Reviewerly/exHarmony huggingface dataset:
Robust Phishing Detection Against Adversaries

Phishing websites have grown more recently than ever, and they become more intelligent, even against well-designed phishing detection techniques. Formerly, we have proposed in the literature a state-of-the-art URL-exclusive phishing detection solution based on Convolutional Neural Network (CNN) model, which we referred as PUCNN model. Phishing detection is adversarial as the phisher may attempt to avoid the detection. This adversarial nature makes standard evaluations less useful in predicting model performance in such adversarial situations. We aim to improve PUCNN by addressing the adversarial nature of phishing detection with a restricted adversarial scenario, as PUCNN has shown that an unrestricted attacker dominates. To evaluate this adversarial scenario, we present a parameterized text-based mutation strategy used for generating adversarial samples. These parameters tune the attacker’s restrictions. We have focused on text-based mutation due to our focus on URL-exclusive models. The PUCNN model generally showed robustness and performed well when the parameters were low, which indicates a more restricted attacker.
An Approach to the Application of Ontologies in the Knowledge Management of Companies

The present paper aims to show the benefits of ontologies in knowledge management and the process in which organizations earn and produce value thanks to the knowledge of their employees. Tacit and explicit knowledge is addressed as the main link. Technological tools and functions are shown that contribute to improving the culture and adequate decision-making in an organization. Services on the web have evolved as an open access technology on the Internet. The web 3.0 has tools such as ontologies that can solve the semantic deficiencies that today make it difficult to access information in networks to give a more structured meaning to data. The methodology to be followed is based on action research that allows the development of the theoretical framework, on the one hand, to later obtain improvements or corroborate the theoretical framework. First, research was carried out on the relationship between ontology and knowledge management, later applications with ontologies and use cases are seen in different domains to support the creation and dissemination of knowledge in the management processes of the knowledge. The results presented in this paper are those related to the theoretical framework. Ontologies applications in public health, digital libraries, as well as in software engineering were described, as well as its potential benefits. The attention paid to the integration of ontologies, both from the same domain and from different domains that share concepts, is insufficient. Knowledge management in an organization requires the use of intelligent agents through ontologies.
Anti-Covi-Bot: A Robot to Combat COVID-19

As per researchers worldwide, the practice of social distancing and wearing masks may have to be continued till the end of 2022 or even after considering the deadly and unpredictable nature of the COVID-19 virus that has gripped our world. In spite of all the rules and regulations in place we still find people breaking the rules. Also, it is to be noted that many officials who are tasked with enforcing the rules have either been severely affected or have lost their lives. Our work focuses on creating a holonomic robot, that can detect violations to mask and social distancing rules and generate alerts. Our work uses OpenCV and CNN model for face mask detection and pre-trained Mobile-Net Single Shot Object Detection (SSD) model to detect people and check if social distancing is adhered to.
A Proof of Work based on Preimage Problem of Variants of SHA-3 with ASIC Resistance

Hashcash, which is a Proof of Work (PoW) of bitcoin, is based on a preimage problem of hash functions of SHA-2 and RIPEMD. Since these hash functions employ the Merkle-Damgard (MD) construction, a preimage can be found with a negligible amount of memory. It is well known that such calculations can be speeded up by ASIC, and this causes a serious problem from the so-called 51% attack by dedicated ASIC mining pools. To address this issue, we propose a new PoW scheme based on a preimage problem of variants of SHA-3. Unlike SHA-2 and RIPEMD, SHA-3 adopts a sponge construction as an underlying domain extension algorithm. This difference allows us to make the problem of finding a preimage very memory-consuming calculations by properly choosing parameters of sponge functions. As a result, our scheme can achieve ASIC resistance by using SHA-3 for Hashcash.
An Integrated Resampling Methods for Imbalanced Sporadic Temporal Data in EHRs

Most real-world applications in EHRs involve temporal data with skewed distributions. The imbalanced classification problem becomes more difficult in sporadic temporal data that variables exist on correlation and have some missing values. A common solution to classification tasks with imbalanced data is the oversampling methods, which generate new samples to re-balancing the classes. However, traditional oversampling methods usually change the distribution, thereby leading to bias. This paper proposed a self-adaptive integrated oversampling method for imbalanced sporadic temporal data in EHRs. The masking vectors and density vectors have been introduced to measure missing value distribution of samples, and the minority samples are divided into high density samples and sparse density samples. We extend the resampling strategies combining a subsample alignment method and structure preserving oversampling method. The weight of sample difference is used to improve classification performance. Furthermore, the filter mechanism is proposed to remove the noise samples with good efficiency. The experimental results show that the proposed method increases performance compared to traditional resampling methods in terms of AUC, F1, and G-mean evaluation metrics.
Makers in Education: Teaching is a Hacking Stuff

Abstract Several changes and challenges affect contemporary society: public administration, business, society at large. The lowest common denominator is digitization through IT tools. And what about school systems? Do they play a decisive role in enabling the skills needed to these challenges? We send our children to school to prepare them for a fast-changing world. But have our schools (and teachers) changed in the last 100 years? Modern teachers are being asked to change their teaching and learning practices, and to implement methodologies (including the use of IT tools) and make way for new teaching practices.
On Cost-Efficient Learning of Data Dependency

In this paper, we consider the problem of learning a tree graph structure that represents the statistical data dependency among nodes for a set of data samples generated by nodes, which provides the basic structure to perform a probabilistic inference task. Inference in the data graph includes marginal inference and maximum a posteriori (MAP) estimation, and belief propagation (BP) is a commonly used algorithm to compute the marginal distribution of nodes via message-passing, incurring non-negligible amount of communication cost. We inevitably have the trade-off between the inference accuracy and the message-passing cost because the learned structure of data dependency and physical connectivity graph are often highly different. In this paper, we formalize this trade-off in an optimization problem which outputs the data dependency graph that jointly considers learning accuracy and message-passing costs. We focus on two popular implementations of BP, <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ASYNC-BP</monospace> and <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SYNC-BP</monospace> , which have different message-passing mechanisms and cost structures. In <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ASYNC-BP</monospace> , we propose a polynomial-time learning algorithm that is optimal, motivated by finding a maximum weight spanning tree of a complete graph. In <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SYNC-BP</monospace> , we prove the NP-hardness of the problem and propose a greedy heuristic. For both BP implementations, we quantify how the error probability that the learned cost-efficient data graph differs from the ideal one decays as the number of data samples grows, using the large deviation principle, which provides a guideline on how many samples are necessary to obtain a certain trade-off. We validate our theoretical findings through extensive simulations, which confirms that it has a good match.
Two-step blind phase-shifting interferometry based on the extreme value of interference and least-squares iterative algorithm

To improve the measuring accuracy in two-step phase-shifting interferometry (PSI), a new approach combining the extreme value of interference (EVI) and the least-squares iterative algorithm (LSIA) is proposed to extract the phase from two-frame blind phase-shifting interferograms. This method first evaluates the phase shift between two interferograms by the EVI algorithm, and then constructs the fitted interferogram by the addition of two interferograms after filtering the corresponding background intensities, so the phase with high precision can be retrieved by combining two real interferograms and this fitted interferogram using the LSIA method. The proposed algorithm expands the flexibility of the LSIA method and has the high-precision performance compared with the existing algorithms in two-step PSI. Simulation and experiment are performed to verify the feasibility of the proposed algorithm.
Method design based on the national scholarship system

With the popularization of higher education in my country and the number of college students increasing year by year, the number of students in colleges and universities that need to be subsidized is also increasing day by day. At present, colleges and universities mostly use offline material collection and manual operation. Not only the workload and the amount of tasks are heavy, but also the current situation of irregular funding management is prone to cause unnecessary troubles to the funding work. There is an urgent need to establish a complete management system for student assistance. This paper presents a design method for the information management system of the financial aid for poor students in colleges and universities based on the Web platform. This method is based on the construction of campus network and adds data analysis modules to realize an efficient informatization management of university funding work.
Author Identification of Handwritten Numbers Based on Transfer Learning and Multi-task Learning

The identification tech is reliable only when the test accuracy is high enough, in order to identify the handwritten numbers’ author and numbers in various applications, this paper focus on how to improve the accuracy of identifying handwritten numbers’ authors and numbers, especially the author identification in deep learning. Comparing with number identification which has been widely used in many fields, author identification is more difficult because the differences between different numbers are very easy to be extracted, however for same numbers that different authors wrote, it is hard to find out the feature points. The problem that has to be solved is what framework is the best choice to improve the test accuracy, CNN is often used in image recognition as required in this experiment, and different methods like transfer learning, multi-task learning and multi-task transfer learning are expected to improve the accuracy of identifying handwritten numbers’ author and numbers as well. Four main methods are described in detail: (1) enlarge the number of samples in a data set, (2) transfer learning, (3) multi-task learning, (4) multi-task transfer learning.
Region-based image retrieval using region of interest (ROI) according to incremental frame and clustering color image

Content-based image retrieval involves the extraction of global feature images for their retrieval performance in large image databases. Extraction of global features image cause problem of the semantic gap between the high-level meaning and low-level visual features images. In this study RBIR, Region of Interest Based (ROI) Image Retrieval Using Incremental Frame of Color Image was proposed. It combines several methods, including filtering process, image partitioning using clustering and incremental frame formation, complementation law of theory set to generate ROI, NROI, or ER of the region. The concept of weighting as well as a significant query is also incorporated as a query strategy. Extensive experiments were also conducted on the Wang database and the color model selected was the CIE lab. Experimental results show the proposed method is efficient in image retrieval. The performance of the proposed method shows a better average IPR value of 3.51% compared to RGB and 22.92% with the HSV color model. Meanwhile, it also performs better by 36%, 5%, and 24% compared to methods CH (8,2,2), CH (8,3,3), and CH (16,4,4).
Runet Users' Attitude to Outrageous Content in Virtual Social Environments

This study of the Runet virtual social environments deals with identifying and analyzing user reactions to watching a video clip containing outrageous behavior, and studying social attitudes of the respondents. The Celebrium-X platform employing the adjoint ideomotion technology for remote collection and processing of group neuro-responses was chosen as a research tool. As the empirical basis of the research, the raw data obtained as a result of the conducted survey was used. The results of the study have shown the respondents' negative attitude to publishing of outrageous video clips in virtual social environments. The Internet audience realizes a negative effect produced by the aggressive content on people's psychological sphere and social behavior.&#x0D; &#x0D; Received: 12 October 2021 / Accepted: 11 December 2021 / Published: 3 January 2022
Local Narrative Text As The Implementation Of “PPK” In ELT Through Android-Based Media

The purpose of this study is to improve students' English competence through local narrative text by introducing Android-based learning media as a form of implementation of PPK in ELT.The analysis used in this research is qualitative which is associated with various methods, perspectives, and approaches such as the Research & Development (R&D) method.The stages of the research included data collection, data designing, developing Android-based learning media, expert validation, and product testing.The results of questionnaires and interviews from data collection to product testing showed that more than 74% of respondents agreed and were satisfied with the ELT media with Android-based local narrative examples.The learning media is used to improve the English competence of students, implement PPK, and preserve local values that are full of moral education.
The perception of languages by Italian native speakers

This essay showcases the results of a survey conducted on a sample of Italian speakers which focused on their perception of languages. The research, carried out in the second half of 2019, has enabled corroborating the initial hypothesis about the existence of preconceptions and stereotypes associated with the perception of foreign languages and cultures (Elman &amp; McClelland 1986) by Italian people. In particular, a questionnaire was used to clarify which adjectives are commonly associated with selected foreign languages, in order to identify certain trends and stereotypes. The survey is based on ethnolinguistic and language education studies (Cardona 2006), and was performed through data collection techniques used in ethnography and sociolinguistics (Tagliamonte 2006).
Determination of the Contribution of the Reserve Fund for Flood Natural Disaster Management in the DKI Jakarta Region

Floods are natural disasters that are quite difficult to predict. As a result, there are many losses both materially, morally and even to the point of taking lives. In Indonesia, one of the areas that experience flooding the most is DKI Jakarta. In early 2020, flooding was the biggest cause of loss in the region. The role of the people of DKI Jakarta is very important in collecting contributions to the reserve fund for disaster emergency response. Therefore, this study aims to estimate the amount of reserve fund contributions for community-based flood disaster management in the DKI Jakarta area based on the Collective Risk Model method approach, using Poisson and Log-Normal distributions, including parameter estimates and (μ,σ) , resulting in an estimate of the expected magnitude of the risk of loss. Based on these expectations, the contribution amount can be calculated using the Individual and Collective Risk Model. The result of this research is the contribution of funds which is calculated based on the principle of expected value
Towards Routines Mining – Designing and Implementing the Argos Miner, a Design Science Artifact for Studying Routine Dynamics with Process Mining

Digital artifacts increasingly support actors in carrying out organizational routines.These artifacts leave digital trace data, that is, time-stamped data about what actions actors performed.While extant research on routines largely builds on qualitative methods, the increasing ubiquitousness and prevalence of trace data enable novel methodological opportunities.However, several challenges currently hinder the adoption of trace data in empirical research on routines in general and their dynamics in particular.Promising approaches such as process mining are neither designed for nor sensitive to the concept of routines.In this paper, we follow a design science research approach to develop the first iteration of an artifact, which we coin Argos Miner.This artifact is based on process mining algorithms and overcomes challenges inherent in adopting process mining in routine dynamics research.It enables scholars to capture reality in flight by analyzing routine dynamics using a computational, mixed-methods approach.
Replicated Study of Effectiveness Evaluation of Cutting-Edge Software Engineering

Although many software engineering studies have been conducted, it is not clear whether they meet the needs of software development practitioners. Some studies evaluated the effectiveness of software engineering research by practitioners, to clarify the research satisfies the needs of the practitioners. We performed replicated study of them, recruiting practitioners who mainly belong to SMEs (small and medium-sized enterprises) to the survey. We asked 16 practitioners to evaluate cutting-edge software engineering studies presented in ICSE 2016. In the survey, we set the viewpoint of the evaluation as the effectiveness for the respondent's own work. As a result, the ratio of positive answers (i.e., the answers were greater than 2 on a 5-point scale) was 33.3%, and the ratio was lower than past studies. The result was not affected by the number of employees in the respondent's company, but would be affected by the viewpoint of the evaluation.
The creation of industrial production of the sub-industry of plastic pipes at the enterprises of the POLYPLASTIC Group

A historical sketch of the development of plastic pipe production in the Russian Federation is presented. The data on the production of pressure pipes of large diameter up to 1600 mm, non-pressure pipes for sewage, with a diameter of up to 2.5 m, pressure reinforced pipes based on cross-linked polyethylene and thermoplastics are presented. The trends in the development of the production of various types of pipes are characterized and data on the specifi cs of their application are given. The technological features of enterprises are analyzed, highlighting the preferred categories of pipes produced by the enterprises in the highest quality categories.
Joint Use of Node Attributes and Proximity for Node Classification

Node classification aims to infer unknown node labels from known labels and other node attributes. Standard approaches for this task assume homophily, whereby a node’s label is predicted from the labels of other nodes nearby in the network. However, there are also cases of networks where labels are better predicted from the individual attributes of each node rather than the labels of nearby nodes. Ideally, node classification methods should flexibly adapt to a range of settings wherein unknown labels are predicted either from labels of nearby nodes, or individual node attributes, or partly both. In this paper, we propose a principled approach, JANE, based on a generative probabilistic model that jointly weighs the role of attributes and node proximity via embeddings in predicting labels. Experiments on multiple network datasets demonstrate that JANE exhibits the desired combination of versatility and competitive performance compared to baselines.
Azure Resource Manager

A few years ago, the former Windows Azure used a classic deployment model through Azure Service Management (ASM) to provision and maintain your resources in Microsoft’s public cloud offering.
UX Testing for Mobile Learning Applications of Deaf Children

Many studies are focusing on deaf children mobile learning.However, they are not concentrating on user experience (UX) testing.Current UX testing is based on existing UX evaluation models that are hard to apply due to the comprehensive measurements and lack of description on how to conduct evaluation for a more specific mobile learning process.Moreover, the existing UX evaluation models are not highlighted to be applied in testing UX for deaf children's mobile learning.Hence, this paper proposed questions for UX testing for deaf children's mobile learning to explore UX issues in offering an enjoyable learning application.Smileyometer is used to capture the data from deaf children after using a selected mobile learning application, KoTBaM and Learning Fakih.This study involves deaf children aged 7 -12 years old familiar with the mobile application.The survey is divided into two sections: i) demographic information and ii) 24 questions that the respondent must answer using a smileyometer.The survey included 38 deaf children from Malaysian Deaf School.The participating deaf children completed the questionnaires with the assistance of their teachers after using the mobile learning application in the classroom.Yet, various issues needed to be addressed in order to improve the deaf children's user experience.Special exercises should be developed for deaf children connected to their school syllabus to consolidate their knowledge and self-learn everywhere.Furthermore, games elements should be adapted so the deaf children are able to learn while playing.