Reviewerly/exHarmony huggingface dataset:
Pengamanan Audio Rindik Bali Menggunakan Metode Least Significant Bit Berbasis Android

The development of an increasingly fast era makes the pace of technology is also growing which is directly proportional to the level of crime that is growing as well. Dissemination of personal or group information becomes vulnerable to attacks from other parties, so the security of data or confidential information needs to be improved. Bali has many different musical instruments in each region, both in terms of the name and the instrument itself. One of them is Rindik. Rindik is one of the percussion instruments that’s still in demand because of its distinctive sound (Aristana et al., 2015). Because of Balinese Rindik is still interesting for most peoples, making its need to be secured to avoid digital crime so that the owner of the audio feels safe. One of the many security that can avoid an action crime on Balinese Rindik musical instruments is by inserting text containing personal data from the audio owner in the format (.txt) into Balinese Rindik audio in the format (.wav) using the Least Significant Bit (LSB) steganography method.
Relationship between personal characteristics and adoption of drip system of irrigation of vegetable growers in the adopted villages of SKNAU, Jobner

Drip irrigation is a technique in which water flows through a filter into special drip pipes, with emitters located at different spacing. Water is distributed through the emitters directly into the soil near the roots through a special slow-release device. Drip irrigation can be a very technical irrigation system for food or plant production fields. But compared to other technical systems. it is a low-technique solution. Furthermore it is possible to combine this system with a water treatment plant (e.g. non-planted filter or constructed wetlands (horizontal flow or vertical flow) and use the treated water as irrigation water. The present study was conducted in the year 2020 with an objective to determine the relationship between adoption of drip system of irrigation of vegetable growers and their personal characteristics. The total sample size of the study was 120. The age was found negatively and size of land holding’ was positively non-significant, furthermore, the independent variable like, Education, Occupation, Annual Income and Social participation were found to be positive and significantly relationship with adoption of drip system of irrigation of vegetable growers.
Tree-Based Unidirectional Neural Networks for Low-Power Computer Vision

Editor’s notes: Convolutional neural networks (CNNs) have paved paths for highaccuracy computer vision. This article presents a tree-based hierarchy of multiple shallow CNNs to enable their low-power implementations for embedded devices. —Muhammad Shafique, New York University Abu Dhabi
Go Basics

What makes Go an easy-to-learn language is that everything you need to know about a Go program is right on the surface. There is no need to remember any language rules, as they are all in the application code. This chapter cover the basics of the Go programming language, like taking input, using math operators and packages, managing memory, using different data structures, and understanding the program flow of a Go-based program. It also covers using functions and returning multiple values, as well as reading and writing data to files and handling JSON data.
Fast and Incremental Computation of Weak Control Closure

Control dependence is a fundamental concept used in many program analysis techniques such as program slicing, program debugging, program parallelization, and detecting security leaks. Since the introduction of this concept in the late eighties, numerous definitions of control dependencies and their computation methods have been developed. The later definitions are progressively more generalized covering a wide spectrum of modern programming language constructs. The most generalized concepts are the weak and strong control closure (WCC and SCC) that capture the nontermination (in)sensitive control dependencies of a given program. In this paper, we have developed a novel method to compute WCC incrementally. Any client application of WCC such as program slicing requires computing the WCC repeatedly in a fixpoint computation. An incremental algorithm to compute WCC will improve the performance of the client application significantly. We have provided the proof of correctness and the theoretical worst-case complexity of our algorithm. We have performed an experimental evaluation on well-known benchmarks, and our experiments reveal that we have significantly improved the practical efficiency in computing WCC incrementally. We have obtained an average speedup of 31.03 in all benchmarks and a maximum speedup of 35.29 than the best state-of-the-art algorithm computing WCC.
Structs, Methods, and Interfaces

Go supports structs (short for structures) that allow programmers to define user-defined types. Related data that exhibits a “has-a” relationship can be grouped into a single logical unit using structs. Methods in Go are functions that can be tied to user-defined types to define their behavior. Another important feature of the Go language is the concept of interfaces. In Go, structs and interfaces go hand in hand to organize methods and data handling. An interface can be defined as a collection of method signatures that can be implemented by any other type. Implementing an interface in Go merely means implementing the methods defined by that interface. Furthermore, an interface only defines but does not declare the behavior of an object. To simplify, a struct defines the fields of an object, e.g., a shape’s type and color, whereas an interface defines the methods that can be used with that object, e.g., setting and returning a shape’s color. This chapter provides Go recipes related to the usage of structs, methods, and interfaces.
Multi-population Variants of Cultural Algorithms

The idea of using multiple independent sub-populations in the population space of meta-heuristic algorithms is quite common in the literature. In some cases, the multiple groups of individuals searching different parts of the search space could increase the diversity and keep the algorithm away from the local optimums. In this chapter, the multi-population versions of CAs and their algorithmic frameworks will be discussed.
A More Design-Flexible Medical Transformer for Volumetric Image Segmentation

UNet-based encoder-decoder networks dominate volumetric medical image segmentation in the past several years. Many improvements focus on the design of encoders, decoders and skip connections. Due to the intrinsic property of convolutional kernels, convolution-based encoders suffer from limited receptive fields. To deal with that, recently proposed Transformer-based networks leveraging the self-attention mechanism build long-range dependency. However, they are highly reliable on pretrained weights from natural images. In our work, we find out ViT-based (Vision Transformer) models’ performance will not decrease significantly without pretrained weights even if there is a limited data source. So we flexibly design a 3D medical Transformer for image segmentation and train it from scratch. Specifically, we introduce Multi-Scale Dynamic Positional Embeddings to ViT to dynamically acquire positional information of each 3D patch. Positional bias can also enrich attention diversities. Moreover, we give detailed reasons why we choose the convolution-based decoder instead of recently proposed Swin Transformer blocks after preliminary experiments on the decoder design. Finally, we propose the Context Enhancement Module to refine skipped features by merging low and high-frequency information via a combination of convolutional kernels and self-attention modules. Experiments show that our model is comparable to nnUNet on segmentation performance of Medical Segmentation Decathlon (Liver) and VerSe’20 datasets when trained from scratch.
Under the after-text mask

Под маской за-текста - Institutional repository of Federal State Autonomous Educational Institution of Higher Professional Education Ural Federal University named after the first President of Russia B.N.Yeltsin
EVALUASI PEMBELAJARAN ONLINE SELAMA PANDEMI COVID-19 TERHADAP HASIL BELAJAR MAHASISWA KEPERAWATAN

Abstract. Internet-based e-learning during Covid-19 quarantine becomes a burden for nursing student due to practical skills courses are difficult to assess. However, on the other hand that situation improves their digital literacy. This study was aimed to investigate the effect of web-based e-learning during quarantine of covid-19 pandemic on grade point average (GPA) among nursing student of Faletehan University. Data used were GPA of first semester (before covid-19 quarantine) and second semester (after covid-19 quarantine). GPA of bachelor nursing student were taken from student who in the first year (n=138), second year (n=119) and third year (n=91) respectively. Using paired t-test analysis, this study showed that there were found significant differences of GPA between first semester and second semester. Student who in the first year (mean = -.33, SD = .30, p = .000), second year (mean = -.04, SD = .22, p = .026) and third year (mean = -.36, SD = .16, p = .000) showed escalation of GPA, while student who in the fourth year (mean = .79, SD = .09, p = .000) showed degradation of GPA. Online learning only effective in learning cognitive area, for nursing student who in the last year of study especially, the majority of courses are practice at laboratory and at ward which is not suitable using online learning. Educational institution needs to develop internet-based learning platform with technology such as case-based virtual reality to facilitate student to learn nursing practice.Keywords: covid-19, e-learning, GPA, nursing, student
Development of a scalable, compact, safe, and secure educational robot arm that enables hands-on learning

There are few current robots that are suitable for education. Therefore, the purpose of this study is to develop a small, safe, and secure robot arm for education. The robot arm was made with a 3D printer. Arduino UNO was used as the microcontroller for control. By using MATLAB to control the robot arm, it was possible to control the robot arm using the knowledge gained in the lecture. To evaluate the educational effects of the robot arm developed in this study, we conducted an experiment with 41 students in a lecture on robotics. From the results of the questionnaire, it can be inferred that the students were able to deepen their understanding of the course content and were highly satisfied with the course.
FL-MFGM: A Privacy-Preserving and High-Accuracy Blockchain Reliability Prediction Model

Reliability prediction mechanism plays an important role in selecting reliable peers for blockchain users. Traditionally, reliability prediction approaches are performed by collecting a large amount of user data and conducting centralized training. Although these methods can achieve high accuracy, they may lead to malicious attacks by third-party services on users’ private data. As a privacy-preserving approach to addressing privacy leakage caused by centralized training, federated learning can enable users to learn a shared prediction model by providing gradients to a central server instead of raw data. However, uploading complete gradients directly may be exposed to a gradient attack and cause privacy leakage. To address the privacy data leakage from centralized training and prevent the attack threat from uploading gradients, we propose a privacy-preserving and high-accuracy blockchain reliability prediction model, namely FL-MFGM. Based on federated learning architecture, the model protects user privacy by uploading the gradients of matrix factorization. Specifically, in order to enhance the privacy-preserving capability of our model, we employ gradient compression with momentum correction for federated matrix factorization. To validate the effectiveness of our approach, sufficient experiments are conducted, which indicate that our approach can achieve higher prediction accuracy than other approaches with privacy-preserving based on federated learning architecture.
An Experimental Study of Think Pair Share (TPS) to Improve Writing Skill

Writing skill becomes one of the most important skills because it communicates students’ ideas and perception in written form. However, based on researcher’s observation, writing activity is not always successfully studied among the students because its complexity and challenges can be daunting for students. Writing should be learned with appropriate learning strategy. One of the techniques is called Think Pair Share (TPS) learning strategy was applied to improve students’ writing skills in class. According to Engkos Kosasih (2014), “Success in learning the materials is not only calculated by individual's ability but also working together in group” (p. 101). The goal of this study is to answer two research questions: (1) Does TPS improve the student’s writing skill in class XI IPS of SMA BOPKRI 2 Yogyakarta? (2) What are the students’ responses toward TPS in improving their writing skill in class XI IPS of SMA BOPKRI 2 Yogyakarta? The result of this study revealed that TPS learning strategy did improve their writing skills because it triggered positive changes in their learning attitude. They were encouraged to be more active, critical, and creative.
Text Summarization terhadap Berita Bahasa Indonesia menggunakan Dual Encoding

Text summarization or automatic text summarization can make readers receive information quickly without having to read the entire news text, so readers can get more time in reading other news texts. Making text summarization can use two techniques, namely, extractive and abstractive techniques. Abstractive techniques have the aim of producing summary sentences with concepts as humans take the essence of a document that is read. In this study, the author builds an abstractive summarization model using the Dual Encoding method consisting of GRU. The evaluation was carried out using K- Fold Cross Validation, the number of folds used was 5. By using K-Fold Cross Validation, the ROUGE- 1, ROUGE-2, and ROUGE-L values were 0.2127749, 0.119851, dan 0.1880595, respectively. For testing when using new data ROUGE-1, ROUGE-2, and ROUGE-L values were 0.3387776, 0.2395176, dan 0.3077376, respectively.
Securing ATE Using the DoD's Risk Management Framework

Information systems are subject to serious threats that can have adverse impacts on organizational operations and assets, individuals, as well as third parties by compromising the confidentiality, integrity, or availability of information being processed, stored, or transmitted by those systems. Successful attacks on systems can result in grave damage to the economic and security interests of those organizations. In the defense space, the DoD Risk Management Framework (RMF) can provide a foundation for an organization's cybersecurity protection strategy. Securing information systems is a shared responsibility between test companies and their customers. ATE suppliers serving the defense industry can assist customers in securing their Automatic Test Equipment (ATE) by implementing the first four steps of the RMF process. ATE customers further increase the security of their systems by working with test companies to understand what additional security controls they could implement to successfully perform the last two steps of the RMF process. ATE suppliers can implement the following steps for the systems they are supplying: (1) Security categorization; (2) Security control selection; (3) Security control implementation; and (4) Security control assessment. Steps that should be performed by ATE customers are: (5) System authorization; and (6) Continuous monitoring. Early integration of the RMF into the product development life cycle is one of, according to NIST 800–37, “the most cost-effective and efficient methods for an organization to ensure that its protection strategy is implemented” [1]. Test companies can ease customer implementation of the RMF by integrating a specific set of security controls into their own product development life cycles. ATE suppliers can develop a more secure supply chain, harden manufacturing and development processes, and apply operating system (OS) security controls. Finally, they can help customers understand the remaining steps of the RMF that could be implemented to secure the confidentiality, integrity, and availability of their information systems.
Blockchain Application Implementation Roadmap

Other than cryptocurrencies, blockchain applications have landed in multiple industries after 2019, such as finance, government affairs, energy and agriculture. Blockchain application implementation roadmap is the most helpful toolkit for any organization that wants to enter the blockchain field. Check the core application value framework of blockchain to figure out the needs, follow the blockchain application implementation path and use the blockchain application scenario selection methods, then a final conclusion of the suitability of applying blockchain will be drawn.
Chapter 12 Logic and the Ontology of Language

The main goal of this paper is to outline a general formal-logical theory of language construed as a particular ontological being. The theory itself will be referred to as an ontology of language, because it is motivated by the fact that language plays a special role: it reflects ontology, and ontology reflects the world. Linguistic expressions will be regarded as having a dual ontological status: they are to be understood as either concreta—i.e. tokens, in the sense of material, physical objects—or types, in the sense of classes of tokens—i.e. abstract objects. Such a duality will then be taken into account in the logical theory of syntax, semantics and pragmatics presented here. We point to the possibility of constructing the latter on two different levels, one stemming from concreta, construed as linguistic tokens of expressions, the other from their classes—namely types, conceived as abstract, ideal beings. The aim of this work is not only to outline such a theory with respect to the dual ontological nature of the expressions of language in terms that take into account a functional approach to language itself, but also to show that the logic based on it is ontologically neutral in the sense that it is abstracted from the level at which certain existential assumptions relating to the ontological nature of these linguistic expressions and their extra-linguistic ontological counterparts (objects) would have to be embraced.
A Secure Deterministic Remote State Preparation Via a Seven-Qubit Entangled Channel of an Arbitrary Two-Qubit State Under the Impact of Quantum Noise

As one of the most prominent subfields of quantum communication research, remote state preparation (RSP) plays a crucial role in quantum networks. Here, we present a deterministic remote state preparation scheme to prepare an arbitrary two-qubit state via a seven-qubit entangled channel created from Borras et al. state. Quantum noises are inherent to each and every protocol for quantum communication that is currently in use, putting the integrity of quantum communication systems and their dependability at risk. The initial state of the system was a pure quantum state, but as soon as there was any noise injected into the system, it transitioned into a mixed state. In this article, we discuss the six different types of noise models namely bit-flip noise, phase-flip noise, bit-phase-flip noise, amplitude damping, phase damping and depolarizing noise. The impact these noises had on the entangled channel may be seen by analysing the density matrices that have been altered as a result of the noise. For the purpose of analysing the impact of noise on the scheme, the fidelity between the original quantum state and the remotely prepared state has been assessed and graphically represented. In addition, a comprehensive security analysis is performed, demonstrating that the suggested protocol is safe against internal and external attacks.
Optimizing Random Forests: Spark Implementations of Random Genetic Forests

The Random Forest (RF) algorithm, originally proposed by Breiman [7], is a widely used machine learning algorithm that gains its merit from its fast learning speed as well as high classification accuracy. However, despite its widespread use, the different mechanisms at work in Breiman’s RF are not yet fully understood, and there is still on-going research on several aspects of optimizing the RF algorithm, especially in the big data environment. To optimize the RF algorithm, this work builds new ensembles that optimize the random portions of the RF algorithm using genetic algorithms, yielding Random Genetic Forests (RGF), Negatively Correlated RGF (NC-RGF), and Preemptive RGF (PFS-RGF). These ensembles are compared with Breiman’s classic RF algorithm in Hadoop’s big data framework using Spark on a large, high-dimensional network intrusion dataset, UNSW-NB15.
Analytical Study on Cyber Crimes in India

The internet is the essence of life in the 21st century. With the development in technology, the internet has become more intertwined with our lives than we can think of. Cyber Security plays an important role in the field of information technology. Securing the information have become one of the biggest challenges in the present day. Nowadays when people think about cyber security, the next thing that comes to their mind is cybercrimes, which has increased massively day to day. Cybercrime is criminal activity that either targets or uses a computer, a computer network or a networked device. Cybercrime is a serious threat nowadays. The paper examines on the various forms of cybercrimes, cybercrimes in India and the laws and the role of judiciary. It also includes cybercrimes from the aspect of international laws, and the social impact in India.
Testing

A lot is said about testing in information technology. There are three reasons for the attention testing has gained during the last decades:
Constrained Graph Convolution Networks Based on Graph Enhancement for Collaborative Filtering

Graph Convolutional Networks (GCNs) have gained much attention and have achieved excellent performance in many graph-based collaborative filtering (CF) tasks in recent years. Its success relies on a fundamental assumption that the original graph structure is reliable and consistent with the properties of GNNs. However, most original graphs can seriously impair model performance due to noise and data sparsity problems. In addition, for large user-item graphs, the explicit message passing in traditional GCNs slows down the convergence speed during training and weakens the training efficiency of the model. Based on this, we propose Constrained Graph Convolution Networks Based on Graph Enhancement for Collaborative Filtering (EL-GCCF). The graph initialization learning module integrates the structural and feature information in the graph by generating two graph structures. It enhances the original graph and effectively mitigates the noise problem. Second, the multi-task constrained graph convolution skips explicit message passing. It effectively mitigates the over-smoothing problem in training and improves the training efficiency of the model by using an auxiliary sampling strategy. Experimental results on two real datasets show that the EL-GCCF model outperforms many mainstream models and has higher training efficiency.