Reviewerly/exHarmony huggingface dataset:
Students activities organization technology in environmental communities

Environmental education in modern world is becoming an essential element in the harmonious development of society, and each individual in particular. Ecological problems lead to irreversible processes that negatively affect the whole world, therefore, environmental awareness and conscious consumption must be developed already at the college age. The purpose of the article is to study the experience of organizing students' activities in environmental communities to develop an environmental culture and responsibility towards the environment. University is stated to be actively developing in the Ecological Movement field and supports the principles of the “Green” Universities Association in Russia. The article shows a part of students’ survey conducted to identify the level of environmental culture development, the level of consumption awareness; and environmental awareness. The survey was organized in 2016 (before the active students’ involvement in environmental activities) and in 2017 and 2018 (after the students’ environmental movements development). The article considers the results of students’ environmental projects. Discussions, interactive and information technologies that were actively used contributed to the students’ activities improvement. Large-scale discussions were held among all participants to solve common issues. Partially, the discussions were organized with the help of electronic means. As part of the university environmental activities, students made projects that were subsequently presented at the university competition. The analysis of the environmental projects presentations conducted over several years has shown that the number of projects has increased significantly and has reached the regional and national level. The research found the environmental indicators growth from 2016 to 2018.
Detection of Metasploit Attacks Using RAM Forensic on Proprietary Operating Systems

Information technology has become an essential thing in the digital era as it is today. With the support of computer networks, information technology is used as a medium for exchanging data and information. Much information is confidential. Therefore, security is also essential. Metasploit is one of the frameworks commonly used by penetration testers to audit or test the security of a computer system legally, but it does not rule out the possibility that Metasploit can also be used for crime. For this reason, it is necessary to carry out a digital forensic process to uncover these crimes. In this study, a simulation of attacks on Windows 10 will be carried out with Metasploit. Then the digital forensics process uses live forensics techniques on computer RAM, where the computer RAM contains information about the processes running on the computer. The live forensic technique is important because information on RAM will be lost if the computer is off. This research will use FTK Imager, Dumpit, and Magnet RAM Capture as the RAM acquisition tool and Volatility as the analysis tool. The results of the research have successfully shown that the live forensics technique in RAM is able to obtain digital evidence in the form of an attacker's IP, evidence of exploits/Trojans, processes running on RAM, operating system profiles used and the location of the exploits/Trojan when executed by the victim.
Deep Learning-Based Modulation Detection for NOMA Systems

Since the signal with strong power need be demodulated first for successive interference cancellation (SIC) receiver in non-orthogonal multiple access (NOMA) systems, the base station (BS) need inform the near user terminal (UT), which has allocated higher power, of the far UT's modulation mode.To avoid unnecessary signaling overhead of control channel, a blind detection algorithm of NOMA signal modulation mode is designed in this paper.Taking the joint constellation density diagrams of NOMA signal as the detection features, the deep residual network is built for classification, so as to detect the modulation mode of NOMA signal.In view of the fact that the joint constellation diagrams are easily polluted by high intensity noise and lose their real distribution pattern, the wavelet denoising method is adopted to improve the quality of constellations.The simulation results represent that the proposed algorithm can achieve satisfactory detection accuracy in NOMA systems.In addition, the factors affecting the recognition performance are also verified and analyzed.
Effects of individuality, education, and image on visual attention: Analyzing eye-tracking data using machine learning

Machine learning, particularly classification algorithms, constructs mathematical models from labeled data that can predict labels for new data. Using its capability to identify distinguishing patterns among multi-dimensional data, we investigated the impact of three factors on the observation of architectural scenes: Individuality, education, and image stimuli. An analysis of the eye-tracking data revealed that (1) a velocity histogram was unique to individuals, (2) students of architecture and other disciplines could be distinguished via endogenous parameters, but (3) they were more distinct in terms of seeking structural versus symbolic elements. Because of the reverse nature of the classification algorithms that automatically learn from data, we could identify relevant parameters and distinguishing eye-tracking patterns that have not been reported in previous studies.
Covid-19: How does the NHS test and trace service work?

After numerous calls from public health experts since national contact tracing ended in March, the UK government has reimplemented the measure for covid-19 as of 28 May. The new NHS test and trace service is part of the government’s move to ease lockdown measures and reopen parts of the economy.When people show symptoms of covid-19 they must isolate at home, along with the rest of the household, and order a test using the www.nhs.uk/coronavirus website or by calling 119. If people with symptoms then test positive they must complete the remainder of their seven day self-isolation, and their household must complete the 14 day isolation period. However, if the test is negative no one needs to isolate.If people do test positive the NHS test and trace service will text, email, or call them with instructions on how to share details of people they have had close contact with. This will apply to contacts made and places visited two days before and seven days since the onset of symptoms.The tracers will contact the people to tell them that they have been in close contact with someone who has tested positive, but they will not provide the name or details of this person. The alert will usually come by text, email, or phone call, but they may also come by post if needed. The contacts will be told to begin self-isolation for 14 days from their last contact with the person who tested positive. The contact’s household members do not need to self-isolate but should avoid contact with …
Trustworthy AI Needs Unbiased Dictators!

EU Draft Ethics guidelines for Trustworthy AI [8] has been proposed to promote ethical, lawful and robust AI solutions. In this article, we entertain the systemic issues and challenges of any development of the proposed guidelines.
Temporal Knowledge Graph completion based on time series Gaussian embedding

Knowledge Graph (KG) embedding has attracted more attention in recent years. Most KG embedding models learn from time-unaware triples. However, the inclusion of temporal information beside triples would further improve the performance of a KGE model. In this regard, we propose ATiSE, a temporal KG embedding model which incorporates time information into entity/relation representations by using Additive Time Series decomposition. Moreover, considering the temporal uncertainty during the evolution of entity/relation representations over time, we map the representations of temporal KGs into the space of multi-dimensional Gaussian distributions. The mean of each entity/relation embedding at a time step shows the current expected position, whereas its covariance (which is temporally stationary) represents its temporal uncertainty. Experimental results show that ATiSE chieves the state-of-the-art on link prediction over four temporal KGs.
Gamifikasi Pembelajaran Sosiologi Materi Ragam Gejala Sosial sebagai Inovasi Pembelajaran Sosiologi Yang Aktif dan Menyenangkan

Abstrak: Desain Pembelajaran Gamifikasi adalah sebuah desain pembelajaran yang dibuat menggunakan prinsip-prinsip game. Prinsip-prinsip game tersebut antara lain, Misi, Tantangan, Level , Poin , Lencana Bintang, dan Leadboard . Desain pembelajaran ini dikembangkan dalam mata pelajaran Sosiologi pada pokok bahasan Ragam Gejala Sosial dalam Masyarakat. Tujuan dari pengembangan desain pembelajaran ini untuk menciptakan proses pembelajaran sosiologi yang aktif dan menyenangkan. Penerapan prinsip-prinsip game dalam pembelajaran ini dirasa dapat menjadi solusi dari adanya permasalahan bahwa proses pembelajaran sosiologi terlalu membosankan dan tidak menarik.. Metode penelitian ini menggunakan ADDIE ( Analysis, Design, Development, Implementation, Evaluation ). Dalam penerepanya desain pembelajaran menggunakan prinsip gamifikasi pada mata pelajaran sosiologi layak di gunakan dalam pembelajaran. Dalam uji coba kelompok besar desain pembelajaran menggunakan prinsip gamifikasi pada mata pelajaran sosiologi dinyarakan valid dan layak digunakan. Desain pembelajaran menggunakan prinsip gamifikasi pada mata pelajaran sosiologi merupakan salah satu solusi untuk menciptakan pembelajaran sosiologi yang aktif dan menyenangkan. Oleh karena itu, desain pembelajaran menggunakan prinsip gamifikasi pada mata pelajaran sosiologi ini memiliki potensi menjadi pedoman guru dalam melakukan proses pembelajaran. Abstract: Gamification Learning Design is a learning design that is created using the principles of the game. The principles of the game include Mission, Challenge, Level, Points, Star Badges, and Leaderboard. This learning design was developed in the subject of Sociology on the subject of the Variety of Social Symptoms in Society. The purpose of developing this learning design is to create an active and fun sociology learning process. The application of game principles in learning is felt to be a solution to the problem that the sociology learning process is too boring and unattractive. Besides, the learning design is designed so that students can interact directly in the scope of the classroom and school environment so that students get direct social experience. This research method uses ADDIE (Analysis, Design, Development, Implementation, Evaluation). In the adoption of learning design using the principle of gamification in sociology, subjects are appropriate for use in learning. In testing large groups of learning designs using the principle of gamification in sociology subjects are said to be valid and feasible to use. Learning design using the principle of gamification in sociology subjects is one of the solutions for creating active and fun sociology learning. Therefore, the learning design using the principle of gamification in these sociology subjects has the potential to guide teachers in conducting the learning process .
Algorithmized Modelling, Simulation and Validation of Clearness Index in Four Regions of Uganda

Uganda as a developing nation, needs to exploit her renewable energy potential to maximum through extensive research in the field of solar engineering. Thus, this work tries to build up a comprehensive clearness index model at three categories; national, regional and district on periodic (monthly) and non-periodic (yearly) basis for this purpose. Approximately, this quest is proceeded with the acquisition of quadragenarious data from both National Aeronautics and Space Administration (NASA) and the on-station data from four locations in Uganda. The data were arranged in the structural order of the proposed clearness index (CI) model in the MS-Excel spread sheet and later exported to OriginLab to obtain the coefficients of the CI models. The statistical inference; the coefficient of determination (R^2), were all tending to unity (1) which indicates the strength of the models obtained. It is observed that clearness index ranges for the different regions of Uganda: Northern (0.5288-0.6077), Eastern (0.5609-0.6077), Central (0.5123-0.6224) and Western (0.5123-0.5893). Besides, the empirical validation of the model results with the on-station data was carried out. There was good agreement between the simulated and on-station data with the trace of deviations which could be attributed to the impact of latitude and longitude of the failed locations. Furthermore, the present models were compared with the existing models, the deviation between the measured and the present model was insignificant compared to the existing models. Therefore, the present model could be employed in the advancement of solar technologies in Uganda.
Automatic Detection of Offensive Language in Social Media: Defining Linguistic Criteria to build a Mexican Spanish Dataset

Phenomena such as bullying, homophobia, sexism and racism have transcended to social networks, motivating the development of tools for their automatic detection. The challenge becomes greater for languages rich in popular sayings, colloquial expressions and idioms which may contain vulgar, profane or rude words, but not always have the intention of offending, as is the case of Mexican Spanish. Under these circumstances, the identification of the offense goes beyond the lexical and syntactic elements of the message. This first work aims to define the main linguistic features of aggressive, offensive and vulgar language in social networks in order to establish linguistic-based criteria to facilitate the identification of abusive language. For this purpose, a Mexican Spanish Twitter corpus was compiled and analyzed. The dataset included words that, despite being rude, need to be considered in context to determine they are part of an offense. Based on the analysis of this corpus, linguistic criteria were defined to determine whether a message is offensive. To simplify the application of these criteria, an easy-to-follow diagram was designed. The paper presents an example of the use of the diagram, as well as the basic statistics of the corpus.
Turning the Rivalrous Relations between Arbitration and Mediation into Cooperative or Convergent Modes of a Dispute Settlement Mechanism for Commercial Disputes in East Asia

This article discusses current developments in East Asia to more effectively use mediation with international commercial arbitration, supported by the 2019 United Nations Convention on International Settlement Agreements Resulting from Mediation (the “Singapore Convention”) and the UNCITRAL Model Law on International Commercial Mediation (the “UNCITRAL Model Mediation Law”), and by online dispute resolution using innovative technology (including artificial intelligence, machine learning, the internet of things, blockchain, distributed ledger technology and smart contracts). It discusses the background to the changing approach including the increasing pressure for arbitration to become a mechanism of last resort in international dispute resolution prompted by policy makers’ and users’ demands for cheaper , quicker and culturally more international dispute resolution and the changing sources of international investment, developments in international relations including the Belt and Road Initiative announced by China, and the initiatives being undertaken by various investment and trade communities including APEC, particularly focusing on micro, small and medium sized enterprises (each a “MSME”). This article briefly refers to the current status of online dispute resolution utilizing innovative technology, and the legal opportunities and challenges posed by it. Finally, it briefly refers to some neuroscience research relevant to dispute resolution.
Understanding user Taste Preferences for Food Recommendation

Machine learning has led to many technological innovations in several industries like marketing, eCommerce, etc…The benefits of AI and ML systems are experienced by many people without even knowing it, in search and recommendation systems as in google search and the way Netflix recommends movies.Furthermore, ML models can hypothetically be trained to understand the taste and predict taste preferences accurately under specific conditions.This ML model is to understand the taste and recommend food products based on users' tastes.The same process by which a machine learns to identify a users' taste in music applies to a user's taste in food.Based on the previous food orders made by the user we make our Ml model understand his taste preference and recommend the food products accordingly.Our study provides a better insight into how an ML model understands the user's taste preferences.In this paper, we provide functionalities to keep track of his previous food orders and taste preferences.Also, we recommend food items for individuals considering their taste preferences.
Students of humanities and digital skills: a survey on Italian university students

This paper presents a study carried out to survey the digital skills of Italian students belonging to the latest generation of digital natives and currently enrolled in university courses of humanities, in which IT and digital skills risk to be neglected both by the students’ interest and the range of offered courses. The study aims to understand how students perceive their level of digital skills and whether there is a discrepancy between their self-assessed level and their actual knowledge. Participants answered a survey of four sections, including preliminary questions, two self-assessment parts and a section containing multiple-choice questions, based on the European Framework of Digital Competences, on both theoretical and practical aspects of IT and digital skills. Results show that participants tend to overestimate their digital skills and that they lack knowledge of basic topics. Besides outlining the digital competences of Italian humanities students, this study points out digital skills that should be strengthen, whereas they are often taken for granted by teachers and institutions, although students need those skills in their university and professional life, but also for their daily digital needs.
Homography estimation along short videos by recurrent convolutional regression network

Many moving-camera video processing and analysis tasks require accurate estimation of homography across frames. Estimating homography between non-adjacent frames can be very challenging when their camera view angles show large difference. In this paper, we propose a new deep-learning based method for homography estimation along videos by exploiting temporal dynamics across frames. More specifically, we develop a recurrent convolutional regression network consisting of convolutional neural network (CNN) and recurrent neural network (RNN) with long short-term memory (LSTM) cells, followed by a regression layer for estimating the parameters of homography. In the experiments, we evaluate the proposed method on both the synthesized and real-world short videos. The experimental results verify that the proposed method can estimate the homographies along short videos better than several existing methods.
PENERAPAN APLIKASI QUICK RESPONSE DALAM MELAYANI PENGADUAN MASYARAKAT BERBASIS WEB DAN SMS GATEWAY

Quick Response application is an application designed to meet the needs of the community.This application is a forum for the public to convey to the relevant Regional Government.Types of complaints that the public can convey are complaints, questions, information and suggestions / proposals related to the topic of the complaint, namely about education, agriculture, health, population, food crop agriculture, road infrastructure, tourism, youth, sports, disaster management, and trade and market.The application designed in this research is web based and SMS Gateway.In its design, this website is made using the PHP programming language and also uses the MYSQL database.For SMS Gateway, it was designed using Gammu application.By using the web, people can send their complaints online.Even the public can also ask for their complaints with the SMS service.With this application, it is also expected to be able to facilitate the local government, as well as related parties involved and provide responses from public complaints that can be accounted for.
Benchmarking open source deep learning frameworks

Deep Learning (DL) is one of the hottest fields. To foster the growth of DL, several open source frameworks appeared providing implementations of the most common DL algorithms. These frameworks vary in the algorithms they support and in the quality of their implementations. The purpose of this work is to provide a qualitative and quantitative comparison among three such frameworks: TensorFlow, Theano and CNTK. To ensure that our study is as comprehensive as possible, we consider multiple benchmark datasets from different fields (image processing, NLP, etc.) and measure the performance of the frameworks' implementations of different DL algorithms. For most of our experiments, we find out that CNTK's implementations are superior to the other ones under consideration.
Assessment of Trace and Rare Earth Element Levels in Stream Sediments in Ijero-Ekiti Area, Southwest Nigeria

The study considered the level, sources and extent of trace and rare earth elements (REE) contamination in Agbangudu stream sediments in Ekiti State, Southwestern Nigeria.The samples were analysed with Laser Ablation Inductively Coupled Plasma Spectrometer (LA-ICP-MS).The trace and rare earth elements' concentration ranged from 0.50 (Mo) to 750 (Ba) and 0.16 (Lu) to 175 (Ce) ppm respectively.The results revealed that the sediments are not that enriched in REEs.The Pollution Load Index (PLI) indicates baseline levels of the metals.The geochemical index (Igeo) of the elements revealed uncontaminated to moderately contaminated, except for Cs and Ta with strongly to extremely contaminated status.The Average Shale Value (AVS) and the Upper Continental Crust (UCC) normalized REE distribution patterns of the sediments.To establish the relationship between the metals, Principal Component Analysis (PCA) and Clusters Analysis (CA) were used as classification techniques.Despite the common occurrences of the elements, their overall patterns were much different as revealed by the cluster analysis.
Sufficient Solvability Conditions for Systems of Partial Fuzzy Relational Equations

This paper focuses on searching sufficient conditions for the solvability of systems of partial fuzzy relational equations. In the case of solvable systems, we provide solutions of the systems. Two standard systems of fuzzy relational equations – namely the systems built on the basic composition and on the Bandler-Kohout subproduct – are considered under the assumption of partiality. Such an extension requires to employ partial algebras of operations for dealing with undefined values. In this investigation, we consider seven most-known algebras of undefined values in partial fuzzy set theory such as the Bochvar, Bochvar external, Sobociński, McCarthy, Nelson, Kleene, and the Łukasiewicz algebra. Conditions that are sufficient for the solvability of the systems are provided. The crucial role will be played by the so-called boundary condition.