Reviewerly/exHarmony huggingface dataset:
IDENTIFIKASI KERENTANAN BANJIR BANDANG MENGGUNAKAN METODE AHP (ANALYTICAL HIERARCHY PROCESS) BERBASIS SIG DI SUB DAS AIR MULAK, KABUPATEN LAHAT (Identification of Flash Flood Vulnerability Using GIS-Based AHP (Analytical Hierarchy Process) Method in Air Mulak Sub-watershed, Lahat Regency)

The high rainfall for a long time resulted in flash floods that hit three villages in Lahat Regency, submerged dozens of residents' houses, damaged several public facilities, and cut off-road access. From this incident, spatial information is needed related to areas prone to flash floods so that the negative impacts can be minimized. This study aims to identify the vulnerability of flash floods in the Air Mulak Watershed. This research uses the AHP (Analytical Hierarchy Process) method for rational decision-making of all parameters, integrated with GIS (Geographical Information System) applications. The results of the consistency test for all parameters, the CR value of 0.97% is smaller than 10% and is considered consistent. The results of scoring all parameters are then overlaid by giving weight to each parameter, and it can be seen that the distribution of flash flood susceptibility in Air Mulak Sub-Watershed with a very high percentage of 0.20%, high class of 4.75%, moderate class of 15.78%, low at 43.50%, and very low at 35.78%. The results showed that the parameters that greatly affect flash flooding in the Air Mulak Sub-Watershed are altitude, river density, slope, and distance from the river because the area is where water accumulates from upstream of the river when it rains with high intensity.
Identification of cyberbullying by neural network methods

The authors consider in detail the identification of cyberbullying, which is carried out by fraudsters with the illegal use of the victim's personal data. Basically, the source of this information is social networks, e-mails. The use of social networks in society is growing exponentially on a daily basis. The use of social networks, in addition to numerous advantages, also has a negative character, namely, users face numerous cyber threats. Such threats include the use of personal data for criminal purposes, cyberbullying, cybercrime, phishing and cyberbullying. In this article, we will focus on the task of identifying trolls. Identifying trolls on social networks is a difficult task because they are dynamic in nature and are collected in several billion records. One of the possible solutions to identify trolls is the use of machine learning algorithms. The main contribution of the authors to the study of the topic is the use of the method of identifying trolls in social networks, which is based on the analysis of the emotional state of network users and behavioral activity. In this article, in order to identify trolls, users are grouped together, this association is carried out by identifying a similar way of communication. The distribution of users is carried out automatically through the use of a special type of neural networks, namely self-organizing Kohonen maps. The group number is also determined automatically. To determine the characteristics of users, on the basis of which the distribution into groups takes place, the number of comments, the average length of the comment and the indicator responsible for the emotional state of the user are used.
Performance Analysis of Multi-threaded Sorting for Unsorted Data Using Remote Procedure Call

Distributed procedures that use a message-passing approach communicate with one another by transferring bits of information in order to provide unique access to all available tasks. Almost every single modern computer now has a CPU with several cores, which provides additional computing capacity. Moreover, parallel execution is critical in the new age of big data to enhance performance to a reasonable threshold. Indeed, parallelization brings with it additional issues that must be tack-led. However, parallel algorithms are studied and contrasted in comparison to their consecutive equivalents. In our paper, we look at sorting the unsorted numbers in distributed systems. Multi-threaded methods and performance, along with the difficulty of defining time, affect speed as well. Following that, an experiment was conducted in which a set of algorithms was implemented as well as the data was gathered through benchmarking and testing. To establish the validity of parallelization, the data were inspected and analyzed with its associated source code. A review was carried out in order to obtain a better idea of the scope of sorting algorithms and parallel computing. In this paper, a multi-sorting threading algorithm approach has been conducted based on previous work. The primary idea behind the algorithm is simple as well as Remote Procedure Calls (RPCs) approach has been used.
Recognition of Geothermal Surface Manifestations: A Comparison of Machine Learning and Deep Learning

Abstract Geothermal surface manifestations (GSMs) are direct clues towards hydrothermal activities of a geothermal system in the subsurface and significant indications for geothermal resource exploration. It is essential to recognize various GSMs for potential geothermal energy exploration. However, there is a lack of work to fulfill this task using deep learning (DL), which has achieved unprecedented computer vision and image interpretation successes. This study aims to explore the feasibility and performance of using a DL model to conduct recognition of GSMs with photographs. An image dataset for GSM recognition named JYU-GSM was created by downloading images from the Internet, preprocessing, and labeling manually with high quality. The dataset consists of seven types of the GSMs, i.e., warm spring, hot spring, geyser, fumarole, mud pot, hydrothermal alteration, crater lake, and one type of none GSM, including 500 images of different photographs of each type. It was divided into training, testing, and validation subsets according to a certain ratio. The recognition results of the GoogLeNet model were compared with those of three machine learning (ML) algorithms, i.e., Support Vector Machine (SVM), Decision Tree (DT), and K-Nearest Neighbor (KNN), by using the assessment metrics of overall accuracy (OA), overall F 1 score (OF) and computational time (CT) for training and testing the models. The results show that the GoogLeNet model using transfer learning (GSM-Net) has significant advantages of accuracy and performance over the three ML classifiers with the highest OA, 91.25% for the validation and 88.25% for the test, the biggest OF, 0.91 for validation, and 0.88 for the test; and the fastest CT of 1.98 s (25.25 fps) and 1.71 s (29.24 fps) for validation and test, respectively. On the contrary, the three ML classifiers perform poorly for this task due to their low OA, small OF, and long CT. The test OA is 49.00%, 26.00% and 20.25%; the test OF is 0.49, 0.26 and 0.17; and the test CT is 7.71 s (6.49 fps), 6.75 s (7.41 fps) and 64.58 s (0.77 fps) for SVM, DT and KNN, respectively. It suggests that deep learning with a pretrained network be a feasible and preferred method to classify GSMs. However, traditional ML models need to optimize algorithms to be competent for the task. Hopefully, this study provides a reference paradigm to help promote further research on the geothermal field to use state-of-the-art deep learning.
Tutorial: Discourse Models for Interaction Design

This tutorial has the primary objective that participants will learn about an open and fully implemented approach to model interaction design with discourse models that we developed for specifying (classes of) dialogues, and to automated GUI generation from such models.The content includes Communicative Acts as the basic elements, as well as Adjacency Pair and RST relations for constructing the discourse models using these elements. It also includes an iterative-incremental process of user-interface generation using such models.The target audience can be students or educators primarily of software or systems engineering, or information systems. They can also be practitioners creating software, embedded systems or information systems with a user interface.
How virtual reality can help address and prevent bullying and cyberbullying?

In the last decade, technologies have made it possible to address different fields of research. Virtual reality is being used as a tool to study different processes, including bullying and cyberbullying. Applied virtual reality research as a methodology to combat bullying and cyberbullying in the learning environment presents challenges when searching for and incorporating adequate pedagogical strategies. The development must include multidisciplinary teams that focus on their field of specialization, to achieve the stated objectives. This article aims to analyze the research related to the application of virtual reality as a strategy to combat bullying and cyberbullying. Published works addressing virtual reality and bullying were identified. The selection was made in the period 2015-2021 and 50 papers published in this field were obtained.
Competitive information security investment under hacker knowledge dissemination

This paper discusses how two competitive firms invest in information security by constructing a differential game in which hacker knowledge dissemination and the law enforcement are considered. Feedback Nash equilibrium solution of information security investment rates and the resulting firm profits are derived, showing that inconsistent with common sense, firm profits may increase with hackers' evaluation rates of information assets and decrease with the law enforcement rate. Interestingly enough, I find that a higher growth rate of hacker knowledge dissemination sometimes increases firm profits. Meanwhile, I counter-intuitively demonstrate under certain circumstances that a higher efficiency of information security investment of inhibiting hacker knowledge dissemination may decrease firm profits and that a larger damage of hacker knowledge dissemination may increase firm profits.
A Novel Reservoir Computing Platform Constructed by Laser Controlled GaAs Resonator

In this work, a novel realization of the well known machine learning approach—reservoir computing (RC) using nanoelectromechanical system (NEMS) GaAs resonator is proposed. The specially designed resonator can interact with laser directly without cavity. Thanks to the richness of nonlinearity embedded in the light-mediated dynamical interacting process, the RC platform performs outstandingly even by eliminating the time delay feedback, and the conduction of nonlinear autoregressive moving-average (NARMA) task shows that the normalized mean square error (NMSE) can achieve <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2.1\,\,\times10$ </tex-math></inline-formula> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−3</sup> that is one order of magnitude lower than the recently reported result. Detailed theoretical modeling as well as numerical simulations on device’s nonlinear dynamics and RC’s hyperparameters are given. The interplay between the transient dynamics of the resonator and RC performance is particularly investigated.
Local Re-Encoding for Coded Matrix Multiplication

Matrix multiplication is a fundamental operation in various algorithms for big data analytics and machine learning. As the size of the dataset increases rapidly, it is now a common practice to distribute the computation on multiple servers. As straggling servers are inevitable in a distributed infrastructure, various coding schemes have been proposed to tolerate potential stragglers. However, as resources are shared with other jobs in a distributed infrastructure and their performance can change dynamically, the optimal way of encoding the input matrices is not static. So far, all existing coding schemes require encoding input matrices in advance, and cannot change the coding schemes or adjust their parameters flexibly. In this paper, we propose a framework that can change the coding schemes and/or their parameters by locally re-encoding the coded task on each server. We first present this framework for entangled polynomial codes, which changes the coding parameters with marginal overhead and saves job completion time. We then extend the framework for matrices with bounded entries, achieving a higher level of flexibility for local re-encoding while maintaining better numerical stability.
A Cross-Prefetcher Schedule Optimization Methodology

Prefetching offers the potential to significantly improve performance by speculatively loading application data so that it is available before it is needed. By their very nature, prefetching techniques are application behavior dependant. This implies that no universal prefetching solution exists. A combination of prefetching strategies need to be used to target a diverse set of applications. In this work, we develop the first comprehensive mathematical framework that allows a designer to better understand the prefetching opportunities of an application. We first use dynamic analysis to study the memory access behavior of an application and measure a series of metrics to both identify the optimized schedule, and estimate its achievable performance. To validate our model, we implement and evaluate three different prefetching strategies: helper threads, software prefetching and FPGA prefetching. We show that, for each individual scenario, our framework correctly generates the optimized schedule of prefetches and predicts the performance improvement with an accuracy of more than 95%. Using our framework, developers can choose the best prefetching strategy and parameters for their specific workload and use case.
A comparison between usability evaluation methods: A case study in the E-Commerce domain

Usability is a quality attribute that nowadays cannot be ignored by software development teams. Designing a software product that is difficult to use and understand, where the user is lost, and the achievement of the user's objectives is prevented, can cause users to choose to use similar alternatives. For this reason, Human-Computer Interaction specialists have developed a set of usability evaluation methods that allow problems to be identified to guarantee an appropriate level of user-friendliness of the application. However, there is a large disagreement between the authors as to which is the best method to use. In this study, the results of two of the most used methods to carry out usability evaluations of software products have been compared: the questionnaire and the heuristic evaluation. On the one hand, the questionnaire has turned out to be the method most used by the scientific community because it is easy to use and does not require much material preparation. However, the majority of questionnaires have been aimed at being answered by end-users. In a previous work, we developed a questionnaire for UX practitioners that in this case has been used for the evaluation of an electronic commerce application. On the other hand, heuristic evaluation is a method where specialists verify compliance with certain principles in the design of the graphical interfaces of a software product. It is also widely used like questionnaires but requires more planning and execution time. In this research, it is possible to evidence the differences in the type of problems that can be identified by each of the methods. In conclusion, it is possible to mention that although a percentage of problems are identified that can only be identified with one or the other method, the recommendation is to use both methods as the results complement each other.
Addressing new cyber risks in immersive reality

The purpose of this paper is to assess the cyber risks associated with the development of Augmented Reality (AR) and Virtual Reality (VR) technologies also in relation to their use in the Metaverse. AR and VR represent one of the most recent and relevant technological advances that have been able to take advantage of the changes brought about by the ongoing digital transformation driven by Industry 4.0, and the pandemic crisis. Moreover, these technologies are also becoming relevant for their potential in war scenarios and military exploitation.A common goal for both technologies is to improve knowledge and operational processes, overcoming the amount of information that is received by normal observation mechanisms, reducing the interactive distance between different variables. Consequently, the fields of application are various and regard all sectors, stemming from the national defense to the health, industrial and educational sectors. The culmination of these technologies, and their applications, is reached by the recent project announced by the founder of Facebook called "Metaverse". Some of the cybersecurity challenges will be similar to and will certainly enhanced others, such as the security and privacy risks related to social media, conference and streaming platforms and even internet browsing. The future of VR and AR spaces will involve a huge increase in new devices connecting to each other, through the creation of new apps and connection bridges, requiring the elaboration and the storage of huge quantities of data. Moreover, laws rarely reflect the rapidly evolving ways in which people interact with each other online. In this context, virtual assets can become as critical as physical ones, creating a worth to be protected. As the opportunities are all encompassing, so are the risks and vulnerabilities, such as identity theft, spying and social engineering. Security of confidential and market data, privacy and security of virtual assets are the most critical issues, linked to the volatility and reliability of providers with respect to assets.Unfortunately, new technologies are often developed and brought to market long before cybersecurity issues are addressed. An early identification of the main cyber risks inherent in the application of AR and VR technologies and the provision of framework for possible solutions to the various issues identified could address the Metaverse new challenge.
Synonymous light verb constructions and synonymy groups. A study of verb variability in Hungarian

Light verb constructions are typically interpreted as idiom-like, fixed patterns in the literature (see e.g. Forgács 2015). However, corpus data suggest that such constructions may also display variability, potentially inviting a study in terms of synonymy. For instance, this is the case with barátságot köt [lit. ‘tie a friendship’] ~ barátságot sző [lit. ‘weave a friendship’] ~ barátságot épít [lit. ‘build a friendship’], all meaning ‘make friends’.&#x0D; In the present case study, I explore patterns of the structure N-ba/be ‘into N’ + V, in particular feledésbe V ‘become forgotten’ (where feledésbe means ‘into oblivion’), homályba V ‘become obscure’ (where homályba means ‘into obscurity’) and sötétségbe V ‘become dark’ (where sötétségbe means ‘into darkness’). More specifically, I examine variants of these LVCs (e.g. homályba vész [lit. ‘be lost into obscurity’] ~ homályba szürkül [‘fade into oblivion’]), yielding synonym groups. Primarily using corpus data from the Hungarian National Corpus, I seek to find out how these three synonym groups are related semantically, and how their similarities and differences can be described by studying i. central and marginal patterns within them and ii. verbal lexemes (elfelejtődik ‘become forgotten’, elhomályosul ‘become obscure’ and elsötétedik ‘become dark’, respectively) with which they are synonymous.
On the approximation capability of GNNs in node classification/regression tasks

Graph Neural Networks (GNNs) are a broad class of connectionist models for graph processing. Recent studies have shown that GNNs can approximate any function on graphs, modulo the equivalence relation on graphs defined by the Weisfeiler--Lehman (WL) test. However, these results suffer from some limitations, both because they were derived using the Stone--Weierstrass theorem -- which is existential in nature, -- and because they assume that the target function to be approximated must be continuous. Furthermore, all current results are dedicated to graph classification/regression tasks, where the GNN must produce a single output for the whole graph, while also node classification/regression problems, in which an output is returned for each node, are very common. In this paper, we propose an alternative way to demonstrate the approximation capability of GNNs that overcomes these limitations. Indeed, we show that GNNs are universal approximators in probability for node classification/regression tasks, as they can approximate any measurable function that satisfies the 1--WL equivalence on nodes. The proposed theoretical framework allows the approximation of generic discontinuous target functions and also suggests the GNN architecture that can reach a desired approximation. In addition, we provide a bound on the number of the GNN layers required to achieve the desired degree of approximation, namely $2r-1$, where $r$ is the maximum number of nodes for the graphs in the domain.
Active Inference Tree Search in Large POMDPs

The ability to plan ahead efficiently is key for both living organisms and artificial systems. Model-based planning and prospection are widely studied in cognitive neuroscience and artificial intelligence (AI), but from different perspectives--and with different desiderata in mind (biological realism versus scalability) that are difficult to reconcile. Here, we introduce a novel method to plan in POMDPs--Active Inference Tree Search (AcT)--that combines the normative character and biological realism of a leading planning theory in neuroscience (Active Inference) and the scalability of tree search methods in AI. This unification enhances both approaches. On the one hand, tree searches enable the biologically grounded, first principle method of active inference to be applied to large-scale problems. On the other hand, active inference provides a principled solution to the exploration-exploitation dilemma, which is often addressed heuristically in tree search methods. Our simulations show that AcT successfully navigates binary trees that are challenging for sampling-based methods, problems that require adaptive exploration, and the large POMDP problem 'RockSample'--in which AcT reproduces state-of-the-art POMDP solutions. Furthermore, we illustrate how AcT can be used to simulate neurophysiological responses (e.g., in the hippocampus and prefrontal cortex) of humans and other animals that solve large planning problems. These numerical analyses show that Active Tree Search is a principled realisation of neuroscientific and AI planning theories, which offer both biological realism and scalability.
Rancang Bangun Sistem Pengaman Pintu Menggunakan RFID dan Fingerprint

In storing valuables such as electronics, money, jewelry, data and so on, it must be in a safe place. Because the safe factor is a social need of every human being. One of the triggers for crime such as theft, burglary or others is a lack of security, especially in the home environment. Another cause of security is that the door locking system still uses manual locks, making it less effective. Because the door is an important thing in a room, where we can always go in and out to store or put things. From these problems, a door guard using RFID (Radio Frequency Identification) or a fingerprint is needed to make the door safe from crime. This security system is designed using Arduino uno r3 ATmega 328 as a microcontroller equipped with an component of RFID reader and fingerprint sensor as well as a door lock solenoid to access the door. By scanning a fingerprint or scanning an RFID card, the data will then be processed and matched with data that has been registered or stored in the database to be able to access the door. Based on the results of the research that has been done, the value of fingerprint accuracy from 3 tests is 80,6%. The safety system can work quite well according to the design..