Reviewerly/exHarmony huggingface dataset:
Cyber Risk Mitigation in Higher Education

The importance of having robust cybersecurity risk mitigation techniques in this rapidly evolving technological world cannot be overstated. Educational organizations hold an immense amount of personal data, and in the event of a data breach, it can cause serious damage to the organization. Similar to other organizations, educational organization data breaches occur in many forms, such as unauthorized access, ransomware, malware, physical theft, financial theft, or merely unintended disclosure of information. When higher education becomes the target of a cyberattack, the damage goes beyond the loss of personal identifiable information (PII) of faculty, staff, or students. According to the U.S. Department of Homeland Security, the cyberattack damage for higher education can include reputational, financial, and even national security, as some higher education installations work on defense research projects. The ability to safely connect to educational systems is an essential component of a supportive and safe learning environment. Cyberattacks are a constant threat for higher education institutions, especially after the COVID-19 pandemic shut down university campuses worldwide, forcing students, faculty, and staff to move online. With higher education shifting its operations online, both academic and IT systems face difficult challenges. The higher education entities need to have proper risk mitigation techniques, including defense strategies and effective security policies to safeguard the educational environment from data breaches and targeted cyberattacks. This article provides an overview of data breaches and risk mitigation techniques and strategies in higher education organizations.
REMOTE PROGRAMMED INSTRUCTION AND TUTORIAL AS STRATEGIES IN THE LEARNING PROCESS FOR THE DEVELOPMENT OF MATHEMATICAL COMPETENCES.

The objective of the research is to determine the importance in the use of the remote programmed instruction and the tutorial as strategies in the learning process for the development of mathematical competences in students of the middle school of Educational Institutions of the District of Barranquilla, Colombia. Given the current situation of remote education caused by the pandemic Sars Cov 2 virus, known as Corona Virus, Covid 19, has had an impact on the fabric of human reality on a planetary level, especially because of the health conditions of communities at the local, regional, national and global levels, educational institutions as social organizations have thus migrated to a form of virtual study. In this sense, the role that the school plays in this current context, its teachers and parents have been decisive to face this formation from the home, where the technologies are the tools that allow to establish the direct connection with the students. Methodologically, the research was descriptive – Field, with a non-experimental, transactional design, with descriptive and inferential analysis. The population consisted of 140 eleventh-grade students. It was found that the eleventh grade students stood out in the high category, when it comes to using technological tools using the Remote Programmed Instruction (IPR) and the tutorial on solving mathematical problems. It is concluded that the use of technologies under the virtual remote modality promotes favorable results in the training of students. It is recommended to establish a curriculum and content in the area of mathematics adapted to learning strategies through remote programmed teaching.
Customer Response to Adverse Security Events: An Empirical Study

Data breaches and identity thefts have dramatically increased in both scope and scale in recent years, in which large number of customers' information has been compromised. With a detailed dataset of anonymized transactions from a large bank in the United States, we use 2014 Home Depot data breach as a natural experiment to directly investigate how data breach affects customers relations with the retailer. We find that these potentially affected customers maintained their regular use of debit cards even after the breach. Furthermore, there was not much difference in impact of data breach on Home Depot stores faced with different levels of competition and there were few, if any, changes in both frequency and amount of transaction. In short, though data breach caused about $300 million expenses for Home Depot, its customers rarely responded negatively to this adverse security event.
Using Computer Vision to Analyze Non-manual Marking of Questions in KRSL

This paper presents a study that compares non-manual markers of polar and wh-questions to statements in Kazakh-Russian Sign Language (KRSL) in a dataset collected for NLP tasks. The primary focus of the study is to demonstrate the utility of computer vision solutions for the linguistic analysis of non-manuals in sign languages, although additional corrections are required to account for biases in the output. To this end, we analyzed recordings of 10 triplets of sentences produced by 9 native signers using both manual annotation and computer vision solutions (such as OpenFace). We utilize and improve the computer vision solution, and briefly describe the results of the linguistic analysis.
Design and Simulation of a Concatenation of Parity Check Code and Polarization Code Based on Verilog HDL Language

Polar code polarization codepolarizes the channel into two types of channels with a channel capacity close to 1 and another channel capacity close to 0. The channel whose channel capacity is close to 1 by polarization is the only coding scheme that can be strictly proved to reach Shannon’s limit. In order to reduce the additional hardware overhead of Polar code for CRC Cyclic Redundancy Check cyclic redundancy circuit this paper proposes a coding scheme based on the concatenation of parity-check-concatenated PCC and polarization codes. FPGA coding scheme based on Verilog’s PCC polar code. Simulation results prove that the scheme can be used in both verification modes.
Forward Secret Encrypted RAM: Lower Bounds and Applications.

In this paper, we study forward secret encrypted RAMs (FS eRAMs) which enable clients to outsource the storage of an n-entry array to a server. In the case of a catastrophic attack where both client and server storage are compromised, FS eRAMs guarantee that the adversary may not recover any array entries that were deleted or overwritten prior to the attack. A simple folklore FS eRAM construction with \(O(\log n)\) overhead has been known for at least two decades. Unfortunately, no progress has been made since then. We show the lack of progress is fundamental by presenting an \(\varOmega (\log n)\) lower bound for FS eRAMs proving that the folklore solution is optimal. To do this, we introduce the symbolic model for proving cryptographic data structures lower bounds that may be of independent interest.
Privacy-preserving Sequential Pattern Mining in distributed EHRs for Predicting Cardiovascular Disease.

From electronic health records (EHRs), the relationship between patients' conditions, treatments, and outcomes can be discovered and used in various healthcare research tasks such as risk prediction. In practice, EHRs can be stored in one or more data warehouses, and mining from distributed data sources becomes challenging. Another challenge arises from privacy laws because patient data cannot be used without some patient privacy guarantees. Thus, in this paper, we propose a privacy-preserving framework using sequential pattern mining in distributed data sources. Our framework extracts patterns from each source and shares patterns with other sources to discover discriminative and representative patterns that can be used for risk prediction while preserving privacy. We demonstrate our framework using a case study of predicting Cardiovascular Disease in patients with type 2 diabetes and show the effectiveness of our framework with several sources and by applying differential privacy mechanisms.
Simulating the Maintenance Performance on the Attractiveness of Data Center Services: A System Dynamics Approach

Nowadays, information-making is growing rapidly due to the developments in technology; thus, it is critical to maintain information and establish integrated systems for storage and office automation in any organization; thereby, the establishment of datacenters has become a requirement for them. Despite offering models related to some parts of the data centers, there are still ambiguities in identifying the influential and key variables. This study aimed at offering a dynamic model for the parts related to the attractiveness of data center services by using Vensim software. Five reinforcing loops and seven balancing loops were extracted through studying the review of literature; then, the relevant stock and flow variables have been defined. Afterwards, the model is presented in the form of cause and effect model and the stock-flow model. Also, various scenarios have been studied, including increasing the training capacity and attractiveness, imposing an overload on the network and switch failure, upgrading the bandwidth carrying capacity, reducing the maximum maintenance. According to the results and sensitivity analysis done in this research, it can be said that the attractiveness of services, the acquired skills, and the maximum feasible maintenance along with the bandwidth carrying capacity, storage, and processing power of the data centers is so vital in determining the performance level of data centers and the attractiveness of services.
Analisis Kinerja Solar Home System untuk Pemenuhan Kebutuhan Energi Listrik pada Kantor Desa Nyamuk di Kecamatan Karimunjawa

Pusat Studi Energi Universitas Gadjah Mada di tahun 2014 memperoleh dana dari United States Agency for International Development (USAID) untuk membangun delapan unit Solar Home System (SHS) dengan kapasitas 500 Wp dan empat unit Solar Water Pumping System (SWPS) dengan kapasitas 600 Wp untuk beberapa fasilitas publik di Kecamatan Karimunjawa yang meliputi kantor kecamatan, kantor desa, puskesmas, masjid, pelabuhan, dan sekolah. Salah satu Solar Home System yang dibangun adalah SHS Kantor Desa Nyamuk. Penelitian dilakukan selama 145 hari yang dimulai dari 1 Juni 2018 dan berakhir pada 24 Oktober 2018 bertujuan untuk mengetahui kinerja dari SHS Kantor Desa Nyamuk dalam pemenuhan kebutuhan energi listrik Kantor Desa Nyamuk. SHS Kantor Desa Nyamuk dari tanggal 1 Juni 2018 hingga 24 Oktober 2018 memproduksi energi listrik berdasarkan data remote monitoring sebesar 49,62 KWh atau 21,4% dari total produksi energi secara teoritis sebesar 229,97 KWh dan 1,7% dari energi potensi energi yang dihasilkan yaitu 2.921,98 KWh. SHS Kantor Desa Nyamuk selama lima bulan mengkonsumsi energi sebesar 31,6 KWh atau sebesar 63,7% dari produksi remote monitoring. Berdasarkan hasil analisis tersebut, SHS dengan kapasitas terpasang 500 W_p mampu mencukupi kebutuhan energi listrik di Kantor Desa Nyamuk Kecamatan Karimunjawa. The Center for Energy Studies at Gadjah Mada University in 2014 obtained funding from the United States Agency for International Development (USAID) to build eight Solar Home System (SHS) units with a capacity of 500 Wp and four Solar Water Pumping Systems (SWPS) units with a capacity of 600 Wp for several public facilities in Karimunjawa Subdistrict which include sub-district offices, village offices, puskesmas, mosques, ports, and schools. One of the Solar Home Systems that was built was SHS Nyamuk Village Office. The research was conducted for 145 days starting from 1 June 2018 and ended on 24 October 2018 with the aim of finding out the performance of the SHS Village Mosquito Office in meeting the electrical energy needs of the Nyamuk Village Office. SHS Mosquito Village Office from 1 June 2018 to 24 October 2018 produced electricity based on remote monitoring data of 49.62 KWh or 21.4% of the total theoretical energy production of 229.97 KWh and 1.7% of potential energy produced 2,921.98 KWh. SHS Village Mosquito Office for five months consumed 31.6 KWh of energy or 63.7% of remote monitoring production. Based on the results of the analysis, the SHS with an installed capacity of 500 Wp was able to meet the electricity needs in the Nyamuk Village Office in Karimunjawa District.
Research on automatic speech recognition based on a DL–T and transfer learning

Speech has been a natural and effective way of communication, widely used in the field of information-communication and human–machine interaction. In recent years, various algorithms have been used for achieving efficient communication. The main purpose of automatic speech recognition (ASR), one of the key technologies in this field, is to convert the analog signals of input speech into corresponding text digital signals. Further, ASR can be divided into two categories: one based on hidden Markov model (HMM) and the other based on end to end (E2E) models. Compared with the former, E2E models have a simple modeling process and an easy training model and thus, research is carried out in the direction of developing E2E models for effectively using in ASR. However, HMM-based speech recognition technologies have some disadvantages in terms of prediction error rate, generalization ability, and convergence speed. Therefore, recurrent neural network–transducer (RNN–T), a typical E2E acoustic model that can model the dependencies between the outputs and can be optimized jointly with a Language Model (LM), was proposed in this study. Further, a new acoustic model of DL–T based on DenseNet (dense convolutional network)–LSTM (long short-term memory)–Transducer, was proposed to solve the problems of a high prediction error rate and slow convergence speed in a RNN–T. First, a RNN–T was briefly introduced. Then, combining the merits of both DenseNet and LSTM, a novel acoustic model of DL–T, was proposed in this study. A DL–T can extract high-dimensional speech features and alleviate gradient problems and it has the advantages of low character error rate (CER) and fast convergence speed. Apart from that, a transfer learning method suitable for a DL–T was also proposed. Finally, a DL–T was researched in speech recognition based on the Aishell–1 dataset for validating the abovementioned methods. The experimental results show that the relative CER of DL–T is reduced by 12.52% compared with RNN–T, and the final CER is 10.34%, which also demonstrates a low CER and better convergence speed of the DL–T.
The Effect of KWL (Know, Want to know and Learned) Strategy on Students’ Writing Skill at SMA Negeri 5 Tanjungpinang

This study aims to determine the effect of using the KWL (Know, Want to know and Learned) strategy on students’ writing skill at SMA Negeri 5 Tanjungpinang. A pre-experimental design was used as the design of the study with one group of eleventh grade of SMA Negeri 5 Tanjungpinang with 21students as the sample of the research. A pre-test was administered at the beginning of the study and was followed by an introduction and understanding of the use of the KWL strategy in the process of students’ writing personal letters as the treatment.A post test was also administered at the end of the study. The pre and post test score then were analyzed by using SPSS 22 to find the mean score between the two scores. Then a paired sample t-test was also run to answer the research hypothesis. The statistical test analysis used on this research showed that KWL strategy is an effective strategy used to improve students' writing skills, especially to be applied to students at Tanjungpinang in this 2021, this is indicated by a significant difference in the mean scores between the mean score of pre and post test, from 80.476 to 86.095. Thus, it answers the research hypothesis that the implementation of the KWL (Know, Want to know and Learned) strategy on students at SMA Negeri 5 Tanjungpinang had a significant effect in improving students' writing skills.
HUBUNGAN ANTARA MINAT MEMBACA DENGAN PRESTASI BELAJAR SISWA SEKOLAH DASAR DI KOTA BANDUNG

This study aims to analyze the relationship between reading interest and learning achievement of elementary school students. The research method used is correlational research with one independent variable and one dependent variable. The data obtained were students' reading interest and learning achievement data. The population in this study were all fifth grade elementary school students in the city of Bandung. The sample in the study was taken by purposive sampling, namely three schools that represent certain areas in the city of Bandung were taken. Reading interest data was obtained from a questionnaire filled out by students who became the sample, and learning achievement data was taken from the average score of students in all subjects. Furthermore, the data were analyzed using the Spearman correlation test with α = 5%. The results of this study indicate that there is a positive and significant relationship between students' reading interest and their learning achievement. The relationship is 12%. This shows that the higher the students' reading interest, the higher the learning achievement obtained in each subject, and vice versa.
Factorization of Polynomials Given by Arithmetic Branching Programs.

Given a multivariate polynomial computed by an arithmetic branchingprogram (ABP) of size s, we show that all its factors can becomputed by arithmetic branching programs of size poly(s).Kaltofen gave a similar result for polynomials computed byarithmetic circuits. The previously known best upper bound forABP-factors was poly $$ (s^{ {\rm \log} s}) $$ .
Currency Detector for Visually Impaired using Machine Learning

Apart from the immediate use of Master cards and any form of electronic payment methods, money has been widely used for general exchange due to its usefulness. However, visually impaired people can suffer to know each currency paper separately. This research project aims to develop a mobile app. based on the Convolutional Neural Network using pre-trained representations to permit visually impaired people to see Indian banknotes in real-time situations. The elementary methods used in our planned system include pre-image classification, segmentation, histogram equalization, region of interest (ROI), and finally template matching using MobileNetV1-224 and TensorFlow.
MODEL PENGELOLAAN ANALISIS KEBIJAKAN AKADEMIS DI FAKULTAS ILMU KOMPUTER DENGANPENDEKATAN REKAYASA INFORMASI

In the era of digitalization, the quality of life is determined by the ability to manage information technology. For this reason, educational institutions are responsible for creating quality graduates in all fields, especially in the field of information technology. The development of information technology, especially in information engineering capabilities, provides future opportunities for the Faculty of Computer Science, Esa Unggul University. The research method is descriptive qualitative, namely conducting field observations by conducting interviews with Human Resources at the Faculty as primary data that will be used as input for the policy analysis process by taking into account the PIECES method. With the description above, the academic side plays a very important role in producing the quality of academic fluency in the direction of policies determined by understanding and consistency of modeled and integrated policies that are rooted in data, activities and policies that are under the authority of the leadership, the needs and users. Keywords: Model, Academic Policy, Faculty of Computer Science, Information Engineering
Exploring blended ic-pbl model & strategy for course based pbls in university: Using a case study in engineering education

The pandemic situation caused by COVID-19 amplifies the volatility, uncertainty, complexity, and ambiguity of society, suggesting that the unpredictability of this will be further maximized in 2021. Each field in society is selecting and executing digital transformations as one of the strategies to flexibly cope with such uncertainty and sudden changes in an environment that requires different lifestyles to lead daily lives. The education field is not an exception and universities are implementing digital transformations that move from face-to-face education that is held in a classroom environment to online learning in the virtual environment. Hanyang University has developed and operated an innovative teaching and learning model called IC-PBL (Industry-Coupled Problem/Project-Based Learning) to flexibly respond to rapid social changes and to cultivate outstanding individuals with capabilities needed in society. IC-PBL, which has been based on the face-to-face classroom environment, is also in the process of digital transformation due to such environmental changes. Previously, in IC-PBL, the online learning environment played an auxiliary role to support learning. However, as the online learning environment has suddenly been highlighted as the main learning environment of IC-PBL, various issues related to the design and operation of PBL classes in actual education fields are being raised. In this study, the results of participation observations, in-depth interviews with instructors and learners were analyzed through qualitative research on 8 classes in Hanyang University's engineering department. As a result, PBL courses strategies, in accordance with 5G, were presented to make PBL classes in blended environments into successful digital transformations. This demonstrates that PBL courses that have been mostly conducted only in face-to-face environments successfully achieved digital transformation in the post COVID-19 era and have presented strategies that can be used in educational attempts and practices necessary to create sustainable performance as a qualified class. © The authors, 2021.
Assessing Intelligence Conversation Agent Trends- Chatbots-AI Technology Application for Personalized Marketing

This research presents an overview of the global trends of personalized marketing along four dimensions: personalization impact, preferred personalized marketing channel, challenges, and personalization drivers/benefits. Based on the analysis of the previously surveyed reports and article, the research presents a rich insight from two perspectives: the present and futuristic role of marketing professionals across industries and countries, as well as consumers’ perceptions based on personalized marketing. The global result shows that the utilization rate of artificial intelligence powered chatbots is lagging across most industries and countries, except for travel and tourism industries. Furthermore, it is shown that the effectiveness of any personalized marketing depends largely on the access to quality data, data analytics, and automated technological solutions to help harness and integrate data from multiple channels. Taken together, the research findings consistently highlight the importance of personalized marketing to be for these: revenue growth, profits, customer loyalty and retention, and increasing customer lifetime value through engagement.