Reviewerly/exHarmony huggingface dataset:
Editorial for the Special Issue on Sustainable Cyber Forensics and Threat Intelligence

The papers in this special issue focus on sustainable cyber forensics and threat intelligence. Increasing societal reliance on interconnected digital systems, including smart grids and Internet of Things (IoT), made sustainable detection and investigation of threat actors among the highest priorities of any society. Scale and attack surface of modern networks mandate optimized deployment of limited cyber forensics and threat intelligence resources to detect and remove malicious actors in a timely manner. However, timely dealing with such a huge number of attacks is not possible without employment of artificial intelligence and machine learning techniques. When a significant amount of data is collected from or generated by different security monitoring solutions, intelligent big-data analytical techniques are necessary to mine, interpret and extract knowledge out of those data. The emerging field of cyber threat intelligence is investigating applications of artificial intelligence and machine learning techniques to perceive, reason, learn and act intelligently against advanced cyber attacks. A crucial success factor in implementation, installation and deployment of threat intelligence and cyber forensics capacities in modern networks is sustainability.
110 YEARS OF ENGINEERING SEISMOLOGY AND EARTHQUAKE ENGINEERING IN CROATIA

The lecture will give a concise overview of development of the disciplines of engineering seismology (and to a lesser extent, also earthquake engineering) in Croatia from a perspective of a seismologist.A significant part of the talk is dedicated to the role that Andrija Mohorovičić played in establishing the framework of research related to the seismic safety of buildings, not just in Croatia -his analyses, ideas, and suggestions on the ways to deal with the threats earthquakes pose to our buildings, although over 110 years old, still sound modern and fresh, and were in several aspects truly visionary.
Design and development a model to present practices for implementation cloud manufacturing system

The purpose of this paper is to identify and classify the main factors implementing the cloud manufacturing systems in the internet service providers company by fuzzy cognitive map methodology. Through expert opinions, 20 main factors were identified and classified based on the importance and then a fuzzy cognitive map approach was applied for obtaining the relationship between the factors, and all of the impact factors are outputs of expert opinion. The outcomes of the study highlighted that three factors, including customer scoring, R&D, and method development were the most important factors impressing the implementation cloud manufacturing systems. Present practices for implementation cloud manufacturing system is and the relationship between the main factors also impressing cloud manufacturing system by employing the fuzzy cognitive map approach in the Iranian internet service provider company. The model obtained in this study guides the managers to identify and classify the important factors of the cloud manufacturing and finally implement it successfully.
To the Question of the Psychology of the Performance of the Musician-Spiritual

this article pays the attention of teachers and students to some achievements of neurophysiology and psychology in the development of mental motor and human activity. The issue of the development of the mental and psychological process among students of musical instruments as a component of pedagogical activity for a long time in performing pedagogy was not properly touched upon. For a long time, the main goal in training performers in all specialties was the purely technical equipment of the student. Practice teachers were engaged only in the development of motor and motor techniques. It was believed that long mechanical repetitions of the musical text will contribute to the improvement of performing skills. Nowadays, the problem of the psychological activity of the spiritual musician, to which this work is devoted, is deeply studied.
De-rendering the World's Revolutionary Artefacts

Recent works have shown exciting results in unsupervised image de-rendering -- learning to decompose 3D shape, appearance, and lighting from single-image collections without explicit supervision. However, many of these assume simplistic material and lighting models. We propose a method, termed RADAR, that can recover environment illumination and surface materials from real single-image collections, relying neither on explicit 3D supervision, nor on multi-view or multi-light images. Specifically, we focus on rotationally symmetric artefacts that exhibit challenging surface properties including specular reflections, such as vases. We introduce a novel self-supervised albedo discriminator, which allows the model to recover plausible albedo without requiring any ground-truth during training. In conjunction with a shape reconstruction module exploiting rotational symmetry, we present an end-to-end learning framework that is able to de-render the world's revolutionary artefacts. We conduct experiments on a real vase dataset and demonstrate compelling decomposition results, allowing for applications including free-viewpoint rendering and relighting.
Confronting the Lies That Protect Racist Hate Speech: Towards Honest Hate Speech Laws in New Zealand and the United States

This Article provides a comparative critique of hate speech jurisprudence in New Zealand and the United States by building on insights from Critical Race Theory (CRT) scholars.My main argument is that neither of these liberal democracies protect the right to freedom of expression/ speech as they claim, but in fact dishonestly protect a right to "freedom of expression of racism" or "freedom of racist speech."They do this by telling lies that inflate the value of free expression/speech and diminish and dismiss the harms that hate speech inflicts on marginalized groups.To move towards honest hate speech laws in both jurisdictions, I propose a communications strategy that seeks to reframe hate speech from a free speech issue to a public health issue.This is in order to push for reforms that will enable the courts to better protect people of color from the physical, mental, psychological or spiritual harms of racist hate speech.
An Appraisal of the Approaches Opted to Evaluate Video Summarization Techniques

Video summarization is an effective tool which is instrumental in evaluating the contents of the video. Video summaries help in searching, browsing and retrieving heavy video files. This paper aims to provide a comprehensive review about the background work of video summarization and techniques employed by various researchers to summarize the videos. In addition to this, the paper provides a brief glimpse on the various applications of the existing video summarization techniques. The paper concludes with sharing the challenges faced by the existing video summarization techniques and the future scope expected in the video summarization domains.
Procurement of healthy lunch for adolescents to prevent iron deficiency anemia (IDA) in Prambanan school area

Objective: This study aim to reduce the rate of iron deficiency anemia in adolescents in the Prambanan area. People still thingking the symptoms of IDA are not serious and have a long-term impact. Iron Deficiency Anemia (IDA) has an impact on cognitive decline in adolescent girls (Hb <12 mg / dL), this condition causes their productivity to decline both in school and in the work area. By the time they grow up, it can cause a high risk of pregnancy and delivery LBW’s baby. The government still focuses on curative treatment rather than promotion or prevention. The highest ADB rate in Sleman district is in Prambanan subdistrict. Content : First, regulation about the day to drink bloods plus tablets was unaffective to decrease IDA in some areas. Second, according to article review of Sekiyama that the provision of lunch can increase consumption of animal protein and also increase hemoglobin and hematocrit levels in children which this article containing the similar program and become the foundation of this study. Beside of that provide this program also include campaign with a booklet containing a healthy breakfast and dinner’s menu as a source of knowledge for adolscents and their parents. Third, canteen should be renovation to provide properly kitchen and dining area than empower the canteen workers who have been selling fried snack, instant noodles or other unhealthy foods to cook healthy and nutritious food in canteen for student lunches. Fourth, the regulatory system is decentralized according to the nature of the region. Funding sorces can come from Corporate Social Responsibility (CSR), government subsidies and a funding system from wealthy parents of students. Conclusion: procurenment of healthy lunch and campaign use booklet will accepted by stakeholder because can decrease IDA in adolescents without causing conflict from food vendors around the school.
A survey to study about different Convolutional Neural Network on various image classifications

Multi label Image classification using Convolutional Neural Network is yet very difficult when it comes to performing. However, Single Label Image Classification can be performed easily and promisingly. As there are many categories of objects in a real world image, it becomes difficult to label them under various categories and also because of the lack of multi-label training image and high complexity. This paper surveys different Convolutional Neural Network (CNN) using Single Label Image Classification on which Multi Label Image Classification can be performed with High Accuracy. We have also learnt different trained Convolutional Neural Network architecture using UC MERCED Dataset which is essayed in this paper.
A Unified Cognitive Learning Framework for Adapting to Dynamic Environment and Tasks

Many machine learning frameworks have been proposed and used in wireless communications for realizing diverse goals. However, their incapability of adapting to the dynamic wireless environment and tasks and of self-learning limit their extensive applications and achievable performance. Inspired by the great flexibility and adaptation of primate behaviors due to the brain cognitive mechanism, a unified cognitive learning (CL) framework is proposed for the dynamic wireless environment and tasks. The mathematical framework for our proposed CL is established. Using the public and authoritative dataset, we demonstrate that our proposed CL framework has three advantages, namely, the capability of adapting to the dynamic environment and tasks, the self-learning capability and the capability of 'good money driving out bad money' by taking modulation recognition as an example. The proposed CL framework can enrich the current learning frameworks and widen the applications.
Deep ConvNet: Non-Random Weight Initialization for Repeatable Determinism, examined with FSGM

This paper presents a non-random weight initialization method in convolutional layers of neural networks examined with the Fast Gradient Sign Method (FSGM) attack. This paper's focus is convolutional layers, and are the layers that have been responsible for better than human performance in image categorization. The proposed method induces earlier learning through the use of striped forms, and as such has less unlearning of the existing random number speckled methods, consistent with the intuitions of Hubel and Wiesel. The proposed method provides a higher performing accuracy in a single epoch, with improvements of between 3-5% in a well known benchmark model, of which the first epoch is the most relevant as it is the epoch after initialization. The proposed method is also repeatable and deterministic, as a desirable quality for safety critical applications in image classification within sensors. That method is robust to Glorot/Xavier and He initialization limits as well. The proposed non-random initialization was examined under adversarial perturbation attack through the FGSM approach with transferred learning, as a technique to measure the affect in transferred learning with controlled distortions, and finds that the proposed method is less compromised to the original validation dataset, with higher distorted datasets.
Training students in Python programming skills and WPS wrapping for geoprocessing tasks by using examples of less commonly applied thematic mapping methods

Abstract. Python programming and the application of web services technology can be considered important skills for geomatics students when looking for employment later. A course on Web Processing Services (WPS) as part of the International Geomatics master programme at Karlsruhe University of Applied Sciences takes this into consideration while using the opportunity of making students aware of less-known thematic mapping methods. Students are introduced to automating a complex geoprocessing task and finally wrapping it into a WPS. It is achieved by following the phases of first conceptualizing the single steps before then using ArcGIS ModelBuilder and Python scripts in combination to build the processing chain. The alternate band map method serves as example in the hands-on exercise. Later students are asked to automate the generation of the geometries for the map face and/or map legend supporting other thematic mapping methods like the square density map or the absolute value raster map. The paper discusses the teaching approach and experiences made so far. Conceptualizing the automatization of a given complex geoprocessing problem proved to be the most challenging part for the students.
Questioning Facilities Management in Higher Education in Egyptian Context for Better Decision-Making Process of Funding Allocation: An Update

The quality of education is influenced by the managerialization of the universities, which refers to the introduction of substantial changes in the decision-making processes of the academic institutions, and the application of renewed information systems along with new managerial methodologies to restructure the organisational strategic relationships with stakeholders. This paper proposes a questionnaire to assess the importance of facilities in universities according to their financial budget consumed value. Semi Structured Interviews were conducted with the heads of logistic and financial departments in Egyptian universities, twenty public universities and twenty-three private universities, in order to identify criteria for the most significant university's facilities and appealing infrastructure that contributes to the quality of education.
Rancang Bangun Sistem Informasi Toko Mas (Studi Kasus : Toko Mas Anggrek)

Toko Mas Anggrek is a gold shop that was founded in 2011. Every day there are two transactions at this shop, namely sales transactions and purchase transactions. Every time a transaction occurs, it will be recorded manually in a book that has been provided. This manual recording has several risks, including books torn, damaged, wet, and errors in recording. Every time a transaction occurs, the orchid gold shop must also provide proof of the transaction to consumers. The proof of the transaction is in the form of paper which must be written directly by the seller manually. Therefore, a system was built that could manage transaction records and print transaction receipts automatically. Based on the results of the boundary value analysis, it was concluded that from the 25/25 test scenarios, all input forms were in accordance with the system format. Then in the user acceptance test, it was concluded that each test item tested was in accordance with the needs
Semantic Representation and Inference for NLP

Semantic representation and inference is essential for Natural Language Processing (NLP). The state of the art for semantic representation and inference is deep learning, and particularly Recurrent Neural Networks (RNNs), Convolutional Neural Networks (CNNs), and transformer Self-Attention models. This thesis investigates the use of deep learning for novel semantic representation and inference, and makes contributions in the following three areas: creating training data, improving semantic representations and extending inference learning. In terms of creating training data, we contribute the largest publicly available dataset of real-life factual claims for the purpose of automatic claim verification (MultiFC), and we present a novel inference model composed of multi-scale CNNs with different kernel sizes that learn from external sources to infer fact checking labels. In terms of improving semantic representations, we contribute a novel model that captures non-compositional semantic indicators. By definition, the meaning of a non-compositional phrase cannot be inferred from the individual meanings of its composing words (e.g., hot dog). Motivated by this, we operationalize the compositionality of a phrase contextually by enriching the phrase representation with external word embeddings and knowledge graphs. Finally, in terms of inference learning, we propose a series of novel deep learning architectures that improve inference by using syntactic dependencies, by ensembling role guided attention heads, incorporating gating layers, and concatenating multiple heads in novel and effective ways. This thesis consists of seven publications (five published and two under review).
Modeling and Analysis of the Effects of Aerobic Exercise on Human Cardiovascular Function in Haze Environment

The aerobic exercise in haze environment has great influence on the cardiovascular function of the human. Cardiovascular indicators of aerobic exercisers in haze environment are collected by establishing the human cardiovascular model. Taking the parameters of maximal oxygen uptake, maximum oxygen ventilation equivalent, oxygen pulse, maximum heart rate as the constraint index, and considering the diffusion intensity and penetration strength of haze, the mathematical expression of the effect of aerobic exercise in haze environment on human cardiovascular function is established. The neural network method is used to model cardiovascular system of the aerobic exercisers in haze environment, and the impact model of aerobic exercise in haze environment is established by Matlab & Simulink work platform simulation. The model analysis showed that the cardiovascular reflex after exercise in the two groups (43% in 86 cases) is significantly higher than that in the observation group (34% in 34 cases) (P<0.05), and the critical rate (7 cases) 14%) is significantly lower than that of the observation group (16 cases 32%) (P < 0.05). Compared with the low-pollution group, the resting heart rate, pulse pressure, and cardiac function index of the severely polluted exercise group are significantly different (P<0.05), and there is a significant difference compared with the heavily polluted control group (P<0.01). The systolic blood pressure is significantly higher than that of the heavily contaminated control group (P<0.05). Under severe pollution, the EDV of the exercise group is significantly higher than that of the control group, and the MVCF is significantly higher than that of the control group, while the MSER is significantly lower than that of the low-pollution control group (P < 0.05). The pulse output and heart rate of the heavily polluted exercise group are significantly higher than those of the severely contaminated control group (P<0.01); the infection rate of males in haze environment is not different from that of women. The conclusions show that although the aerobic exercise in the haze environment is more harmful, but the aerobic exercise in the severe haze weather has the positive effect on cardiovascular function, which can enhance the overall function of the cardiovascular system. Aerobic exercise in severe haze environment has better protective effect than aerobic exercise in haze.
KEMAMPUAN SISWA DALAM MENENTUKAN UNSUR INTRISIK PADA KARANGAN NARASI DI KELAS V SD GUGUS IV KECAMATAN SUKAJADI KOTA PEKANBARU

This research is a descriptive quantitative research. This study aims to describe the ability of students to determine intrinsic elements in narrative essays in class V of Elementary School Group IV, Sukajadi District, Pekanbaru City. Data collection techniques are objective tests. The validity of the research has been validated by experts. The sample in this study was a saturated sample so that the sample in this study were all fifth grade students of Elementary School Group IV Sukajadi District, Pekanbaru City with a total of 186 students who had been determined. The results show that the average indicator determines the theme of the narrative composition of 66.98 with a sufficient category, the average indicator determines the characters in the narrative composition of 72.95 with a good category, the average of the indicator determines the character's character on narrative essay is 70.05 with a good category, the average of the indicators found the background of the narrative essay is 69.08 with enough categories, the average on the indicator of finding the mandate in the narrative essay is 66.88 with a sufficient category. From these results the overall value of students' ability to determine intrinsic elements in the narrative essay as seen from the five indicators shows sufficient results. This shows that the ability of students in determining intrinsic elements in narrative essays in the Cluster IV Elementary School Sukajadi District Pekanbaru City is sufficient.
Deterministic generation of genuine tri-partite hybrid atom–photon entanglement through dissipation

The ability to deterministically generate genuine multi-partite entanglement is fundamental for the advancement of quantum information science. We show that the interaction between entangled twin beams of light and an atomic ensemble under conditions for electromagnetically induced transparency leads to the generation of genuine hybrid tri-partite entanglement between the two input fields and the atomic ensemble. In such a configuration, the system is driven through dissipation to a steady state given by the hybrid entangled state. To show the presence of the genuine hybrid entanglement, we introduce a new approach to treat the atomic operators that makes it possible to show a violation of a tri-partite entanglement criterion based on the properties of the two optical fields and collective properties of the atomic ensemble. Additionally, we show that while neither of the input optical fields exhibits single beam quadrature squeezing, as the fields propagate through the atomic medium, their individual quadratures can become squeezed and in some cases oscillate between the presence and absence of squeezing. Finally, we propose a technique to characterize the tri-partite entanglement through joint measurements of the fields leaving the atomic medium, making such an approach experimentally accessible.
On longest palindromic subwords of finite binary words

In a recent paper, Müllner and Ryzhikov posed a question about palindromic subwords of finite binary words which can be rephrased as follows: Given four equally long binary words w1,w2,w3,w4 of total length n, what is the size of a longest palindrome p=qqR such that (i) q is a subword of w1w2 and also of (w3w4)R or (ii) q is a subword of w2w3 and also of (w4w1)R? Müllner and Ryzhikov conjectured that the answer is at least n/2. We disprove this conjecture, constructing sequences of words w1,w2,w3,w4 such that the longest palindromes have size 15n/32+o(n). Additionally, we show that the longest palindromes have size at least 3n/8.