Reviewerly/exHarmony huggingface dataset:
Services Computing for Cyber-Threat Intelligence: The ANITA Approach

Major cybersecurity and threat intelligence analysts agree that online criminal activity is increasing exponentially. Technologies, newspapers, the internet, and social media made the dark web an accessible place to almost everyone. The ease of accessing the dark side of the web makes the problem more critical than ever. For this reason, the European Union financed the ANITA project, consisting of different tools for monitoring and fighting illegal criminal activities on the Dark Web. In the ANITA project, we propose different Big Data analytic tools for the analysis of all data extracted from illegal marketplaces. In this survey paper we present our developed tools for detecting trends and analyzing the incoming information with respect to illegal trafficking. The tool extracts information about specific trends, analytics and produces actionable insight on buying and transaction habits and user behaviors. The tool extracts statistics in order to support and guide investigators and law enforcement agencies for the detection of criminal activities.
Binary intersection formalized

We provide a reformulation and a formalization of the classical result by Juhani Karhumäki characterizing intersections of two languages of the form {x,y}⁎∩{u,v}⁎. We use the terminology of morphisms which allows to formulate the result in a shorter and more transparent way, and we formalize the result in the proof assistant Isabelle/HOL.
Evaluating Predictive Deep Learning Models

Predicting the future using deep learning models is a research field of increasing interest. However, there is a lack of established evaluation methods for assessing their predictive abilities. Images and videos are targeted towards human observers, and since humans have individual perceptions of the world, evaluation of videos should take subjectivity into account. In this paper, we present a framework for evaluating predictive models using subjective data. The methodology is based on a mixed methods research design, and is applied in an experiment to measure the realism and accuracy of predictions of a visual traffic environment. Our method is shown to be uncorrelated with the predominant approach for evaluating predictive models, which is a frame-wise comparison between predictions and ground truth. These findings emphasise the importance of using subjective data in the assessment of predictive abilities of models and open up a new direction for evaluating predictive deep learning models.
Robust Face Tracking using Improved Mean Shift Algorithm

Abstract Face tracking is important system for robotic. The problem is that it is less resistant to occlusion and noise. Robust face tracking algorithm in this paper using improved mean shift algorithm is proposed. This algorithm consist of face detection and tracking. The face identification is done by the Viola-Jones algorithm. Then, Mean Shift algorithm is done to track face as a target and improve robustness from occlusion. CBWH is added in face as target to reduce noise. The experimental results prove that it is robust from noise background and occlusion..
Trust management model based on Intuitionistic Fuzzy Information in cloud environment

Due to the subjectivity and ambiguity of users' trust ratings in the cloud environment, the existing trust management mechanism cannot express well the uncertainty of trust. This paper proposes a trust evaluation model based on intuitionistic fuzzy information, which firstly generates direct intuitionistic fuzzy information based on the user's own historical interaction information, and then removes abnormal recommendation information based on normal distribution for the recommendation information of the recommended user. Finally, the intuitionistic fuzzy information is integrated into the final decision information and the relative closeness degree with the optimal solution is calculated. Experiments show that the model has a higher interaction success rate than the classical algorithm for the recommendation of malicious services and malicious users.
Fourier-based Verifiable Function Secret Sharing

Function secret sharing is a cryptographic protocol for secure distribution of a secret function. While the notion of function secret sharing is a conceptual extension of secret sharing schemes, it faces difficulties in its constructions. We demonstrate that if we consider function secret sharing schemes for Fourier basis functions then we can enjoy the linear properties of linear secret sharing schemes to construct function secret sharing schemes. In this paper, we construct a verifiable function secret sharing by using Fourier basis functions, where players can verify if the dealer is cheating.
Perception of Chilean University Professors on the Impact of the Didactic Operation in the COVID-19 Context

The pandemic context in the world has implied a curricular change in higher education institutions globally, impacting the management and operationalization of teaching in an accelerated way. Given this, it's relevant to consider the perception of university professors about the impact of the pandemic context on the didactic operation generated, because this can contribute to the understanding of the ways in which the curriculum is adapted in an artisanal way in this online modality that it has been necessary to carry out. For this study, a quantitative methodology is used, and the IOP20 questionnaire is used as a methodological data collection strategy, which establishes 4 dimensions, on which it is necessary to analyze the results. The method used to analyze the data is quantitative and descriptive, statistical methods and clustering techniques are used. With the results obtained, it is possible to clearly establish the perception of university professors in a very favorable way, about the importance of attending to what is related to didactic innovation in this teaching-learning process in the current situation.
Online Learning in Faculty of Mathematics and Natural Sciences, State University of Medan: Lecturers’ Preferences for Online Learning Media During the Covid-19 Pandemic

Abstract Online learning has long been promoted by the Faculty of Mathematics and Natural Sciences (FMNS), State University of Medan (SUM). The online learning system (SIPDA) has been developed by Unimed as the backbone of online learning, but the number of lecturers using the system has not been as expected. The covid-19 has succeeded in forcing all lecturers to migrate from classroom face-to-face (offline) to online learning. This research was aimed to study the preferences of lecturers for available online learning media during the Covid-19 pandemic. The data were obtained using questionnaire that distributed online to all lecturers and students who were active in lectures for the even semester of the 2019/2020 academic year. The data shows that all lecturers have implemented online learning to serve all classes. The majority of lecturers serve their class by using more than one online media. The order of the three types of online media which became the main preference of lecturers experienced a shift during the 3 months of observation, namely from WhatsApp (WA) - Email - Google Classroom (GC) in the first, to WA - GC - SIPDA in the second, and to WA - SIPDA - GC in the third month. It can be concluded that, although initially the lecturers’ main preference in learning was social media (WA), they have tried hard to make SIPDA as the main choice among the available e-learning media.
Object Identification Model using Deep Reinforcement Machine Learning Concept for Image

Abstract This paper presents a model which gives the detailed process of object identification. We need to identify class and location of object in image for completing process of objet identification. Proposed model works on the principal of reinforcement learning which takes action on the basis of rewards and experiences. Normally methods in literature uses sliding window which moves in same direction but proposed algorithm provides a variable mask which moves 360 degree for identifying object using action history vector proposed with RL also not only this work focuses on localization like other work but also used class information with Softmax classification able to classify multiple object in single image with efficient time which is novel. Proposed mask acts as agent and focuses on proposed candidate reason this saves time and works in efficient manner for identification. Agent depends on transformation action and by applying top down reasoning it gives location of object. Classification is done using Softmax classification as we are having features of image by CNN. Reinforcement learning concept used for training of agent and Pascal voc dataset used for testing. Analysis of only 10 to 25 regions is sufficient with proposed work to identify first instance of object. Experiment and performance evaluation shows the efficiency of proposed work.
Modeling of cloudiness based on the difference between the theoretical solar irradiance and the real solar irradiance on the earth’s surface for the installation of photovoltaic energy projects

Abstract Considering that the solar irradiance on the surface is a direct indicator of cloudiness [1], we proceeded to make a model that determines the cloudiness, contrasting the differences that are generated between the theoretical solar irradiance estimated on the surface [1] [2] and real solar irradiance recorded on the surface. To evaluate the quality of the model, the real cloud values and the estimates were contrasted in a dispersion diagram, both in eighths at the daily average level, obtaining a correlation coefficient of 0.7. The modeling of cloudiness is a relevant variable for the installation of photovoltaic energy projects.
Research on Distributed Operation Mode of Internet of Things

Abstract The application of distributed system in the Internet of things makes the system control completely networked, realizes the connection of point-to-point and star data transmission control, makes the system highly scalable, convenient for centralized management, and is suitable for multi-functional Internet of things applications. In the distributed architecture, data is distributed on multiple servers. A distributed database is composed of multiple logically related databases distributed on the Internet of things. Each node on the network has independent processing ability, so it is necessary to find a suitable distributed system solution between code and database.
Novel Approach for Task Scheduling of HPC Applications Using SDN

Modernizations and progresses in cloud computing are some of the developing trends in distributed computing that fascinated researchers to deploy high-performance computing (HPC) applications on a cloud. HPC applications in multitenant clouds with resource virtualization not only provide resource elasticity, but also response time for applications. In order to lower the time that is needed for task implementation in the process of data migration, task scheduling algorithm personates a vital role. This study aims to propose a novel approach for task and resource scheduling for HPC applications using software-defined networking. Herein, a precedence-based dynamical scheduling approach is provided to setup and update routes for concurrent flow of data during data transmission, so as to optimize resource hiring cost and minimize incoming applications’ execution time. Experimental results revealed that the proposed method offers considerably better computational resource utilization than classical scheduling methods like first come FCFS, round-robin and optimal task scheduling algorithm.
IMAGING TECHNOLOGIES IN HYBRID TRAINING OF FUTURE ENGINEERS: IMPLEMENTATION DIRECTIONS AND PROFESSIONAL-PERSONAL SUCCESS

The article is devoted to the problems of using visualization technology in the training of future engineers when combining distance and face-to-face training formats (hybrid training). The directions of using visualization options are presented: prototyping of production objects, Flickr-illustration of presentations, animation and video-laboratory work. The positive influence of visualization technologies on the professional and personal success of future engineers is revealed.
The use of video recordings in the organization of recreational classes with students

One of the important areas that has undergone significant changes due to the epidemic and the quarantine measures caused by it is education. The need to quickly change the forms of conducting classes has become a real test for teachers of performing disciplines of a sports profile, and prompted them to search and experiment in the use of information technologies and technical means in the educational process. Since sports education in general and the teaching of academic disciplines that form sports skills have their own specific differences, the use of information technologies in this area has caused a number of problems that require separate detailed consideration. Distance learning has become mainstream in the context of global quarantine. Therefore, this problem inspired the consideration in the article of such issues as the methodology for preparing and conducting practical classes, as well as the use of information technologies and technical means such as video products, multimedia presentations, encyclopedias, simulators and games that are used in the educational process of students and will help it. improvement. The use of these technical means and information technologies contributes to the improvement of education in conditions of quarantine restrictions, as well as to the improvement of the perception of information through new technologies, methods and means. With the use of information technologies, students become more clear about the material of the disciplines of a sports profile.
Re-Os dating, and Pb-H-O isotope characteristics, of the Abra Cu-Ag-Pb-Au polymetallic deposit in Western Australia

Abstract The Abra deposit, a large lead-silver-copper–gold polymetallic deposit in Western Australia, is located at the eastern of the metallogenic belt of the Jillawarra basin in the Bangemall basin. The 4th to the 6th rock section of the Irrigully Group of Edmund Series are the principal ore-host strata, composed mainly of sandstone and fine sandstone. The orebody in Abra can be classified into two types as upper layer-like lead-silver and lower veins or netvein copper–gold. The metal minerals are mainly galena, chalcopyrite, and pyrite, while the gangue minerals are mainly quartz, dolomite, and barite. Both Re-Os isotopic age of the pyrite (1329.5 ± 98 Ma) with the initial ( 187 Os/ 188 Os) = 5.0 ± 3.8 and Pb isotopic compositions ( 206 Pb/ 204 Pb = 15.914–15.967, 207 Pb/ 204 Pb = 15.425–15.454, 208 Pb/ 204 Pb = 35.584–35.667) suggests that the metal minerals were sourced from the wall-rocks. δD V-SMOW values of quartz range from -35‰ to -17‰ whereas δ 18 O V-SMOW value range from 12‰ to 16‰ which indicates that the ore-forming fluids of Abra were medium–low temperature and medium–low salinity, and were mainly metamorphic water and secondary atmospheric precipitation. When the medium–low temperature ore-forming fluids are mixed with oxidizing reducing fluids carrying a large number of metal substances, a large number of ore-forming substances will be precipitated when the physical and chemical conditions change, thus it can be considered that the Abra deposit is a medium–low temperature hydrothermal polymetallic deposit.
Exploring Large Language Models in a Limited Resource Scenario

Generative Pre-trained Transformers (GPT) have gained a lot of popularity in the domain of Natural Language Processing (NPL). Lately, GPTs have been fine-tuned for tasks like sentiment analysis and text summarization. As the number of tunable parameters increases with larger language models (like GPT-3), it becomes resource-heavy to fine-tune these models on commercially available personal computer systems. In addition to that, GPT-3 is only available through an API which makes it even harder to fine-tune it for a specific task. This makes these models less accessible to the general public and researchers. Alternative ways are required to better understand the nature of these language models and employ them for challenging NLP tasks without explicit fine-tuning. This study capitalizes on the raw capabilities of GPT-2, it proposes and proves the efficacy of one such system in the task of sentiment analysis without explicit fine-tuning. It also sheds light into the nature of such generative language models and shows how explainability can be exploited to achieve good results with minimum resources. It was observed that the proposed system does a good job of capturing the sentiment of a given text. It reached an accuracy of 82% on a part of the IMDB Data set of Movie Reviews. The system performed better with natural language prompt when compared to symbol-based syntactic prompts.
Interpreting and Evaluating Black Box Models in a Customizable Way

A vast majority of complex deep learning models currently remain black boxes, which means that their internal working process and the logical relationships between the input data and the output predictions are hidden to human users. For financial and medical use cases, this characteristic greatly hinders the application of deep learning models because people cannot know the logic behind model decisions such as stock forecast and disease diagnosis. In this paper, we propose CMIE, namely, Customizable Model Interpretation Evaluation, which is a set of customizable evaluation methods of using the in-model and post-model information to generate multi-dimensional interpretability evaluation of the convolutional neural networks (CNNs) with different structures. The evaluation report includes the results of several interpretability criteria of the CNNs so that the user can know how well the model learns from the features in a comprehensible way. Experiments on LeNet, AlexNet and VGG-16 with MNIST and CIFAR-10 have demonstrated the practicality and effectiveness of the adopted Explainable Artificial Intelligence (XAI) method.
Development of electronic teaching materials based Flip Book Makers for language skills in elementary schools

Abstract This study aims to develop electronic teaching materials for Indonesian language skills based Flip Book Makers. The design used is research and development with the following procedures: (1) needs analysis, (2) developing electronic teaching materials based Flip Book Makers, and (3) validating the electronic teaching materials based Flip Book Makers that have been developed. The legibility test results of the teaching materials showed that the readability criteria could be understood with an average score of 86%. From the results of the research conducted, the development of performance-based teaching materials for Indonesian language skills was deemed feasible.
A Heuristic Review on Analog Performance and Accomplishment of Activation Functions at RTL Level

Abstract In this article, the behavior of many functional activities used for Artificial Neural Networks (ANNs) study is demonstrated together with their similar performance under transistor conditions. ANN plays an important role in computer science, technology, machine learning, automation, speech and voice processing. The accuracy of any automated device largely depends on ANN training. Therefore, choosing an appropriate operating system affects the performance of the entire system. Performing operational movements at the transistor levels with low power, latency, and power requirements without adjusting their properties is a major challenge. Process actions are performed on an analog or digital one. Analog performance simulations are clearly demonstrated in this article, as the field and power limit for simulation applications is greater than for digital applications. The efficiency of the operating system relies on the % of errors between its manufacture and quality.
Learning Cube Strategy in Backgammon with Neural Networks

While neural networks have been used to study checker play in backgammon, the decision of giving or taking the doubling cube, especially in relation to the match score, has been largely considered an orthogonal problem. We integrate the doubling cube and match scoring into the network. In doing so, our network learns how differences in match scores and cube circumstances influence not just further cube decisions, but checker play as well. We find that by incorporating the doubling cube situation into the input of the neural network, we were able to statistically outperform networks without this feature.
Rancang Bangun Sistem Informasi Guest Book pada Dinas PU Bina Marga dan Tata Ruang Berbasis Web

Hostess is an important part of an institution as a medium for community service. This is because the reception is the first and last place the guest meets, so the first and last impression is formed on the receptionist. One of the agencies that requires a reception is the Public Works Office of Highways and Spatial Planning of South Sumatra Province (PUBMTR), because every day visitors often come from outside, either in terms of work or non-governmental organizations (NGOs). Based on observations at the Public Works Department of Bina Marga and Spatial Planning, there are many obstacles faced by the receptionist in carrying out their duties because they still use manual methods so that they are not effective. This study aims to design a Web-based Receptionist Information System and create a new system, in order to make it easier for the Public Works of Highways and Spatial Planning of South Sumatra Province in filling out the Guest Book. This research uses several methods, namely, prototype, PHP, and ERD (Entity Relationship Diagram). Field research is carried out in two ways, namely through direct observation, as well as conducting interviews with related parties, and practical work is a combination of the two research methods. The result of this research is a Website-based Guest Book Information System. The benefits obtained from implementing the information system are to make it easier for the office to improve the better quality.
Realización de un termostato inteligente utilizando la plataforma Raspberry PI como proyecto integrador de la materia de programación avanzada

Project Oriented Learning (POL) is a teaching methodology, where students have their own learning, they devoped a product that allows them to apply and show the acquired knowledge. POL as an educational tool allows the student to develop skills such as interdisciplinary work and problem solving to tackle current scientific and social demands while creating an innovative transformation in the educational process. This article presents the process of developing an intelligent thermostat using the Raspberry PI platform, and Internet of Things IoT technologies, aimed at training students at the level of a Technical College , enrolled in the ninth quarter of the Mechatronics degree curriculum. The creation of this project allows the fulfillment of the specific competence of the degree, which is "Develop automation and control projects, through the design, administration and application of new technologies to meet the needs of the productive sector".
Architecture of machine reading comprehension models

Early machine reading comprehension (MRC) models are mostly based on information retrieval technology, that is, search and return statements in the article related to the question. However, information retrieval is heavily dependent on keyword matching, which often gives answers irrelevant to the question. With the development of deep learning, MRC has entered the era of neural networks. Advances in related technologies have considerably improved the efficiency and quality of models. The accuracy of MRC models have been steadily increasing, even reaching or exceeding human performance in some datasets. In this chapter, we introduce the architecture of these MRC models and explore the reasons behind the improvements of performance.