Reviewerly/exHarmony huggingface dataset:
TrustedAP: Using the Ethereum Blockchain to Mitigate the Evil Twin Attack

The wireless network Evil Twin Attack (ETA) is still only partially mitigated decades after the advent of wireless networking. An ETA is a spoofing attack wherein a malicious actor creates a convincing copy of a wireless network Access Point (AP) by duplicating a Service Set ID (SSID) and/or corresponding Message Access Control (MAC) addresses. We introduce TrustedAP: A blockchain-based solution that provides means for clients' (End Users') verification of AP devices as trustworthy using minimal (if any) connected time. We intend to use the Ethereum blockchain and smart contracts as a medium for an End User to challenge an AP's trustworthiness. Next, the AP (or AP trust-managing entity) delivers a sound, unique response to both the blockchain via smart contract and the requesting End User by a network channel. The End User validates an AP response using cryptographic integrity-preserving tools alongside an additional capacity for auditing an AP ownership chain. Once validated, clients have a very strong reason to believe that their connection is trustworthy, genuine, and under positive control of the intended public WiFi provider.
ANALYSIS OF DATA VISUALIZATION IN PANDEMIC SITUATION

Data visualization is a technique to expand and explore the difficult dataset into diagrammatical, maps and pictorial forms.This paper focuses on data visualization worked in two Era's of pandemic situation such as plague in 1920 and Covid-19 in 2020.In the current scenario we now that data visualization as proven effectively and played vital role in Covid-19.We have examined some techniques and tools which are present in 1920 and 2020.Data visualization has shown his importance to society and also different languages such as python and different tools of big data has done well job.We have seen some different results from data visualization such as destroying chain of virus means that tracking the first person record and stopping him from community transmission.
Multiview Sentiment Analysis with Image-Text-Concept Features of Indonesian Social Media Posts

Social media development makes it possible for everyone to express their opinions and information through text, speech, video, or images.Multiview sentiment analysis in current studies generally combines two modalities, text and image.It seeks to classify social media posts into two or more polarities, such as positive, neutral, or negative.To improve the performance of multiview Sentiment Analysis, we added another modality, which is concepts derived from text and image.Our proposed model integrates three views into a fusion with an ensemble approach by a metaclassifier.We performed text classification with Deep Convolutional Neural Networks.The input feature is Word2Vec for text representation in order to preserve semantic meaning.Additionally, we analyzed concepts from texts with SenticNet 5 as a knowledge base model and extracted concepts from images using the DeepSentiBank model.We obtained 2089 Adjective Noun Pairs and classified it with Multi-Layer Perceptron.Then we combined predicted probabilities from each classifier for Image, Text, and Concept by Ensemble Learning.A meta-classifier was implemented to predict the final sentiment from a fusion of Image-Text-Concept features.The fusion for multiview sentiment analysis works well and could achieve the best accuracy of 70% by applying the ensemble approach with Logistic Regression as the meta-classifier.
The impact of digital technology on the quality of higher education

COVID-19 pandemic intensified the transition to online learning practices. The article aims to analyse how students perceive distance education quality compared to face-to-face education mode. The author argues that the groups of social actors, interested in the promotion of distance education in higher schools, include: university leadership, geographically and physically disadvantaged students. In the period 2015-2020 the group of USUE researchers conducted a series of students’ surveys. Analysing the survey findings, the author states that despite the benefits of distance education, students consider that the quality of distance learning is inferior to the traditional face-to-face education mode. Among the problems faced by students in distance learning, is their inability to select information, assess its reliability, and differentiate significant information from insignificant. It is concluded that it is of great importance to continue research into the impacts of digitalization on the quality of higher education.
Missing data handling for machine learning models

This paper discusses a novel algorithm for solving a missing data problem in the machine learning pre-processing stage. A model built to help lenders evaluate home loans based on numerous factors by learning from available user data, is adopted in this paper as an example. If one of the factors is missing for a person in the dataset, the currently used methods delete the whole entry therefore reducing the size of the dataset and affecting the machine learning model accuracy. The novel algorithm aims to avoid losing entries for missing factors by breaking the dataset into multiple subsets, building a different machine learning model for each subset, then combining the models into one machine learning model. In this manner, the model makes use of all available data and only neglects the missing values. Overall, the new algorithm improved the prediction accuracy by 5% from 93% accuracy to 98% in the home loan example.
Moral value in serial cartoon Upin and Ipin English version

There are many technology-based media that can use in learning English. One of them is the English version of Upin Ipin which can download from YouTube. The purpose of this study is to describe the ability of students to study, analyze and translate moral values ​​contained in each episode of the Upin Ipin cartoon series. The research method taken is a qualitative method in which the ability of students to describe the value of moral tests using tests and ruptures. The results of this study were the first of 31 students 22 people were able to explain moral values ​​well, and 3 people were excellent
Transmission [Normalized) Demonstration of on chip cavity to thermal Rb vapor strong coupling

We experimentally demonstrate a chip scale platform supporting strong coupling between hot atomic rubidium vapor and a whispering gallery mode, with diverse applications in quantum photonics, sensing, nonlinear physics, chemistry and more.
Person Re-Identification by Effective Features and Self-Optimized Pseudo-Label

With the development of deep learning, person re-identification (ReID) has been widely concerned and studied. At present, in practical application, there are three main problems in person ReID: first, it is difficult to locate the target person because the person is frequently partially occluded in crowed scenes; second, it is difficult to match the target person due to the similarity of the target person and other pedestrian features; third, the problem of model performance degradation caused by the large style discrepancies across domain/datasets. These three problems greatly limit the application of person ReID in real scenes. To solve these problems, we proposed a person ReID method based on effective features and self-optimized pseudo-label. Firstly, we designed a feature aggregation module which combines mask channel and pose channel to accurately extract the global saliency features, so as to solve the occlusion problem; secondly, we designed a head-shoulder feature auxiliary module to enhance the feature representation of the head-shoulder, so as to solve the problem of similarity between the target person and other pedestrian features; finally, we designed a self-optimized pseudo-label training module to improves the generalization ability of the model, so as to solve the problem of different styles in the cross-domain environment. Extensive contrast experiments with the state-of-the-art methods on multiple person re-ID datasets show that our method leads to significant improvement, which prove the effectiveness of our method.
Investigating Simple Object Representations in Model-Free Deep Reinforcement Learning

We explore the benefits of augmenting state-of-the-art model-free deep reinforcement algorithms with simple object representations. Following the Frostbite challenge posited by Lake et al. (2017), we identify object representations as a critical cognitive capacity lacking from current reinforcement learning agents. We discover that providing the Rainbow model (Hessel et al.,2018) with simple, feature-engineered object representations substantially boosts its performance on the Frostbite game from Atari 2600. We then analyze the relative contributions of the representations of different types of objects, identify environment states where these representations are most impactful, and examine how these representations aid in generalizing to novel situations.
Methods of countering speech synthesis attacks on voice biometric systems in banking

The paper considers methods of countering speech synthesis attacks on voice biometric systems in banking.Voice biometrics security is a large-scale problem significantly raised over the past few years.Automatic speaker verification systems (ASV) are vulnerable to various types of spoofing attacks: impersonation, replay attacks, voice conversion, and speech synthesis attacks.Speech synthesis attacks are the most dangerous as the technologies of speech synthesis are developing rapidly (GAN, Unit selection, RNN, etc.).Anti-spoofing approaches can be based on searching for phase and tone frequency anomalies appearing during speech synthesis and on a preliminary knowledge of the acoustic differences of specific speech synthesizers.ASV security remains an unsolved problem, because there is no universal solution that does not depend on the speech synthesis methods used by the attacker.In this paper, we provide the analysis of existing speech synthesis technologies and the most promising attacks detection methods for banking and financial organizations.Identification features should include emotional state and cepstral characteristics of voice.It is necessary to adjust the user's voiceprint regularly.Analyzed signal should not be too smooth and containing unnatural noises or sharp interruptions changes in the signal level.Analysis of speech intelligibility and semantics are also important.Dynamic passwords database should contain words that are difficult to synthesize and pronounce.The proposed approach could be used for design and development of authentication systems for banking and financial organizations resistant to speech synthesis attacks.
Theoretical review of the emergence history and development of education for children with disabilities

The article discusses the history and development of education for children with disabilities. Ways to involve people with disabilities in inclusive education are explored, their integration into society, programs adopted by the state in this area and the identification of future steps to create a barrier-free environment for them are announced. It is stated that every child should take advantage of the opportunity for inclusive education and become an active member of society without being separated from the society that surrounds them, and no child should be excluded from education and society due to disability. In conclusion, it is argued that there are many problems faced by people with disabilities that pose serious challenges to the development of an inclusive society and to the development of an educated population in general. One of the main problems in our country is the lack of an inclusive education system. It is noted that one of the main factors that can lead to significant improvements in the lives of children with disabilities is the creation of accessible infrastructure. Thus, in the absence of accessible infrastructure, education-related programs cannot have any serious effect. Key words: Inclusive education, children with disabilities, special schools, education, secondary schools
Supplementary Education Within Transition Strategy Towards Post-Industrial Development Stage

The lead role in the post-industrial economy’s establishment of the social layer of highly qualified specialists’ formation determines the strategic importance of education. The article presents an analysis of the statistical indicators distribution characterizing the correlation of changes in the structure of GDP and economics of education. The analysis showed the relevance of supplementing basic education systems at all levels with informal flexible training courses. These courses allow adapting the acquired competencies to dynamic changes in the nature of labor and the structure of employment. The prospects of attracting potential of public-state partnership for the organization of additional adaptive training are considered. In such a case, basic education is combined with capabilities of educational public organizations that are not bound by the inertia of standards and agreeing on topics and methods.
Thinking Styles of Understanding Creative Mathematical Problems in the Process of Solving Them

The results of research of creative mathematical thinking are analyzed and the expediency of studying its individual differences through the analysis of mental styles of solving mathematical problems is stated. The aim of the article is to identify thinking mathematical styles of students and to analyze the influence of style on the process of understanding the creative mathematical problem. For identifying and determining the essence of mathematical thinking styles, the method of analysis of search actions of subjects during the solution of mathematical problems of different classes was used. The experiment involved 100 students of a technical university, 1000 processes for solving mathematical problems were analyzed. The results of the research. It is stated that mental mathematical style is a holistic system of interconnected actions, by means of which the mental mathematical result is achieved, which distinguishes the activities of one person from another one. The authors identify three thinking styles in creative mathematical process of students: the differential, the integral and the differential-integral. It is established that the thinking style is manifested during all processes micro-stages of understanding. It is proved that the different thinking mathematical styles lead to the formation of different purports of the same problem. It is established that unconscious mental guesses have different meanings in the process of understanding a mathematical problem by students with different styles of mathematical thinking. Conclusions. Thinking mathematical style is manifested throughout the process of understanding a mathematical problem, provides a different content of the search process aimed at understanding a mathematical problem.
Direction Matters: On the Implicit Bias of Stochastic Gradient Descent with Moderate Learning Rate

Understanding the algorithmic bias of \emph{stochastic gradient descent} (SGD) is one of the key challenges in modern machine learning and deep learning theory. Most of the existing works, however, focus on \emph{very small or even infinitesimal} learning rate regime, and fail to cover practical scenarios where the learning rate is \emph{moderate and annealing}. In this paper, we make an initial attempt to characterize the particular regularization effect of SGD in the moderate learning rate regime by studying its behavior for optimizing an overparameterized linear regression problem. In this case, SGD and GD are known to converge to the unique minimum-norm solution; however, with the moderate and annealing learning rate, we show that they exhibit different \emph{directional bias}: SGD converges along the large eigenvalue directions of the data matrix, while GD goes after the small eigenvalue directions. Furthermore, we show that such directional bias does matter when early stopping is adopted, where the SGD output is nearly optimal but the GD output is suboptimal. Finally, our theory explains several folk arts in practice used for SGD hyperparameter tuning, such as (1) linearly scaling the initial learning rate with batch size; and (2) overrunning SGD with high learning rate even when the loss stops decreasing.
E-learning, e-searching and e-resource management in the libraries

E-learning, e-searching and e-resource management in the libraries - IJLSIT- Print ISSN No: - 2582-1555 Online ISSN No:- 2456-9623 Article DOI No:- 10.18231/2456-9623.2018.0005, IP Indian Journal of Library Science and Information Technology-IP Indian J Libr Sci Inf Technol
PENERAPAN TEKNOLOGI FORTIGATE DALAM PEMBANGUNAN JARINGAN VPN-IP BERBASIS IPSEC

IPSec based VPN (Virtual Private Network) is a standard that provides data confidentiality, data integrity, and source authentication on public communication such as the internet. IPSec VPN is a secure technology and reliable (secure and reliable) which can connect private networks between campuses using internet public communications. The availability of data and services in real-time is very much needed by the main campus to ensure that operational and business activities at branch campus and main campus run smoothly. FortiGate is the best choice for system security that provides high protection against threats dynamic security and simplifies the IT security infrastructure company. This study aims to build a VPN (Virtual Private Network) based on IPSec. The research was conducted on a Wide Area Network using two FortiGate devices as a firewall and gateways are connected to each other and form a tunnel as a special path connect private networks between campuses securely. The results showed that clients at branch campus can access servers located at the main campus in real-time, and data transfer performance between branch campus and main campus was successfully accepted, and getting bigger packets sent, the file transfer process time will also be longer
Android Trainer Wawancara Pekerjaan Dalam Bahasa Inggris Menggunakan Audio Visual Dengan Metode Prototype

Job competition is getting tougher after the implementation of the ASEAN Economic Community. Prospective workers are required to have competence at work and be skilled at communicating using international languages, especially English. Based on the results of the questionnaire on 100 data samples using google form, there are as many as 50% having difficulty when interviewing using English. So this study is proposed to improve English pronunciation skills during job interviews. Android Trainer is self-training using the Android Platform. It is designed using Google Speech API and Speech Recognition Technology. It is displayed using Audio Visual and the development method uses the Prototype method. The result of this research is the Android Trainer which is a smart solution to make it easier to practice independently at any time. So that it can improve English pronunciation skills. This is useful when going through the interview process using English at various types of companies during the employee recruitment process.
The Human Factor at the Center of a Cyber Security Culture

With the issuing of the Resolution of 3 October 2017, on the fight against cybercrime, the European Parliament stressed once again that although the awareness about the risk posed by cybercrime has increased, “precautionary measures taken by individual users, public institutions and business, remain wholly inadequate, primarily due to lack of knowledge and resources”. (own emphasis) Consequently, there is a vital need to enable the end-users with easy-to-understand technical terminology, so that the goal becomes to maximise to the greatest extent feasible the human-factor as the strong link at the end of an IP address. Considering also the recent guidelines issued by ENISA on this topic, the paper proposes a simple, easy-to-implement model of a cyber-savvy digital user, as a possible way to approach the overall (mis)interpretation of “the human as the weakest link of cyber security”. Cyber security is often perceived as either belonging to the State or to organizations who can afford to implement it, or it is poorly put in place by small and medium size businesses due to financial constraints. This is, de facto, what is fuelling the perception of human as the weakest link of cyber security. Consequently, cyber security needs more recognition and increased visibility in the European Union. This paper proposes that the best manner to address this is via a human-centered approach to learning, trainings and awareness raising initiatives, tailored to suit all levels of digital literacy and regardless of demographics such as age or level of income.
'Language ... Without Metaphor'

Henry David Thoreau has been celebrated for his observation of the natural world. While noting Thoreau's skills of observation in relation to the natural world and his responsiveness to sensory experience, scholars have, however, tended to privilege sight over sound. Even though Thoreau was recognized by musicians such as Charles Ives and John Cage for having an exceptionally fine ear for the symphonies of nature, sound still remains a neglected aspect of Thoreau's Walden; Or, Life in the Woods. This article is a corrective to this status quo, as it reads Walden as a transmedial project in which Thoreau frequently tuned in to the sounds encountered during his sojourn in nature in order to figure the essential parameters of his experiment and to relate to the entire world of experience. The complex soundscape of Walden engenders a multifaceted awareness of modern space, as sounds of nature, sounds of progress, and the clamor of people intersect. Accordingly, this article explores how Thoreau uses a vast array of sounds to relate to the world; how he apprehended, and even appreciated, not only the harmonies of nature, but also dissonance—within nature, as well as between nature, modernity and rurality. In doing so, this article proposes a reading of Thoreau's auditory experience as a reflection on, and negotiation with, a multifaceted world where the pastoral and the industrial coexist.
Intelligent Voice Instructor-assistant System for Collaborative and Interactive Classes

College classes are becoming increasingly large. A critical component in scaling class size is the collaboration and interactions among instructors, teaching assistants, and students. We develop a prototype of anIntelligent Voice Instructor-AssistantSystem (IVIAS)for supportinglargeClasses,in whichAmazon Web Services, Alexa Voice Services and self-developed services are used. It uses ascraping service for reading the questions and answers from the past and current course discussion boards, organizes the questions in JSON format and stored them in thedatabase, which can be accessed by AWS Alexa skills. When a voice question from a student comes, Alexais used fortranslating the voice sentence into texts. Then Siamesedeep LSTM (Long Short-Term Memory)model is introducedto calculate the similarity between the question asked and the questions in the database to find the best-matched answer. Questions with no match will be sent to the instructor, and instructor’s answer will be added into the database. Experiments show that the implemented model achieve promising results that can lead to a practical system. IVIAS starts with a small set of questions. It can grow through learning and improving when more and more questions are asked and answered.