Reviewerly/exHarmony huggingface dataset:
Understanding Targeted Video-Ads in Children's Content

As the volume of online video entertainment via streaming increases, ever so more are users targeted by online advertisement algorithms. Nevertheless, this rise in targeting and revenue does not come without any concerns. That is, even though the online advertising business model has is very successful, nowadays, rising societal concerns regarding the ethics and extent to which such algorithms agree with the laws of different countries are also present. Motivated by the dichotomy above, we here explore how targeted video-ads meet the regulatory policies regarding children advertising in Brazil and Canada. To perform our study, we create synthetic user personas that watch YouTube videos daily. Our personas are tailored to stream children's content while controlling for several variables (e.g., gender, country, and type of content streamed). With the data gathered, our analyses reveal statistical evidence of algorithmic targeting in videos geared towards children. Also, some of the advertised products (e.g., alcoholic beverages and fast-food) go directly against the regulations of the studied countries. With advertisements being matched to users by machine learning algorithms, it is impossible to state whether regulations are not followed on purpose (e.g., advertisers gaming the system). Nevertheless, our findings and discussion do raise a flag that regulations may not be sufficient, and content providers may still need to audit systems to meet the regulations.
Revisiting Blockhain Use in Notary Services: An European Perspective

Notary services have long been identified as a recurrent example for dematerialisation through blockchain adoption, but has failed to become a world wide reality. The key issue being the distinct legal frameworks throughout the world. Europe in this context has a more restrictive legal context with regard to blockchain use. In this work, we briefly discuss the European role of the Notary, review the existing European solutions and identify related open issues that are not resolved in the existing solutions.
Optimization of Sentiment Analysis for Indonesian Presidential Election using Naïve Bayes and Particle Swarm Optimization

Twitter can be used to analyze sentiment to get public opinion about public figures to find a trend in positive or negative responses, especially to analyze sentiments related to presidential candidates in the 2019 election in Indonesia. Naive Bayes (NB) can be used to classify tweet feed into polarity class negative or positive, but it still has low accuracy. Therefore, this study optimizes the Naive Bayes algorithm with Particle Swarm Optimization (NB-PSO) to classify opinions from twitter feeds to get a good accuracy of public figures sentiment analysis. PSO used to select features to find optimization values to improve the accuracy of Naive Bayes. There are four steps to optimize NB using PSO, i.e., initializing the population (swarm), calculate the accuracy value that matched with selected features, selected the best accuracy of classification, and updating position and velocity. From this study, the group of tweets was obtained based on the positive and negative sentiments from the community towards two Indonesia presidential candidates in 2019. The NB-PSO test shows the accuracy result of 90.74%. The result of accuracy increases by 4.12% of the NB algorithm. In conclusion, the inclusion of the Particle Swarm Optimization algorithm for Naive Bayes classification algorithm gives a significant accuracy, especially for sentiment analysis cases.
Comparison of Template Matching Algorithm and Feature Extraction Algorithm in Sundanese Script Transliteration Application using Optical Character Recognition

The phenomenon that occurs in the area of West Java Province is that the people do not preserve their culture, especially regional literature, namely Sundanese script, in this digital era there is research on Sundanese script combined with applications using Feature Extraction algorithm, but there is no comparison with other algorithms and cannot recognize Sundanese numbers. Therefore, to develop the research a Sundanese script application was made with the implementation of OCR (Optical Character Recognition) using the Template Matching algorithm and the Feature Extraction algorithm that was modified with the pre-processing stages including using luminosity and thresholding algorithms, from the two algorithms compared to the accuracy and time values the process of recognizing digital writing and handwriting, the results of testing digital writing algorithm Matching algorithm has a value of 87% word recognition accuracy with 236 ms processing time and 97.6% character recognition accuracy with 227 ms processing time, Feature Extraction has 98% word recognition accuracy with 73.6 ms processing time and 100% character recognition accuracy with 66 ms processing time, for handwriting recognition in feature extraction character recognition has 83% accuracy and 75% word recognition , while template matching in character recognition has an accuracy of 70% and word recognition has an accuracy of 66%.
Comparative Analysis of the Performance of Six Models for the Estimation of Global Solar Radiation for Katsina, Nigeria

Solar radiation data is an essential pre-requisite for the designing, sizing and performing evaluation of any solar energy conversion system in any part of the globe, even though solar radiation data are not readily available for many location of many developing countries such as Nigeria, hence the needs to rely on empirical models.There are many developed models across the globe for the estimation of solar radiation, to identify the optimum performing model for locations such as Katsina requires the comparism between the various models.In this study, performance evaluation of six selected models for the estimation of global solar radiation was carried out for Katsina location, Nigeria.The models were formed from different combinations of some meteorological parameters (sunshine hours, relative humidity and temperatures) obtained for a period of ten years (2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015) from Nigerian Meteorological Agency (NIMET).Monthly average extraterrestrial global solar radiation was evaluated for the locations.The data was analysed to find the empirical constants for all the selected models in the locations.Estimated values of global solar radiation was obtained from the six selected models.The estimated values were then compared using statistical parameters (mean bias error (MBE), mean percentage error (MPE), root mean square error (RMSE) and coefficient of determinant (R 2 ).Model 6 was found to be the optimum model for Katsina because it fitted the measured data most for each month of year based on various statistical parameters used for the analysis.
Influence of metrics on determination of bifurcation points in time series of meteorological values

The influence of a metric kind and a method of setting the "dividing" polynomial on the results of finding bifurcation points in time series of meteorological values has been studied. The bifurcation point reflects the moment of change of the established system mode, caused by a significant change in the factors that generate the process under study. This article searches for a moment when the direction of the process changes in the time series of measurement data as the boundary between local trends. Zero to third degree polynomials have served as an indicator for determining the position of the bifurcation point. As the metrics allowing to determine the optimal position of these polynomials within the time series under investigation the following ones have been considered: standard deviation, Euclidean metric, Manhattan and relative distance. The statistical significance of the split is checked using the Fischer test. The proposed algorithm has been tested on time series of instantaneous and averaged air temperature and atmospheric pressure. The degree of the initial variation unexplained by the model is used as an estimate of the approximation accuracy. The use of Manhattan distance as a metric has been found to be the best for the time series considered. The approach proposed in this paper can be applied in the future, in particular, when determining the start time of climate change in different regions of our planet
Exploratory Overview on Breaking CAPTCHAs Using the Theory of the Consolidated Meta-Analytic Approach

This study sought to provide an integrating model of the main contributions of the literature on CAPTCHAs with an impact in this field. With the expansion of internet access, there is an increasing need for a mechanism to protect websites from attacks, although there are situations where it is interesting to be able to automate some activities. This work consisted of identifying the most influential CAPTCHA-related academic works and trends in the field, which could serve as a metric on what approaches to take when developing new studies. Data such as main authors, current lines of research and more prolific research centers are arrived at using the Theory of the Consolidated Meta-analytic Approach. Inputting the keyword “captcha” in the Web of Science database, 539 records were found, from 2001 to 2020. The main classes retrieved are: (a) Captcha in Security Context (31.9%), (b) Usability in Captcha Design (28%), (c) Captcha Recognition by AI (21.8%), (d) Captcha Approaches and Novel Implementation Proposals (18.2%).
Unique Authentication Model for the Management and Visualization of Web Components oriented to Services through LMS Platforms

Authentication over applications on the Internet, is a performed mechanism by most people to access their personal accounts. Into a educational environment, the LMS (Learning Management System) platforms allow access to academic material with the purpose of strengthening a lot of training processes. However, there are still limitations to access educational materials from different platforms, through a mechanism that allows a step through a single point of access. The following article aims to present the construction and implementation of a unique authentication model that allows mitigating security risks in the credential validation process for entry on different LMS platforms. Finally, a study case is presented where the proposed model is validated, with the purpose of generating a single access point through reliable authentication mechanisms on different LMS platforms.
ASSESSMENT ON PHISHING AVOIDANCE BEHAVIOUR AMONG INTERNET BANKING USERS IN NIGERIA: A CONCEPTUAL MODEL

Although acceptance of Internet Banking (IB) has improved among banking customers due to the suitability it offers, there are quite a few risks accompanying with its since it depends heavily towards the usage of Internet network, which has increased the chances of Phishing Attacks (PA). PA referred to as the most defiant of all information security threats and often perpetuated by conning user’s information systems to inadvertently disclose their personal information or by modifying or deleting sensitive information and maliciously destructing and destroying users’ resources Despite this huge enhancement, the ratio of usage has been relatively low, among IB users in Nigeria. This evidence indicates that there is an urgent requirement to investigate the factors behind the issue. Therefore, this study is conducted to develop a conceptual model based on Technology Threat Avoidance Theory (TTAT) to evaluate the PA among IB users in Nigeria and to enhance avoidance behaviour. This paper will present the initial investigation that leads to the development of the conceptual model. Researchers in this field can use the model in different populations and settings, and thus create an avenue in stopping the factors that contribute to the PA.
Revisiting Cloud Migration: Strategies and Methods

Abstract Cloud computing attracts users with its advantage of unlimited resource supply where resources can be elastically expanded on demand and balanced-load at the same time. This means that the application in the cloud environment should run in the way of elastic expansion cluster. At present, most of the elastic expansion and load balancing technologies provided by IaaS level are oriented to virtual machines (VM), with little consideration of application level, which does not fundamentally meet the needs of users. Based on this, starting from the motivation of cloud migration, we first analyse the cloud migration technology from three aspects: migration object, migration means and migration objectives, then explore the cloud migration criteria and four cloud migration strategies at the application level in the cloud environment, and point out that cloud migration should be optimized and maintained in terms of elastic expansion, load balancing, security, etc.
{ALERT}: Accurate Learning for Energy and Timeliness

An increasing number of software applications incorporate runtime Deep Neural Networks (DNNs) to process sensor data and return inference results to humans. Effective deployment of DNNs in these interactive scenarios requires meeting latency and accuracy constraints while minimizing energy, a problem exacerbated by common system dynamics. Prior approaches handle dynamics through either (1) system-oblivious DNN adaptation, which adjusts DNN latency/accuracy tradeoffs, or (2) application-oblivious system adaptation, which adjusts resources to change latency/energy tradeoffs. In contrast, this paper improves on the state-of-the-art by coordinating application- and system-level adaptation. ALERT, our runtime scheduler, uses a probabilistic model to detect environmental volatility and then simultaneously select both a DNN and a system resource configuration to meet latency, accuracy, and energy constraints. We evaluate ALERT on CPU and GPU platforms for image and speech tasks in dynamic environments. ALERT's holistic approach achieves more than 13% energy reduction, and 27% error reduction over prior approaches that adapt solely at the application or system level. Furthermore, ALERT incurs only 3% more energy consumption and 2% higher DNN-inference error than an oracle scheme with perfect application and system knowledge.
Perancangan Sistem Informasi Aplikasi Tracer Study Alumni Berbasis Website

Tracer Study is a study conducted by tertiary institutions to search for alumni whose purpose is to find out the reciprocal of alumni education in the world of work. National universities themselves already have a tracer study website but still have difficulty in carrying out tracer studies, especially to contact alumni. The method used in this study uses a structured waterfall method. In this case, the initial stage for this method is to make observations that aim to find and find out the problems faced by users and to collect data by searching literature studies related to website-based tracer studies to testing website testing systems using blackboxes, namely testing the function requirements of each menu in a program. This research resulted in the application of a website-based tracer study system that facilitates alumni to carry out the tracer study process which can be done anytime and anywhere. The results of the tracer study itself will serve as a benchmark for the university in developing the ability of future students to compete in the workforce.
Reference Markers in Introduction Section of Undergraduate Students’ Theses: A Corpus-based Systemic Functional Analysis

Cohesion has significant role in academic discourse. This study is conducted to investigate the reference marker used as grammatical cohesive device and why those are realized in the introduction section of the undergraduate students’ thesis. This analysis departs from the grammatical cohesion in systemic functional grammar theory. The AntConc software was used as analysis tool in corpus linguistic method to identify the frequency, lists, concordances and collocations of reference markers. The data source are taken from 90 thesis introduction written by English literature students of Faculty of Cultural Sciences, Universitas Sumatera Utara who graduated during 2017-2019 period and the data in the corpus is 120748 word-tokens. To improve the reliability of the data, the instances of Reference were confirmed by two raters. The findings are personal, demonstrative, and compative references found in the data which are dominated by the demonstrative reference. This domineering demonstrative reference marker explains that the students attempt to give the specification of the information in the data and to refer to the outside world shared between the writer and the reader.
Learning Effective Feature Representation against User Privacy Protection on Social Networks

Users pay increasing attention to their data privacy in online social networks, resulting in hiding personal information, such as profile attributes and social connections. While network representation learning (NRL) is widely effective in social network analysis (SNA) tasks, it is essential to learn effective node embeddings from privacy-protected sparse and incomplete network data. In this work, we present a novel NRL model to generate node embeddings that can afford data incompleteness coming from user privacy protection. We propose a structure-attribute enhanced matrix (SAEM) to alleviate data sparsity and develop a community-cluster informed NRL method, c2n2v, to further improve the quality of embedding learning. Experiments conducted across three datasets, three simulations of user privacy protection, and three downstream SNA tasks exhibit the promising performance of our NRL model SAEM+c2n2v.
An Extension Method to Construct &lt;i&gt;M&lt;/i&gt;-Ary Sequences of Period 4&lt;i&gt;N&lt;/i&gt; with Low Autocorrelation

Pseudorandom sequences with low autocorrelation magnitude play important roles in various environments. Let N be a prime with N=Mf+1, where M and f are positive integers. A new method to construct M-sequences of period 4N is given. We show that these new sequences have low autocorrelation magnitude.
Improve MPTCP Transmission Performance Based on Quality Evaluation Algorithm in High-loss Network Environment

When using a wireless network to transmit data, especially for terminal devices that have a large amount of data to transmit, a single link often cannot meet their own needs. Therefore, compared to single-link devices, devices with multiple network interfaces have the ability to increase their own network throughput. However, wireless networks have high latency and high packet loss rates when moving at high speeds. And because of different operators, the link conditions are also very different. MPTCP has good parallel transmission performance under low latency, but under harsh environments, the performance of MPTCP rapidly decreases due to the influence of bad links. This paper proposes a work flow screening algorithm based on quality evaluation, which can improve the performance of MPTCP in high-loss network environments. The feasibility is verified by simulation.
Some Analytical Considerations Regarding the Traveling Salesman Problem Solved with Wolfram Mathematica Applications

The paper presents an introduction to the Ant Colony Optimisation (ACO) algorithm and methods for solving the Travelling Salesman Problem (TSP). Documenting, understanding and knowledge of concepts regarding the emergent behavior and intelligence swarms optimization, easily led on solving the Travelling Salesman Problem using a computational program, such as Mathematics Wolfram via Creative Demostration Projects (*.cdf) module.&#x0D; The proposed application runs for a different number of ants, a different number of ants, a different number of leaders (elite ants), and a different pheromone evaporation index. As a result it can be stated that the execution time of the algorithm to solve the TSP is direct and strictly proportional to the number of ants, cities and elite ants considered, the increase of the execution time increasing significantly with the increase of the variables.
Feature extraction in batik image geometric motif using canny edge detection

Muhammad Fikri Hidayattullah et.al (Feature extraction in batik image...)filter on a grayscale image, calculation of gradient intensity, Non-Maximum Suppression, determination of double threshold values, and Edge Tracking with Hysteresis.The final stage of this series is obtaining a new image that is only displaying the edge area of a batik image. IV. Result and DiscussionAll geometric motifs batik's image datasets are carried out one by one edge detection process.The results of the edge detection stage can be seen in Table 1.
Text-Based Intent Analysis using Deep Learning

Intent classification focuses on analyzing input to gauge the users’ needs and opinions. With this proposed system, we utilise a neural network classifier that analyzes text strings that are fed to it as input. Based on the analyzed intent, the system responds to the user by acting upon the commands issued to it. The areas covered by the system include weather, ratings, and booking restaurants. Firstly the intent of the text will be analysed and then it will be classified based on the topic it belongs to. Due to expressiveness of intent by users intent , for instance ”need a rental space”, ”places to visit in chennai” intent analysis plays a major role in ecommerce, real estate and online marketing industry. We aim to maximize the accuracy and minimize the errors and the time complextity of the proposed system
Environment Classification for Unmanned Aerial Vehicle Using Convolutional Neural Networks

Environment classification is one of the most critical tasks for Unmanned Aerial Vehicles (UAV). Since water accumulation may destabilize UAV, clouds must be detected and avoided. In a previous work presented by the authors, Superpixel Segmentation (SPS) descriptors with low computational cost are used to classify ground, sky, and clouds. In this paper, an enhanced approach to classify the environment in those three classes is presented. The proposed scheme consists of a Convolutional Neural Network (CNN) trained with a dataset generated by both, an human expert and a Support Vector Machine (SVM) to capture context and precise localization. The advantage of using this approach is that the CNN classifies each pixel, instead of a cluster like in SPS, which improves the resolution of the classification, also, is less tedious for the human expert to generate a few training samples instead of the normal amount that it is required. This proposal is implemented for images obtained from video and photographic cameras mounted on a UAV facing in the same direction of the vehicle flight. Experimental results and comparison with other approaches are shown to demonstrate the effectiveness of the algorithm.
Cloud-Enabled Interactive Sound Propagation for Untethered Mixed Reality

We describe the first system for physically-based wave acoustics including diffraction effects within a holographic experience shared by multiple untethered devices. Our system scales across standalone mobile-class devices, from a HoloLens to a modern smart phone. Audio propagation in real-world scenes exhibits perceptually salient effects that complement visuals. These include diffraction losses from obstruction, re-direction (“portaling”) of sounds around physical doorways and corners, and reverberation in complex geometries with multiple connected spaces. Such effects are necessary in mixed reality to achieve a sense of presence for virtual people and things within the real world, but have so far been computationally infeasible on mobile devices. We propose a novel cloud-enabled system that enables such immersive audio-visual scenarios on untethered mixed reality devices for the first time. Talk and Demo can be viewed here We propose a novel system architecture where an “acoustic map” in the cloud stores acoustic simulation data that are bound to various locations in the world (such as via Azure Spatial Anchors).