Reviewerly/exHarmony huggingface dataset:
Social Networks and the Exploitation of Digital Data From the Empire of Surveillance to the Economy of Attention

This piece of work entitled “Social networks and the exploitation of digital data: From the empire of .surveillance to the economy of attention”, addresses the digital environment and social media, which depend on various software and applications, and interaction devices that control human existence and communication. In addition to look the effects they have on the real world of individuals, especially with the emergence of forms of hidden domination, and a growing practice of administrative and voluntary slavery. To reach these researches aims, descriptive analytical approach was adapted, by invoking the studies and contributions of researchers and thinkers and their cognitive and critical conclusions, to elucidate the forms of exploitation of digital data and its use in the field of comprehensive global monitoring and attention economy. The main conclusion of the study is that there is digital management and exploitation of various comprehensive data of individuals who reveal their passions, emotions, consumer behaviors and intellectual activities, for the purpose of using them in the political, commercial and security fields. This constitutes a threat to the private life of humanity and individual liberties as well as the human condition.
Educational Tourism in Jordan the Problematic Concept and Motivational Factors

Educational tourism is one of the promising sectors that contribute to revitalizing the base of the national economy, and providing job opportunities. This study aimed to identify the concept and structure of educational tourism and to know the motivating factors to attract non-Jordanian students to study in Jordan, and to achieve the objectives of the study, the descriptive analytical approach was used and the use of appropriate statistical methods to analyze the hypothesis of the study, and the study concluded that there is an effect of the study variables on the decision to choose students other than Jordanians to study in Jordan, as it was found that the most influential factors in the decision to choose students for Jordanian educational institutions are social, environmental, legislative and legal factors. Therefore, the study recommends the necessity of joining efforts of private and governmental universities to exploit the competitive and comparative advantages of Jordanian educational institutions to stimulate an attractive environment for students from outside Jordan. And the need to study the effect of the relatively high costs of education in Jordan on the attractiveness of educational institutions for international students.
Capacity Building Needs of Home Economics Lecturers for Computer Assisted Teaching of Pattern Drafting for Entrepreneurship in South East Nigeria

This study looked at the capacity development requirements of Home Economics teachers in South East Nigeria for computer-assisted pattern drafting training. A research question and a hypothesis led the investigation. Ex-post facto research was used in this study. A total of 131 Home Economics lecturers from universities, polytechnics, and colleges of education in South East Nigeria were included in the study. Because of its manageable size, the complete population was investigated, so there was no sampling. Capacity Building Needs for Computer Assisted Teaching Questionnaire (CBNCATQ) was used for data collection. The instrument was face validated and tested for internal consistency using Cronbach Alpha Coefficient. Data obtained from the respondents were analyzed using Statistical Package for Social Science (SPSS). Percentage, mean scores and standard deviation were used to answer the research question while the hypothesis was tested with a t-test at a 0.05 level of significance. The results of this study showed that training on the use of computers, ability to use various pattern drafting materials and equipment, availability of electricity to power equipment, and ability to follow basic principles of pattern drafting among others were capacity building needs of lecturers. Based on the findings, it was recommended among others that lecturers should be given opportunities for in-service training on computer usage and workshops should be organized for the lecturers on the benefits of using computers in teaching.
The Speech Act of Promising in Political Speeches: A Case Study of Obama’s Speeches

This study tried to present a pragmatic analysis of the act of promising made by Barack Obama during the presidential campaigns of 2008 and 2012. It utilized a qualitative approach and the data were collected through speeches. However, this study also employed a quantitative method to calculate and present the frequency of promising utterances and performative verbs and their percentages. The researcher used Searle’s taxonomy to analyze Obama’s speeches. The findings showed that assuring, confirming, and reconfirming were the most frequent intentions of the speaker’s speeches during the two election campaigns. Further analysis revealed that Obama used the performative verb ‘‘promise’’ in 7 samples. Moreover, the study showed that the speaker used the modal verb ‘‘will’’ and the form ‘‘be going to + infinitive’’ as ways of promising instead of the performative verb ʻʻpromise.’’ Based on the results of the study, it can be concluded that further studies should be conducted in other contexts to obtain more comprehensive data regarding the act of promising.&#x0D;
The Lothbrok approach for SPARQL Query Optimization over Decentralized Knowledge Graphs

While the Web of Data in principle offers access to a wide range of interlinked data, the architecture of the Semantic Web today relies mostly on the data providers to maintain access to their data through SPARQL endpoints. Several studies, however, have shown that such endpoints often experience downtime, meaning that the data they maintain becomes inaccessible. While decentralized systems based on Peer-to-Peer (P2P) technology have previously shown to increase the availability of knowledge graphs, even when a large proportion of the nodes fail, processing queries in such a setup can be an expensive task since data necessary to answer a single query might be distributed over multiple nodes. In this paper, we therefore propose an approach to optimizing SPARQL queries over decentralized knowledge graphs, called Lothbrok. While there are potentially many aspects to consider when optimizing such queries, we focus on three aspects: cardinality estimation, locality awareness, and data fragmentation. We empirically show that Lothbrok is able to achieve significantly faster query processing performance compared to the state of the art when processing challenging queries as well as when the network is under high load.
Pattern matching under DTW distance

In this work, we consider the problem of pattern matching under the dynamic time warping (DTW) distance motivated by potential applications in the analysis of biological data produced by the third generation sequencing. To measure the DTW distance between two strings, one must "warp" them, that is, double some letters in the strings to obtain two equal-lengths strings, and then sum the distances between the letters in the corresponding positions. When the distances between letters are integers, we show that for a pattern P with m runs and a text T with n runs: 1. There is an O(m + n)-time algorithm that computes all locations where the DTW distance from P to T is at most 1; 2. There is an O(kmn)-time algorithm that computes all locations where the DTW distance from P to T is at most k. As a corollary of the second result, we also derive an approximation algorithm for general metrics on the alphabet.
One-class Recommendation Systems with the Hinge Pairwise Distance Loss and Orthogonal Representations

In one-class recommendation systems, the goal is to learn a model from a small set of interacted users and items and then identify the positively-related user-item pairs among a large number of pairs with unknown interactions. Most previous loss functions rely on dissimilar pairs of users and items, which are selected from the ones with unknown interactions, to obtain better prediction performance. This strategy introduces several challenges such as increasing training time and hurting the performance by picking "similar pairs with the unknown interactions" as dissimilar pairs. In this paper, the goal is to only use the similar set to train the models. We point out three trivial solutions that the models converge to when they are trained only on similar pairs: collapsed, partially collapsed, and shrinking solutions. We propose two terms that can be added to the objective functions in the literature to avoid these solutions. The first one is a hinge pairwise distance loss that avoids the shrinking and collapsed solutions by keeping the average pairwise distance of all the representations greater than a margin. The second one is an orthogonality term that minimizes the correlation between the dimensions of the representations and avoids the partially collapsed solution. We conduct experiments on a variety of tasks on public and real-world datasets. The results show that our approach using only similar pairs outperforms state-of-the-art methods using similar pairs and a large number of dissimilar pairs.
Rethinking Conversational Recommendations

Conversational recommender systems (CRS) dynamically obtain the users' preferences via multi-turn questions and answers. The existing CRS solutions are widely dominated by deep reinforcement learning algorithms. However, deep reinforcement learning methods are often criticized for lacking interpretability and requiring a large amount of training data to perform.
Accelerating differential evolution algorithm with Gaussian sampling based on estimating the convergence points

In this paper, we propose a simple strategy for estimating the convergence point approximately by averaging the elite sub-population. Based on this idea, we derive two methods, which are ordinary averaging strategy, and weighted averaging strategy. We also design a Gaussian sampling operator with the mean of the estimated convergence point with a certain standard deviation. This operator is combined with the traditional differential evolution algorithm (DE) to accelerate the convergence. Numerical experiments show that our proposal can accelerate the DE on most functions of 28 low-dimensional test functions on the CEC2013 Suite, and our proposal can easily be extended to combine with other population-based evolutionary algorithms with a simple modification.
A Unified Filter for Fusion of Multiple Inertial Measurement Units

Navigation plays a vital role in the ability of autonomous surface and underwater platforms to complete their tasks. Most navigation systems apply a fusion between inertial sensors and other external sensors, such as global navigation satellite systems, when available, or a Doppler velocity log. In recent years, there has been increased interest in using multiple inertial measurement units to improve navigation accuracy and robustness. State of the art examples include the virtual inertial measurement unit (VIMU) and the federated extended Kalman filter (FEKF). However, each of those approaches has its drawbacks. The VIMU does not improve the sensor biases, which constitute a significant source of error, especially in low-cost inertial sensors. While the FEKF does improve accuracy, it models uncertainty propagation empirically. If not modeled correctly, this can cause the global solution to diverge. To cope with those shortcomings, we propose and derive a new filter structure for multiple inertial sensors data fusion: the unified extended Kalman filter (UEKF). In addition, to cope with the multiple equal bias variance estimation problem we offer the bias variance redistribution algorithm. Our filter design enables bias estimation for each of the inertial sensors in the system, improving its accuracy and allowing the use of a varying number of inertial sensors. We show that our UEKF performs better than other state of the art, multiple inertial sensor filters using real data recorded during sea experiments.
Clustered Autoencoded Variational Inverse Reinforcement Learning

Abstract Variational Auto-Encoder (VAE) is a handy and computationally friendly Bayesian tool for Inverse Reinforcement Learning (IRL) problems, with the native setting of absent reward functions in a Markov Decision Process (MDP). However, recent works mainly deal with single reward, which turn out to be insufficient for complex dynamic environments with multiple demonstrators of various characteristics (hence multiple reward functions). This paper extends the dimensionality of reward (from ℝ to ℝ K ) by incorporating a latent embedding and clustering step on top of a scalable Bayesian IRL model, which enhances her applicability to multi-reward scenarios. We introduce our method, Clustered Autoencoded Variational Inverse Reinforcement Learning (CAVIRL), which is able to approximate multiple posterior reward functions and learn the corresponding policies for experts of various characteristics and skills. As a by-product, the proposed model also thrives to determine the number of clusters K on her own, as opposed to the competing multi-reward imitation learning models that require K to be prespecified. We trained the proposed model within a grid world with multiple types of players, where we achieved 100% correctness in determining the number of players’ types and 80%-83.9% match between the model-learned policies and the players’ demonstrations from the data.
SecureBERT: A Domain-Specific Language Model for Cybersecurity

Natural Language Processing (NLP) has recently gained wide attention in cybersecurity, particularly in Cyber Threat Intelligence (CTI) and cyber automation. Increased connection and automation have revolutionized the world's economic and cultural infrastructures, while they have introduced risks in terms of cyber attacks. CTI is information that helps cybersecurity analysts make intelligent security decisions, that is often delivered in the form of natural language text, which must be transformed to machine readable format through an automated procedure before it can be used for automated security measures. This paper proposes SecureBERT, a cybersecurity language model capable of capturing text connotations in cybersecurity text (e.g., CTI) and therefore successful in automation for many critical cybersecurity tasks that would otherwise rely on human expertise and time-consuming manual efforts. SecureBERT has been trained using a large corpus of cybersecurity text.To make SecureBERT effective not just in retaining general English understanding, but also when applied to text with cybersecurity implications, we developed a customized tokenizer as well as a method to alter pre-trained weights. The SecureBERT is evaluated using the standard Masked Language Model (MLM) test as well as two additional standard NLP tasks. Our evaluation studies show that SecureBERT\footnote{\url{https://github.com/ehsanaghaei/SecureBERT}} outperforms existing similar models, confirming its capability for solving crucial NLP tasks in cybersecurity.
AUDIT KEAMANAN SISTEM INFORMASI PERPUSTAKAAN: STUDI KASUS DI UNIVERSITAS NAHDLATUL ULAMA AL GHAZALI CILACAP

Nahdlatul Ulama University Al Ghazali Cilacap began to utilize Information and Communication Technology (ICT) to support existing processes so as to produce accurate and fast information. In the implementation of Information and Communication Technology (ICT) governance, information is a very important asset for a company, both information related to management, finance, reports and other information. Leaks, damage, inaccuracies. The aim is to find out the extent of the completeness and maturity of Information Technology at Nahdlatul Ulama Al Ghazali University, Cilacap. However, they do not yet know whether their framework has met the requirements for the level of completeness and maturity of the application of the ISO 27001 standard. For this reason, it is necessary to carry out an information system security audit to determine the current condition compared to the expected condition. In the process, the audit is carried out based on the ISO 27001 Standard which is applied in the form of an evaluation of the KAMI Index. A thorough assessment was carried out on five areas of information security using the US Index version 2.3 template from the Ministry of Communications and Information Technology (Kominfo). The audit results are in the form of recommendations for evaluating the completeness and maturity of the information security framework based on the ISO 27001 standard.
Different coefficients for studying dependence

Through computer simulations, we research several different measures of dependence, including Pearson's and Spearman's correlation coefficients, the maximal correlation, the distance correlation, a function of the mutual information called the information coefficient of correlation, and the maximal information coefficient (MIC). We compare how well these coefficients fulfill the criteria of generality, power, and equitability. Furthermore, we consider how the exact type of dependence, the amount of noise and the number of observations affect their performance.
Android-Based Learning Media in Supporting the Recovery of Science Learning Class VII

Abstrak: Penelitian yang dilakukan bertujuan menjabarkan efektivitas produk pengembangan media berbasis android dalam mendukung pemulihan pembelajaran sistem organisasi kehidupan yang dianalisis oleh ahli, guru dan siswa. Rancangan penelitian ini berbasis pengembangan berorientasi pada model Borg dan Gall yang direduksi menjadi lima tahapan. Hasil penelitian meliputi merancang dan membangun media pembelajaran IPA melalui lima tahapan. Penilaian media dari ahli isi dan ahli media memberikan tanggapan yang sangat baik. Hal yang sama dengan hasil pengujian media kepada guru dan siswa, kategori sangat baik diberikan oleh guru terhadap media dengan persentase 99 persen dan uji coba kelas memberikan persentase 97 persen dengan kategori sangat baik. Media yang telah dikembangkan memberikan efektivitas melalui uji coba pra eksperimen terhadap hasil belajar peserta didik dan media pembelajaran efektif membantu pemulihan proses pembelajaran pada materi sistem organisasi kehidupan. Abstract: This research aims to describe the effectiveness of android-based media development products in supporting the restoration of learning organizational life systems analyzed by experts, teachers, and students. This research design is development-oriented based on the Borg and Gall model, reduced to five stages. The research results include designing and building science learning media through five steps. Media assessments from content experts and media experts gave an excellent response. The same thing with the results of media testing for teachers and students; the teacher provided the outstanding category to the media with a percentage of 99 percent, and the class trial gave a rate of 97 percent with an exceptional variety. The media that has been developed provides effectiveness through pre-experimental practices on student learning outcomes, and effective learning media helps restore the learning process of life organization systems.
Development of Early Childhood Dance Pattern Module with Augmented Reality (AR)

Abstrak: Media pembelajaran yang dapat menjembatani dunia maya dan dunia nyata menjadi kebutuhan dalam meningkatkan pemahaman mahasiswa tentang pola lantai tari anak usia dini. Penelitian ini bertujuan untuk mengembangkan modul pola tari anak usia dini dengan teknologi augmented reality yang layak dan praktis. Model ADDIE yang meliputi analisis, desain, pengembangan, dan implementasi menjadi model pengembangan yang digunakan oleh peneliti. Hasil penelitian ini adalah: (1) penilaian kelayakan media oleh ahli media dan ahli materi berada pada kategori layak dengan prosentase 75 persen dan 83 persen; (2) penilaian kepraktisan oleh pengguna yaitu mahasiswa dari dimensi kebermanfaatan media 87,75 persen, dimensi desain 86,7 persen dan dimensi bahasa 91,8 persen sehingga modul dikatakan praktis. Maka, modul pola tari anak usia dini dengan teknologi augmented reality yang dikembangkan layak dan praktis untuk digunakan pada proses pembelajaran mahasiswa pada matakuliah seni tari anak usia dini.Abstract: Learning media that can bridge the virtual world and the real world is a necessity in increasing students' understanding of dance floor patterns for early childhood. The research aims to develop a dance pattern module for early childhood with augmented reality that is feasible and practical. The ADDIE model, which includes analysis, design, development, and implementation, becomes the development model used by the researchers. The results of this study are: (1) media feasibility assessment by media experts and material experts is in the appropriate category with a percentage of 75 percent and 83 percent; (2) the practicality assessment by users, namely students from the media usefulness dimension, is 87.75 percent, the design dimension is 86.7 percent, and the language dimension 91.8 percent so that the module is said to be practical. In conclusion, the developed early childhood dance pattern module with augmented reality is feasible and practical to use in the student learning process in early childhood dance art courses.
Net-Zero Energy Building Using Metaheuristics in Melbourne City

In this paper, a net-zero energy building setup over an existing building in the suburbs of Melbourne city is proposed. Based on the power consumption, a solar panel system considering minimum size is done to determine the optimum number of solar panels required for the building. An anisotropic method is utilized to model the incident radiation. For optimization purpose, various metaheuristic optimization methods (i.e., particle swarm optimization, water cycle algorithm, and neural network algorithm) are used to maximize the incident radiation on solar panels by optimizing tilt angle of solar modules to reduce the number of solar panels required for the system. Statistical optimization results prove the efficiency of applied optimizers and show that seasonal adjustment can increase the incident radiation per meter squared significantly and simultaneously decrease the required number of solar panels (reduction in equipment costs).
Implementation of Zoning-Based New Student Admission System Policy at the High School Level in Bandung City, West Java, Indonesia

The purpose of this study was to determine the implementation of the Zoning System policy in the Admission of New Students in Bandung City at the senior high school (SMA) level and to see how the implementation of the policy was implemented at the high school level in Bandung, West Java. The research method used is descriptive research method with a qualitative approach. In the research conducted, primary data and secondary data obtained through literature study techniques, field studies (interviews and observations) were used, besides that purposive techniques were also used to determine informants in the study. The results of this study were analyzed using the Miles and Huberman model. The results of the study indicate that the government has not been optimal in its implementation, this is viewed from several aspects, namely, organizations where the government has used its resources, interpretations or ways of delivering government that are not easy to understand, and its implementation is in accordance with the direction of goals, and objectives, aspects of the principle Therefore, there is a need for policy consistency and policy evaluation in regulations related to this zoning system in order to realize the objectives of this policy.