still done conventionally so that companies sometimes have difficulty in calculating employee salaries. With the problems faced at Bank Sumsel Babel Capem Lemabang, the authors raised the research with the title "Employee Payroll Information System for Bank Sumsel Babel Lemabang Sub-Branch Office".
A Fast Image Encryption Scheme Based on Simultaneous Permutation-Diffusion

In the traditional image encryption algorithm where permutation and diffusion are performed independently, multiple image scans are required, which reduces the encryption efficiency. In this paper, a fast image encryption scheme based on simultaneous permutation-diffusion is proposed, which performs simultaneous permutation-diffusion row by row and column by column. Firstly, the sorting and cyclic-shift are combined for the permutation. The permutation index sequence of the first row/column is obtained by sorting, and the rest is updated by the cyclic-shift operation related to the plain image, which can resist the chosen-plaintext attack. Secondly, cross-diffusion is performed with dynamic index sequences, which enhances the randomness of the diffusion behavior. Finally, the permutation and diffusion are performed at the same time. Therefore, a round of permutation and diffusion only needs to scan the image once, which improves the encryption efficiency. Note that our scheme only needs to generate and sort chaotic sequences with row/column level length, which reduces the iteration times of the chaotic system and sorting length. In addition, all index sequences are updated at row/column level, which is faster than pixel-level. The simulation results and security analysis show that our scheme has higher encryption efficiency than existing similar methods, and has sufficient security to resist common attacks.
Classification of Typhus and Dengue Fever Using the Pseudo Nearest Neighbor Algorithm

Typhus and dengue fever are diseases that often occur in Indonesia. The spread of these two diseases is relatively fast with similar symptoms. This could be a fatal thing if there is a misdiagnosis. Therefore, an application was developed to assist in the classification of typhus and dengue fever based on the patient's clinical symptoms using the PNN (Pseudo Nearest Neighbor) algorithm. This application receives input in the form of clinical symptoms experienced by the patient, then a preprocessing process is carried out to convert user input into discrete data, and the results are processed in classification using the PNN method. From the validation process with 5-fold cross validation obtained the best k value is k=6. Then, the accuracy testing process concluded that the accuracy of the classification process for typhoid and dengue fever with the PNN method is 68,97%. Then, from 25 respondents in the user acceptance test obtained that 88.4% of respondents strongly agree with the application design, 87.6% respondents strongly agree with the ease of application, and 86.6% respondents strongly agree with the efficiency provided by the application.
The Influence of Avatar Representation on Interaction Force in Virtual Environment

The representation of avatars in virtual environments (VE) affects co-presence was reported. These reports are limited only to visual interaction. This paper aims to clarify the effect of the avatar of representation on the interaction force in the cooperative task in VE. The interaction force in VE is essential for the construction of teleoperation. The cooperative task with multilateral control was conducted, and the interaction force and questionnaire were analyzed. As a result, the avatar’s representation was not affected by the interaction force. On the other hand, the subjective evaluation was changed by the avatar of representation. Therefore, it was concluded that avatars are not always necessary in a cooperative task.
High-Performance Graph Coloring on Intel CPUs and GPUs Using SYCL and KOKKOS

A variety of high-performance computing devices opens up many opportunities for numerical simulation in science and industry. Therefore, there is a growing need to investigate approaches for heterogeneous programming that allow developing and using a single code for different devices, sometimes with minor modifications to improve performance. In this paper, we explore the efficiency of using SYCL (Data Parallel C++) and KOKKOS to develop portable codes solving the graph coloring problem. We employ the commonly used Catalyurek algorithm, implement it on C++/OpenMP, SYCL (DPC++) and KOKKOS, and evaluate performance on Intel CPUs and GPUs. The paper discusses approaches of optimization performance that allow getting portable codes that run efficiently on both CPUs and GPUs. It is shown that performance portability takes place for this algorithm. We hope that our results can be used by researchers who implement graph algorithms for high-performance computing devices of various architectures.
Data Collection for Detecting Unwillingness to Answer Questions in Dialogue

To achieve dialogue agents that are social and can exhibit hospitality, it is important to detect the unwillingness of users to answer questions. However, it is difficult to detect such unwillingness because of the lack of data in which users are having difficulty answering questions, which hinders the creation of machine-learning based classifiers. This paper aims to collect dialogue data in which people are unwilling to answer questions. For this purpose, we created a list of difficult-to-answer questions on the basis of the cross-cultural communication literature and used such a list to collect dialogues in which such questions appear. By this procedure, we successfully collected dialogues in which users feel the difficulty to answer questions. Using the collected data, we also investigated if it is possible to train a machine-learning based classifier of unwillingness to answer questions.
Research publications of prof. C.N.R. Rao over the period of 20 years from 2001–2020: a study

The study aims to find out the publication details of Prof. C N R Rao which has been indexed under the Web of Science database over the period of 20 Years from 2001 to 2020. The methodology adopted for the present research study is scientometrics tools such as authorship pattern and collaboration, degree of collaboration, and citations. The study has revealed that a total of 371 publications contributed by Prof. CNR Rao in collaboration up to the end of the year 2009, no one publications are indexed in the web of science contributed by CNR after 2009. The highest 305 publications are articles were published in journals and a maximum of 125 publications was in three authorship patterns. He has contributed most of his (72.24%) publications in the field of chemistry. The mean value of the Degree of Collaboration is 0.97.
Quantitative Characterization of Scientific Computing Clusters

Several forms of non-HPC clusters named cluster of workstations and cluster of virtual machines have become available in universities and research institutions as cost effective solutions for scientific computing. With the need to characterize the cluster computing systems that are traditionally used to run high-performance computing applications and those that are not, the terms tightly- and loosely- coupled clusters were adopted. However this qualitative characterization of clusters does not provide further characterization of non-HPC systems, and does not reveal real insights into their capacity to tackle many scientific applications. As a consequence, researchers who use these computing systems do not have the tools to make informed decisions about what type of applications better fits the capacity and capabilities of every kind of non-HPC cluster. In this work, we propose the cluster performance profile. This profile enables the quantitative characterization, initially, on non-HPC clusters in order to support decisions in the use and development of these clusters.
AOED: Generating SQL with the Aggregation Operator Enhanced Decoding

NL2SQL is a translation task that converts natural language queries to SQL. We revisit the popular NL2SQL models and find that the accuracy of aggregation operator prediction remains a bottleneck of current NL2SQL models. We present a novel statistics-based approach called AOED, which stands for Aggregation Operator Enhanced Decoding, to help predict aggregation operator. AOED is a carefully designed mechanism that takes full advantage of the statistical information of the aggregation keywords in the natural language query to help improve the prediction accuracy of the aggregation operator. Experiments on the WikiSQL dataset show that our model outperforms the state-of-the-art model SQLova and NL2SQL-RULE by 3.4% and 0.7% on overall SQL results in the logical form accuracy and by 0.2% and 0.7% on aggregation operator result.