Reviewerly/exHarmony huggingface dataset:
Animal Tracking Berbasis Internet of Things

Animal husbandry is an activity of breeding and rearing carried out with a cage or a loose system. Loose livestock systems are commonly found in the Nusa Tenggara region, and some areas in Bali. This type of farm is difficult to monitor so it is vulnerable to theft. In addition to the possibility of theft, this livestock grazing system also has many other risks, one of which is the loss of cows either because they are lost or trapped in some places. Therefore, it is necessary to develop a tracking device that is able to monitor the presence of these livestock. This research aims to implement internet of things (IoT) for monitoring livestock or pets using GPS, NodeMCU ESP 8266, Firebase and Kodular. Data in the form of latitude and longitude values obtained by the GPS module will be sent to Firebase via a WiFi network. That data is then displayed in the Kodular application in latitude, longitude coordinates, and markers on maps. In addition, this tracking device that can be worn around the neck of the animal is equipped with an LED light that aims to monitor the position of livestock at night so that the position of them can be seen when it is dark.&#x0D; Keywords — Animal Tracking, Firebase, GPS, IoT, Kodular
The Development of Information Technology and Its Influence on the Field of Management Accounting

This study aims to determine the impact of the development of information technology in the field of management accounting. The method used is descriptive qualitative research methods and library research methods. The results of this study include the development of information technology that has brought important changes to the business world. There are many types of information systems that use emerging information technologies, including electronic data processing systems, data processing systems (DPS), decision support systems (DSS), data processing systems, management information (MIS), executive information systems (EIS), expert system (ES)) and Accounting Information System (AIS). The development of information technology has also influenced the field of management accounting because it is a field where information is generated in the context of management planning, control, and decision making. These impacts can be beneficial or detrimental to the business.
LIVER SEGMENTATION IN CT SCAN FOR OPTIMIZED METHOD

In a medical side liver segmentation is a major problem. It is a much more challenging task among other segmentation than a liver segment. So we have to introduce automatic segmentation of liver in CT images and MRI has become significance in medical area, so we are focusing the managing on domain from MRI to CT volumes on the example of 3D and 2D liver segmentation. Here, we are implemented to fuzzy clustering method and cuckoo optimized method to used in this method. In additional that the purpose of this method is to used for liver boundary calculation and segment the values using fuzzy C-means and fuzzy clustering using Support Vector Machine (SVM) for classification). The spatial liver boundary by constraints are named with different method such as the user can choose on particular region of interest and contour method until more accurate results to be obtained.
A multi-level approach with visual information for encrypted H.265/HEVC videos（China MM）

Abstract High-efficiency video coding (HEVC) encryption has been proposed to encrypt syntax elements for the purpose of video encryption. To achieve high video security, to the best of our knowledge, almost all of the existing HEVC encryption algorithms mainly encrypt the whole video, such that the user without permissions cannot obtain any viewable information. However, these encryption algorithms cannot meet the needs of customers who need part of the information but not the full information in the video. In many cases, such as professional paid videos or video meetings, users would like to observe some visible information in the encrypted video of the original video to satisfy their requirements in daily life. Aiming at this demand, this paper proposes a multi-level encryption scheme that is composed of lightweight encryption, medium encryption and heavyweight encryption, where each encryption level can obtain a different amount of visual information. First, we employ AES-CTR to generate a pseudo-random number sequence. Then, the main syntax elements in the H.265/HEVC encoding process are encrypted by a pseudorandom sequence. In the lightweight encryption level, the syntax element of the luma intraprediction model is chosen for encryption. In the medium encryption level, the syntax element of the discrete cosine transform (DCT) coefficient sign is employed for scrambling encryption. In the heavyweight encryption level, syntax elements of both the luma intraprediction model and the DCT coefficient sign are encrypted simultaneously by the pseudorandom sequence. It is found that both encrypting the luma intraprediction model (IPM) and scrambling the syntax element of the DCT coefficient sign can achieve the performance of a distorted video in which there is still residual visual information, while encrypting both of them can implement the intensity of encryption and one cannot gain any visual information. The experimental results meet our expectations appropriately, indicating that there is a different amount of visual information in each encryption level. Meanwhile, users can flexibly choose the encryption level according to their various requirements.
Comparison of K-Means and K-Medoids Algorithms in Text Mining based on Davies Bouldin Index Testing for Classification of Student’s Thesis

The thesis is one of the scientific works based on the conclusions of field research or observations compiled and developed by students as well as research carried out according to the topic containing the study program which is carried out as a final project compiled in the last stage of formal study. A large number of theses, of course, will be sought in looking for categories of thesis topics, or the titles raised have different relevance. However, the student thesis can be by topics that are almost relevant to other topics so that it can make it easier to find topics that are relevant to the group. One of the uses of techniques in machine learning is to find text processing (Text Mining). In-text mining, there is a method that can be used, namely the Clustering method. Clustering processing techniques can group objects into the number of clusters formed. In addition, there are several methods used in clustering processing. This study aims to compare 2 cluster algorithms, namely the K-Means and K-Medoids algorithms to obtain an appropriate evaluation in the case of thesis grouping so that the relevant topics in the formed groups have better accuracy. The evaluation stage used is the Davies Bouldin Index (DBI) evaluation which is one of the testing techniques on the cluster. In addition, another indicator for comparison is the computation time of the two algorithms. According to the DBI value test carried out on algorithm 2, the K-Medoids algorithm is superior to K-Means, where the average DBI value produced by K-Medoids is 1,56 while K-Means is 2,79. In addition, the computational time required in classifying documents is also a reference. In testing the computational time required to group 50 documents, K-Means is superior to K-Medoids. K-Means has an average computation time for grouping documents, which is 1 second, while K-Medoids provide a computation time of 26,7778 seconds.
The Effect of Multimedia Teaching Intervention on Physical Education Curriculum on University Students� Sports Attitudes and Sports Behaviors

The Effect of Multimedia Teaching Intervention on Physical Education Curriculum on University Students’ Sports Attitudes and Sports Behaviors
Identification based on random coding

Ahlswede and Dueck showed possibility to identify with high probability one out of $M$ messages by transmitting $1/C\log\log M$ bits only, where $C$ is the channel capacity. It is known that this identification can be based on error-correcting codes. We propose an identification procedure based on random codes that achieves channel capacity. Then we show that this procedure can be simplified using pseudo-random generators.
FedHeN: Federated Learning in Heterogeneous Networks

We propose a novel training recipe for federated learning with heterogeneous networks where each device can have different architectures. We introduce training with a side objective to the devices of higher complexities to jointly train different architectures in a federated setting. We empirically show that our approach improves the performance of different architectures and leads to high communication savings compared to the state-of-the-art methods.
Part-of-Speech Tagging of Odia Language Using statistical and Deep Learning-Based Approaches

Automatic Part-of-speech (POS) tagging is a preprocessing step of many natural language processing (NLP) tasks such as name entity recognition (NER), speech processing, information extraction, word sense disambiguation, and machine translation. It has already gained a promising result in English and European languages, but in Indian languages, particularly in Odia language, it is not yet well explored because of the lack of supporting tools, resources, and morphological richness of language. Unfortunately, we were unable to locate an open source POS tagger for Odia, and only a handful of attempts have been made to develop POS taggers for Odia language. The main contribution of this research work is to present a conditional random field (CRF) and deep learning-based approaches (CNN and Bidirectional Long Short-Term Memory) to develop Odia part-of-speech tagger. We used a publicly accessible corpus and the dataset is annotated with the Bureau of Indian Standards (BIS) tagset. However, most of the languages around the globe have used the dataset annotated with Universal Dependencies (UD) tagset. Hence, to maintain uniformity Odia dataset should use the same tagset. So we have constructed a simple mapping from BIS tagset to UD tagset. We experimented with various feature set inputs to the CRF model, observed the impact of constructed feature set. The deep learning-based model includes Bi-LSTM network, CNN network, CRF layer, character sequence information, and pre-trained word vector. Character sequence information was extracted by using convolutional neural network (CNN) and Bi-LSTM network. Six different combinations of neural sequence labelling models are implemented, and their performance measures are investigated. It has been observed that Bi-LSTM model with character sequence feature and pre-trained word vector achieved a significant state-of-the-art result.
Representation Learning in Continuous-Time Dynamic Signed Networks

Signed networks allow us to model conflicting relationships and interactions, such as friend/enemy and support/oppose. These signed interactions happen in real-time. Modeling such dynamics of signed networks is crucial to understanding the evolution of polarization in the network and enabling effective prediction of the signed structure (i.e., link signs and signed weights) in the future. However, existing works have modeled either (static) signed networks or dynamic (unsigned) networks but not dynamic signed networks. Since both sign and dynamics inform the graph structure in different ways, it is non-trivial to model how to combine the two features. In this work, we propose a new Graph Neural Network (GNN)-based approach to model dynamic signed networks, named SEMBA: Signed link's Evolution using Memory modules and Balanced Aggregation. Here, the idea is to incorporate the signs of temporal interactions using separate modules guided by balance theory and to evolve the embeddings from a higher-order neighborhood. Experiments on 4 real-world datasets and 4 different tasks demonstrate that SEMBA consistently and significantly outperforms the baselines by up to $80\%$ on the tasks of predicting signs of future links while matching the state-of-the-art performance on predicting the existence of these links in the future. We find that this improvement is due specifically to the superior performance of SEMBA on the minority negative class.
Approximate Solutions of Linear Systems at a Universal Rate

Let $A \in \mathbb{R}^{n \times n}$ be invertible, $x \in \mathbb{R}^n$ unknown and $b =Ax $ given. We are interested in approximate solutions: vectors $y \in \mathbb{R}^n$ such that $\|Ay - b\|$ is small. We prove that for all $0&lt; \varepsilon &lt;1 $ there is a composition of $k$ orthogonal projections onto the $n$ hyperplanes generated by the rows of $A$, where $$k \leq 2 \log\left(\frac{1}{\varepsilon} \right) \frac{ n}{ \varepsilon^{2}}$$ which maps the origin to a vector $y\in \mathbb{R}^n$ satisfying $\| A y - Ax\| \leq \varepsilon \cdot \|A\| \cdot \| x\|$. We note that this upper bound on $k$ is independent of the matrix $A$. This procedure is stable in the sense that $\|y\| \leq 2\|x\|$. The existence proof is based on a probabilistically refined analysis of the Random Kaczmarz method which seems to achieve this rate when solving for $A x = b$ with high likelihood.
A Methodology to Support Automatic Cyber Risk Assessment Review

Cyber risk assessment is a fundamental activity for enhancing the protection of an organization, identifying and evaluating the exposure to cyber threats. Currently, this activity is carried out mainly manually and the identification and correct quantification of risks deeply depend on the experience and confidence of the human assessor. As a consequence, the process is not completely objective and two parallel assessments of the same situation may lead to different results. This paper takes a step in the direction of reducing the degree of subjectivity by proposing a methodology to support risk assessors with an automatic review of the produced assessment. Our methodology starts from a controls-based assessment performed using well-known cybersecurity frameworks (e.g., ISO 27001, NIST) and maps security controls over infrastructural aspects that can be assessed automatically (e.g., ICT devices, organization policies). Exploiting this mapping, the methodology suggests how to identify controls needing revision. The approach has been validated through a case study from the healthcare domain and a set of statistical analyses.
Politeness Maxims Used in English Classroom Interaction of Fourth Semester at STKIP Taman Siswa Bima

The The aims of this research is to find out the used types of politeness maxims and the function of most dominant type of politeness maxims used in English classroom interaction during the learning process. The reason why the researcher did this research because the research wanted to provide more sources or references about the types of politeness maxims and the function of most dominant type of politeness maxim that was hopefully be useful for the institution where the researcher study, for other students, other researchers, or readers in general. This research used descriptive qualitative method. Descriptive qualitative method is intended to describe everything related to the type of politeness maxims in classroom interaction. There were three videos which used as a source of data. Those videos showed the activity of the learning process in the classroom interactions. In collecting the data, the researcher would transcribe the videos into written data accurately and classify the data based on types of politeness maxim in order the data would be easily in identified and analyzed. After all the data collected, the researcher analyzed the data and drawed the conclusion. The results of this research can be seen as follows: (1) There were 96 data of politeness maxim appeared in 3 of videos, which consist of 68 data of tact maxim, 14 data of approbation maxim, 10 data of agreement maxim, 2 data of sympathy maxim, 1 data of generosity maxim and 1 data of modesty maxim. (2) Based on the research, tact maxim was the dominant type and the function that can be concluded for several function as declarative, interrogative, imperative and apology function that appeared during the learning process to espouse the spirit of students with the factor of context of situation and participant.
Learning to restore images degraded by atmospheric turbulence using uncertainty

Atmospheric turbulence can significantly degrade the quality of images acquired by long-range imaging systems by causing spatially and temporally random fluctuations in the index of refraction of the atmosphere. Variations in the refractive index causes the captured images to be geometrically distorted and blurry. Hence, it is important to compensate for the visual degradation in images caused by atmospheric turbulence. In this paper, we propose a deep learning-based approach for restring a single image degraded by atmospheric turbulence. We make use of the epistemic uncertainty based on Monte Carlo dropouts to capture regions in the image where the network is having hard time restoring. The estimated uncertainty maps are then used to guide the network to obtain the restored image. Extensive experiments are conducted on synthetic and real images to show the significance of the proposed work. Code is available at : https://github.com/rajeevyasarla/AT-Net
GAMIFICATION IN DISTANCE EDUCATION AS A INSTRUMENT FOR STUDENT ENGAGEMENT IN HIGHER EDUCATION

Digital Information and CommunicationTechnologies (TDIC) provide a new path to be followed, with challenges and opportunities that make the teaching-learning process more effective and dynamic.Distance education (EAD) has a better development with the emergence of technological tools, with greater access and advancement of the internet.And one of the biggest challenges that teachers have been facing is getting students to engage in achieving quality in distance education.This article aims to promote the debate on the possibilities that gamification in Education provides for the virtual classroom in higher education to become more attractive and pedagogical practices can reflect the need for digital inclusion in contemporary society.
Sistem Manajemen Konferensi Ilmiah Berbasis Web Menggunakan Metode Pengembangan Waterfall

Scientific publications are a means of delivering research results and innovations of researchers. Recently, researchers' interest in publishing publications is also increasing. This increase occurred in the publication of papers through scientific conferences. This encourages the need for the use of information technology to facilitate conference management. However, the application of the conference management system is still minimal. Based on these problems, this study proposes a web-based conference management method. The system is built using a MySQL database and Model-View-Controller (MVC) code architecture in PHP. Therefore, every conference process can be included in the system. the system is designed with three main modules, namely the participant/presenter module, reviewer module, and committee/admin module, which covers the entire conference process starting from registration, paper submission, review, payment and distribution of presentation rooms. Trials and evaluations are carried out using the Black Box Testing method to ensure the system can work well. The results of the tests and evaluations that have been carried out show that the proposed system is proven to have good performance with a score of 100% or successfully fulfills all existing test scenarios
A Secured Framework for Short Messages Service in Global System for Mobile Communication

Short Message Service (SMS) has upgraded and elevated our lives and is principal in our livelihood. This service has been driven by the Global System for Mobile Communications (GSM) which is the most popular standard for mobile telephony systems. As a result, the GSM has constituted the moderate execution of short message service (SMS), which has uplifted mobile phone quality. A proper understanding of the essentials of SMS/MMS security opens the door to prevent some common security. which has fostered the prevention of some common threats in SMS usage. Unfortunately, the SMS does not has any built-in vetting procedure to authenticate the text or provide security for the data/text, and supportive phone facilities are designed without considering the SMS/MMS security aspects. There is also a problem of mobile applications developer and the mobile service providers not knowing the correct identities of the communicating parties, and also the problem of little to no SMS/MMS content confidentiality and integrity during data transmission. This paper is aimed at addressing these problems by focusing on the use of substitution techniques of audio steganography. We achieve our method by having a low robustness against attacks which try to reveal the hidden message. This allows only SMS that is encrypted, and application to multimedia messages needs to be further investigated. Furthermore, encryption is done using substitution cipher methods. We also analyze the security mechanism based on the mobile security requirements and mobile performance capability. Analysis of the security mechanism based on the mobile security requirements and mobile performance capability is carried out. After an exhaustive computer simulation, the performance of the proposed method is confirmed. The result obtained showed that compression ratio achieved is within the range of 0.1 and 0.5, the best achieved so far.
Improving Data Reliability through Assured Digital Signing

Digital signatures are an important mechanism for ensuring data trustworthiness via source authenticity, integrity, and source non-repudiation. However, their trustworthiness guarantee can be subverted in the real world by sophisticated attacks, which can obtain cryptographically legitimate digital signatures without actually compromising the private signing key. This problem cannot be adequately addressed by a purely cryptographic approach, by the revocation mechanism of Public Key Infrastructure (PKI) because it may take a long time to detect the compromise, or by using tamper-resistant hardware because the attacker does not need to compromise the hardware. This problem will become increasingly more important and evident because of stealthy malware (or Advanced Persistent Threats). In this paper, we propose a novel solution, dubbed Assured Digital Signing (ADS), to enhancing the data trustworthiness vouched by digital signatures. In order to minimize the modifications to the Trusted Computing Base (TCB), ADS simultaneously takes advantage of trusted computing and virtualization technologies. Specifically, ADS allows a signature verifier to examine not only a signature’s cryptographic validity but also its system security validity that the private signing key and the signing function are secure, despite the powerful attack that the signing application program and the general-purpose Operating System (OS) kernel are malicious. The modular design of ADS makes it application-transparent (i.e., no need to modify the application source code in order to deploy it) and almost hypervisor-independent (i.e., it can be implemented with any Type I hypervisor). To demonstrate the feasibility of ADS, we report the implementation and analysis of an Xen-based ADS system.