Reviewerly/exHarmony huggingface dataset:
ToxCCIn: Toxic Content Classification with Interpretability

Despite the recent successes of transformer-based models in terms of effectiveness on a variety of tasks, their decisions often remain opaque to humans. Explanations are particularly important for tasks like offensive language or toxicity detection on social media because a manual appeal process is often in place to dispute automatically flagged content. In this work, we propose a technique to improve the interpretability of these models, based on a simple and powerful assumption: a post is at least as toxic as its most toxic span. We incorporate this assumption into transformer models by scoring a post based on the maximum toxicity of its spans and augmenting the training process to identify correct spans. We find this approach effective and can produce explanations that exceed the quality of those provided by Logistic Regression analysis (often regarded as a highly-interpretable model), according to a human study.
A Stratified Optional Mathematical Model

This paper advocates a stratified optional mathematical model constructed via Singh and Mathur's model with comparative and Neyman provision and grander increase in productivity. The delinquent verbalized stays cracked through Lagrange multipliers method, and the optimum allocation is grown. Mathematical artworks stand prearranged in sustenance of the existing learning and the consequences verbalized over LINGO.
Bayesian Hierarchical Growth Model for Experimental Data on the Effectiveness of an Incentive-Based Weight Reduction Method

The objective of this current research is to model the experimental data on the effectiveness of an incentive-based weight reduction method by using Bayesian hierarchical growth models. Three Bayesian hierarchical growth models are proposed, namely parametric Bayesian hierarchical growth model with correlated intercept and slope random effects model, parametric Bayesian hierarchical growth model with no correlated intercept and slope random effects model and semi-parametric Bayesian hierarchical growth model with Dirichlet process mixture prior model. The data is obtained from forty eight (48) students who had participated in an experiment on weight reduction method. The students were divided equally into two groups: single and pair groups. The experiment was carried out over the period of three months with a weight reading session for every two weeks. At the end of the study, we had six repeated measures of each student’s weight in kg and some measures of covariates and factors. Our results showed that the best model for the above data based on the Bayesian fit indexes and the models’ flexibility is the semi-parametric Bayesian hierarchical growth model with Dirichlet process mixture prior model. The results of the semi-parametric model showed that the ‘growth’ or reduction rates of the weight reduction experiment relate to the students’ gender, height in cm, experimental group (single or pair) and time in term of weeks.
Identification of Phishing Urls Using Machine Learning

Abstract Phishing is a typical assault on unsuspecting individuals by making them to reveal their one-of-a-kind data utilizing fake sites. The target of phishing site URLs is to purloin the individual data like client name, passwords and web based financial exchanges. Phishers utilize the sites which are outwardly and semantically like those genuine sites. As innovation keeps on developing, phishing strategies began to advance quickly and this should be forestalled by utilizing against phishing systems to recognize phishing. AI is a useful asset used to endeavor against phishing assaults. We as a whole know bunches of assaults are happening continuously situation in light of phishing URLS. There is no programmed procedure has been set up so far Multiple assaults of phishing URLs has not yet coordinated. In the proposed framework finding the phishing assaults/URLs, the System will identify various phishing assaults in equal succession and caution the ordinary clients with respect to phishing URLs.
Research on Infiltration Teaching of Higher Vocational Mathematics Culture Based on Big Data

Abstract In response to the current information needs of higher vocational mathematics and cultural teaching, Hadoop framework and collaborative recommendation algorithms are used to apply these two technologies to the construction of a shared platform for higher vocational mathematics and cultural infiltration teaching. Then through experiment and comparison, let the same teacher conduct mathematics and cultural infiltration teaching in two different classes. The results show that by investigating higher vocational students’ interest in mathematics and their understanding of mathematics, the number of people interested in vocational mathematics classes increased by 14.6%, and the number of people interested in control classes only increased by 10%. At the same time, in the assessment of mathematics culture penetration knowledge points, the number of outstanding people in the experimental group was significantly more than that in the control group.
Translation Quality Analysis of Cultural Words in Translated Tourism Promotional Text of Central Java

&lt;em&gt;One of the key aspects of successful in availability of tourism promotional text (TPT) that enables tourists to understand fully tourism destinations in area. Because each region has a different culture, tourism promotional text (TPT) is very much needed for the convenience of tourists who visit Indonesia. The Government already provides it with 2 languages, Indonesian and English. But most of TPT in Indonesia are Indonesian to English translations. There must be an assurance whether tourists can comprehend cultural aspects that usually become untranslatable items. Mostly untranslatable aspects such as traditional food, traditional ceremonies, traditional objects and place names are not translated or use the original sentence. This problem really needs attention because it really affects the tourists who read TPT. Thus, this paper aims to analyze the translation quality of cultural words in translated TPT of Central Java. This paper also explains how to use translation for cultural words in TPT correctly, so the text can be useful for the tourist. In order to achieve this aim, the authors used descriptive qualitative research along with the utilization of Katan and Nababan techniques of data collection and analysis. The findings show that most cultural words in TPT used environment type based on Katan’s theory (2009). For the accuracy, it’s shows that most of them are less accurate based on Nababan’s theory (2012). &lt;/em&gt;
A Simple Matlab Simulink Model for Adaptive General Type-2 Fuzzy Logic Systems

The type-2 (T2) fuzzy logic systems (FLSs) are widely used in control problems. In the most of control problems the dynamics of the under control plant are uncertain and the T2-FLSs are frequently used for dynamics identification. Recently, a generalized version of T2-FLSs with higher estimation capability has been presented that is called general T2-FLSs (GT2-FLS). A simple Matlab Simulink Model is designed for online control problems. The effectiveness of the suggested model is shown by simulations.
Big Data Infrastructure for Marine Environment in the Arctic and Ice Silk Road

Abstract In this paper, the big data infrastructure for the marine environment in the Arctic and Ice Silk Road from National Marine Environmental Forecasting Center is taken as the main line, the construction of the Arctic observation system, the basic data construction of the Ice Silk Road and the construction of the high-performance computer system are described in detail, which clearly outlines the technical framework and construction progress of the Marine environmental data sources, data acquisition and transmission, data warehouse of the Arctic and Ice Silk Road. And from the perspective of climate change, the future of the Arctic and Ice Silk Road and the trend of corresponding marine environmental monitoring, warning (alarm), security assurance services and their big data infrastructure construction are prospected roughly.
Development of learning material in the form of a smartphone application on the subject of temperature and heat by inquiry-based learning (IBL) for physics high school subject

Abstract An smartphone application which are used as learning material on the subject of temperature and heat have been developed. The development process is carried out using research and development methods with reference to the Analysis-Design-Development-Development-Implementation-Evaluation development model. The novelty of learning material that was developed was to use inquiry-based learning in the writing system. In addition, the learning material which developed also includes questions that refer to higher-order thinking. The results of the validity tests show the adequacy of the learning materials with the level of difficulty of the learning materials in high school. The learning material has been tested in SMA 16 Jakarta. The test results show an increase in learning outcomes shown by the gain score of 0.4.
Self-distillation for German and Dutch dependency parsing

In this paper, we explore self-distillation as a means to improve statistical dependency parsing models for Dutch and German over purely supervised training. Self-distillation (Furlanello et al. 2018) trains a new student model on the output of an existing (weaker) teacher model. In contrast to most previous work on self-distillation, we perform distillation using a large, unannotated corpus. We show that in dependency parsing as sequence labeling (Spoustov´a and Spousta 2010, Strzyz et al. 2019), self-distillation plus finetuning provides large improvements over models that use supervised training. We carry out experiments on the German T¨uBa-D/Z universal dependency (UD) treebank (C¸ ¨oltekin et al. 2017) and the UD conversion of the Dutch Lassy Small treebank (Bouma and van Noord 2017). We find that self-distillation improves German parsing accuracy of a bidirectional LSTM parser from 92.23 to 94.33 Labeled Attachment Score (LAS). Similarly, on Dutch we see improvement from 89.89 to 91.84 LAS.
On Parallel Real-Time Security Improvement Using Mixed-Integer Programming

Network security defenses evolve, responding to real-time attack incidents, modifying the underlying topology, or reallocating defense systems across the network. The present work emphasizes reducing the time to compute new optimal reallocations of defense systems, responding to emerging real-time remote attacks. The proposed heuristic method utilizes parallel processing by slicing the underlying graphical model representing the network topology, solving in parallel multiple mixed-integer programming problems corresponding to the created subgraphs, and producing an estimate of the optimal defense. The parallelized method to compute a new defense enables producing a response, in real-time, before remote attackers compromise a target machine in the network. Our prototype tool to compute a new defense, the high-performance security analyzer, has a speedup of at least 20 over solving the original problem using a serial algorithm, and with an insignificant difference between the performance of the (computed in parallel) approximately optimal defense and the (serially computed) optimal defense. A major conclusion is that further speedups will come from parallel integer programming algorithms rather than from graph partitioning.
Digital Girls Summer Camp: Bridging the Gender ICT Divide

Women are currently severely underrepresented in the ICT field of study and in the ICT professions. This brings about severe gender gap at disadvantages in the access to new employment and wages and increased the risk to be left out from the digital revolution. Not only EQUAL-IST maps the gaps in ICT at the disadvantage of women in the different institutions and contexts involved in the partnership, but it also promotes actions to close the digital divide. One of these actions is a Summer Camp that is reserved to girls attending the third or fourth year of high schools to acquire knowledge in ICT by active learning implemented at the University of Modena and Reggio Emilia (UniMORE). The activity has shown to be successful in a twofold way: a) in terms of diffusion and replication, increasing the number of involved girls from the initial 35 participants of the first edition in 2014 in Modena to almost 130 girls participating in 2018 to the summer camps located in an increased number of sites, including locations outside of the region Emilia Romagna; b) in its capacity to reduce the digital divide with a real change in girls’ ICT knowledge and in their expectation with regards to future education and professional roles.
Head Detection Method for Indoor Scene

This paper proposes a trainable fully convolutional, end-to-end crowd head detector model, which is a single convolutional network architecture, applying for target bounding box prediction and classification tasks in images. Due to the network structure, this model is very lightweight in terms of parameters, and needs less calculation time and memory space. Experiments showed that our model had a better overall average precision than some head detection benchmark algorithms, and can achieve this goal by effective receiving domains of model’s network to choose different anchor sizes. By testing on the classic head detection datasets, it is concluded that our model can achieve a 0.69 AP result on the challenging head detection Brainwash dataset, which is comparable to those head detection benchmark methods. At the same time, the best result was achieved on the HollywoodHeads dataset. In addition to accuracy, our model also has the advantages of less parameters, less computing time, and less storage space requirements. It has a certain reference significance for making an intelligent monitoring system.
Extended target tracking for autonomous street crossing

Autonomous navigation on sidewalks and pedestrian areas is a complex problem, that requires the solution of different challenging tasks. One that is particularly hard to tackle is that of autonomous street crossing, which requires the robot to be aware of the position and speed of surrounding vehicles in order to decide whether is safe to cross. This work is dedicated to the development of an obstacle speed estimation algorithm to be applied to the context of autonomous navigation at crosswalks. In particular, a novel approach to the extended-target tracking problem is presented, which leverages a nested structure and a clustering algorithm that reduces the problem to a standard target tracking one. The effectiveness of the algorithm is demonstrated through testing on a prototype parcel-delivery robot operating in a real-world urban environment.
Pulsed thermography digital motion stabilization for the unmanned vehicle inspection of solar farms and GFRP wind blades through UAVs and UGVs

The quality control of structures and fuselages in both the wind-turbine and solar sectors is a fundamental part that allows a lifetime assessment of their elements, from its initial assembly to the recurring inspection cycles. Automating the active thermography on this scale, cannot be achieved with conventional industrial robots. Unmanned vehicles, such UAVs and UGVs, present distinctive advantages that should certainly be exploited, but, its inherent static motion is one of the main stumbling blocks towards its use in an active thermography inspection. In this paper, a two-step digital stabilization scheme has demonstrated its efficacy in real defects located in both a wind blade and solar panel. The combination of a featurebased registration algorithm and a dense parametric optical flow direct alignment has enabled the pseudo-static reconstruction of the thermograms. The adopted experimental methodology, employing a robot with both halogens and IR camera, subjected to random motions with varying speed and amplitudes, has allowed a direct repeatable comparison of static and stabilized phase images. The phase image contrast comparison of both static and dynamic tests, have been carried out on a flat bottom hole (FBH) wind blade GFRP sample, showing nearly identical phase contrast with marginal differences. Likewise, a real GFRP wind-blade impact delamination defect has also reached a close phase contrast regarding its counterpart, albeit with a decreased contrast. Additionally, the registration algorithm has been used to stitch the individual frames, derived from a dynamic recording of an electroluminescent solar panel, to allow for a unified detection and mapping of defects.
Drawing Competency Development Using the Atik Model in Kindergarten (TK)

Drawing competencies in Kindergarten (TK) can be developed by implementing theATIK Model in Bekasi City. The TK institutions chosen were TK Widya Bakti, TK Putra XII,TK Amira, and TK Baiturrahim. This research was conducted for 6 (six) months starting fromSeptember 2018 to February 2019. Research and Development (R&D) is one of the methods usedin this research, where there are 7 (seven) stages needed, namely 1. Research Analysis and NeedsAssessment, 2. Product Planning and Design, 3. Product Development 4. Preliminary field test,5. Product Revision, 6. Main Field Test, 7. Final Product Revision and Dissemination. The resultsof development and research based on effectiveness testing, it was found that the ATIK Modelwas declared effective in developing drawing competencies in Kindergarten, because it wasconsidered a new innovation to be implemented.
The use of virtual reality interfaces in the field of information security

Technologies of virtual and augmented reality are gaining popularity. Virtual reality is used in many areas, including beginning to be used in the field of information and computer security. In addition, virtual reality interfaces are also susceptible to attacks. However, there are still few works on research in this area. This article provides an overview of existing solutions to computer security problems using virtual and augmented reality interfaces, as well as an overview and classification of the identified threats for these interfaces themselves.
Scalable Gaussian Process Analysis for Implicit Physics-Based Covariance Models

The performance of Gaussian process analysis can be significantly affected by the choice of the covariance function. Physics-based covariance models provide a systematic way to construct covariance models that are consistent with the underlying physical laws. But the resulting models are still limited by the computational difficulties for large-scale problems. In this study, we propose a new framework combining low-rank approximations and physics-based covariance models to perform both accurate and efficient Gaussian process analysis for implicit models. The proposed approximations interact with the physical model via a black-box forward solver and can achieve quasilinear complexity for Gaussian process regression, maximum likelihood parameter estimations, and approximation of the expected Fisher information matrix when performing uncertainty quantification. We also propose a way to include higher-order terms in the covariance model to account for the nonlinearities. To accomplish the goal, we choose a specific global low-rank approximation of the covariance matrix and use stochastic trace estimators. Our numerical results demonstrate the effectiveness and scalability of the approach, validate the accuracy of maximum likelihood approximations and confidence intervals, and compare the performance favorably with other covariance models.
SQLSketch: Generating SQL Queries using a sketch-based approach

In the last decade, many intelligent interfaces and layers have been suggested to allow the use of relational databases and extraction of the content using only the natural language. However most of them struggle when exposed to new databases. In this article, we present SQLSketch, a sketch-based network for generating SQL queries to address the problem of automatically translate Natural Languages questions to SQL using the related databases schemas. We argue that the previous models that use full or partial sequence-to-sequence structure in the decoding phase can, in fact, have counter-effect on the generation operation and came up with more loss of the context or the meaning of the user question. In this regard, we use a full sketch-based structure that decouples the generation process into many small prediction modules. The SQLSketch is evaluated against GreatSQL, a new cross-domain, large-scale and balanced dataset for the Natural Language to SQL translation task. For a long-term aim of making better models and contributing in adding more improvements to the semantic parsing tasks, we propose the GreatSQL dataset as the first balanced cross-domain corpus that includes 45,969 pairs of natural language questions and their corresponding SQL queries in addition to simplified and well structured ground-truth annotations. We establish results for SQLSketch using GreatSQL dataset and compare the performance against two popular types of models that represent the sequential and partial-sketch based approaches. Experimental result shows that SQLSketch outperforms the baseline models by 13% in exact matching accuracy and achieve a score of 23.9% to be the new state-of-the-art model on GreatSQL.
Governance, Inequality and Inclusive Education in Sub-Saharan Africa

The study provides thresholds of income inequality that if exceeded will nullify the positive effect of governance dynamics on gender-inclusive education in 42 countries in sub-Saharan Africa for the period 2004-2014. The Generalized Method of Moments is used as an estimation strategy. The following findings are established. First, the unconditional effects of governance dynamics on inclusive education are consistently positive whereas the corresponding conditional effects from the interaction between inequality and governance dynamics are consistently negative. Second, the levels of inequality that completely crowd-out the positive incidence of governance on inclusive “primary and secondary education” are: 0.587 for the rule of law and 0.565 for corruption-control. Third, the levels of inequality that completely dampen the positive incidence of governance on inclusive “secondary education” are: 0.601 for “voice & accountability” and 0.700 for regulation quality. Fourth, for tertiary education, inequality thresholds are respectively 0.568 for political stability and 0.562 for corruption-control. The main policy implication is that for governance dynamics to promote inclusive education in the sampled countries, income inequality levels should be kept within the established thresholds. Other implications are discussed in the light of Sustainable Development Goals.