Reviewerly/exHarmony huggingface dataset:
Domain Discrepancy Measure Using Complex Models in Unsupervised Domain Adaptation.

Appropriately evaluating the discrepancy between domains is essential for the success of unsupervised domain adaptation. In this paper, we first point out that existing discrepancy measures are less informative when complex models such as deep neural networks are used, in addition to the facts that they can be computationally highly demanding and their range of applications is limited only to binary classification. We then propose a novel domain discrepancy measure, called the paired hypotheses discrepancy (PHD), to overcome these shortcomings. PHD is computationally efficient and applicable to multi-class classification. Through generalization error bound analysis, we theoretically show that PHD is effective even for complex models. Finally, we demonstrate the practical usefulness of PHD through experiments.
Implementation of Outdoor Games to Improve 4-5 Year Old Childrens Number Sense

This research implemented some outdoor games, which can improve students number sense, through a quasi-experiment project with one group pretest-posttest design in a kindergarten class (4-5 years old) in Bantul district. The students were invited to participate actively in outdoor games. They can contribute directly to the outdoor games which can develop their competence in number using learner-centred method and supported by the teachers. The right outdoor games can be identified as a way to improve students sense in numbers. Many teachers can also shift their perspectives from the old class management they had.
Multiple Connected Residual Network for Image Enhancement on Smartphones

Image enhancement on smartphones needs rapid processing speed with comparable performance. Recently, convolutional neural networks (CNNs) have achieved outstanding performance in image processing tasks such as image super-resolution and enhancement. In this paper, we propose a lightweight generator for image enhancement based on CNN to keep a balance between quality and speed, called multi-connected residual network (MCRN). The proposed network consists of one discriminator and one generator. The generator is a two-stage network: (1) The first stage extracts structural features; (2) the second stage focuses on enhancing perceptual visual quality. By utilizing the style of multiple connections, we achieve good performance in image enhancement while making our network converge fast. Experimental results demonstrate that the proposed method outperforms the state-of-the-art approaches in terms of the perceptual quality and runtime. The code is available at https://github.com/JieLiu95/MCRN .
Discriminative Learning of Filterbank Layer within Deep Neural Network Based Speech Recognition for Speaker Adaptation

Deep neural networks (DNNs) have achieved significant success in the field of automatic speech recognition. One main advantage of DNNs is automatic feature extraction without human intervention. However, adaptation under limited available data remains a major challenge for DNN-based systems because of their enormous free parameters. In this paper, we propose a filterbank-incorporated DNN that incorporates a filterbank layer that presents the filter shape/center frequency and a DNN-based acoustic model. The filterbank layer and the following networks of the proposed model are trained jointly by exploiting the advantages of the hierarchical feature extraction, while most systems use pre-defined mel-scale filterbank features as input acoustic features to DNNs. Filters in the filterbank layer are parameterized to represent speaker characteristics while minimizing a number of parameters. The optimization of one type of parameters corresponds to the Vocal Tract Length Normalization (VTLN), and another type corresponds to feature-space Maximum Linear Likelihood Regression (fMLLR) and feature-space Discriminative Linear Regression (fDLR). Since the filterbank layer consists of just a few parameters, it is advantageous in adaptation under limited available data. In the experiment, filterbank-incorporated DNNs showed effectiveness in speaker/gender adaptations under limited adaptation data. Experimental results on CSJ task demonstrate that the adaptation of proposed model showed 5.8% word error reduction ratio with 10 utterances against the un-adapted model.
Structural dynamic response analysis using deviations from idealized edge profiles in high-speed video

In our study, a deviation extraction method is introduced to obtain subtle deviation signals from structural idealized edge profiles. The deviations are employed to reconstruct an analysis matrix that consists of global translations along selected edge profiles, and then a singular value decomposition-based approach is proposed to extract valuable variations from the calculated analysis matrix. To avoid noises from textured edge profiles, a colorization optimization approach is applied to remove variations because of the textures and turn real image stripes into ones that satisfy the constant edge profile assumption more closely in the deviation extraction process. Two practical experiments are conducted to demonstrate the effectiveness and potential applications of our proposed method. The dynamic properties of a lightweight beam and a sound barrier are analyzed successfully by using high-speed videos.
Self- and Cross-Excitation in Stack Exchange Question &amp; Answer Communities

In this paper, we quantify the impact of self- and cross-excitation on the temporal development of user activity in Stack Exchange Question & Answer (Q&A) communities. We study differences in user excitation between growing and declining Stack Exchange communities, and between those dedicated to STEM and humanities topics by leveraging Hawkes processes. We find that growing communities exhibit early stage, high cross-excitation by a small core of power users reacting to the community as a whole, and strong long-term self-excitation in general and cross-excitation by casual users in particular, suggesting community openness towards less active users. Further, we observe that communities in the humanities exhibit long-term power user cross-excitation, whereas in STEM communities activity is more evenly distributed towards casual user self-excitation. We validate our findings via permutation tests and quantify the impact of these excitation effects with a range of prediction experiments. Our work enables researchers to quantitatively assess the evolution and activity potential of Q&A communities.
On Efficient Optimal Transport: An Analysis of Greedy and Accelerated Mirror Descent Algorithms

We provide theoretical analyses for two algorithms that solve the regularized optimal transport (OT) problem between two discrete probability measures with at most $n$ atoms. We show that a greedy variant of the classical Sinkhorn algorithm, known as the \emph{Greenkhorn algorithm}, can be improved to $\widetilde{\mathcal{O}}(n^2\varepsilon^{-2})$, improving on the best known complexity bound of $\widetilde{\mathcal{O}}(n^2\varepsilon^{-3})$. Notably, this matches the best known complexity bound for the Sinkhorn algorithm and helps explain why the Greenkhorn algorithm can outperform the Sinkhorn algorithm in practice. Our proof technique, which is based on a primal-dual formulation and a novel upper bound for the dual solution, also leads to a new class of algorithms that we refer to as \emph{adaptive primal-dual accelerated mirror descent} (APDAMD) algorithms. We prove that the complexity of these algorithms is $\widetilde{\mathcal{O}}(n^2\sqrtδ\varepsilon^{-1})$, where $δ&gt; 0$ refers to the inverse of the strong convexity module of Bregman divergence with respect to $\|\cdot\|_\infty$. This implies that the APDAMD algorithm is faster than the Sinkhorn and Greenkhorn algorithms in terms of $\varepsilon$. Experimental results on synthetic and real datasets demonstrate the favorable performance of the Greenkhorn and APDAMD algorithms in practice.
Don't feed the phish: how to avoid phishing attacks

We've all heard the phishing attack stories that start with someone receiving an email that requests an urgent invoice review or password change and ends with a data breach in which personal information is compromised and money is lost. Although many of us may roll our eyes at the prospect of falling for such an obvious scam, we must acknowledge that if those tricks didn't work, malicious actors wouldn't keep trying. Although many of us may roll our eyes at crude phishing attacks, we must acknowledge that if those tricks didn't work, malicious actors wouldn't keep trying. While many individuals have wised up to the cruder attempts on their bank details via email, education is clearly still needed for the silent majority that are still clicking through. People continue to respond to the humble phish and would-be hackers are still profiting from it. Adam Vincent of ThreatConnect lays out some essential guidelines for how not to get caught.
Kernel Estimation of Truncated Volterra Filter Model Based on DFP Technique and Its Application to Chaotic Time Series Prediction

In order to overcome some problems caused by improper parameters selection when applying Least mean square (LMS), Normalized LMS (NLMS) or Recursive least square (RLS) algorithms to estimate coefficients of second-order Volterra filter, a novel DavidonFletcher-Powell-based Second-order Volterra filter (DFPSOVF) is proposed. Analysis of computational complexity and stability are presented. Simulation results of system parameter identification show that the DFP algorithm has fast convergence and excellent robustness than LMS and RLS algorithm. Prediction results of applying DFPSOVF model to single step predictions for Lorenz chaotic time series illustrate stability and convergence and there have not divergence problems. For the measured multiframe speech signals, prediction accuracy using DFPSOVF model is better than that of Linear prediction (LP). The DFP-SOVF model can better predict chaotic time series and the real measured speech signal series.
Study of flood characteristic in Cikalumpang River by using 2D flood model

Flood is one of the natural disasters that occur in Indonesia frequently. At least there are 8.498 occurrences since 1980 through Indonesia. In order to prevent and resolve that natural disaster, some actions should be done. To know the best solution to solve flood, we should know the flood characteristic (what causes it, the inundation area, the height, how long it occurs, and etc.). The characteristic of flood will be easily known only when the flood occurs. Thus, it is necessary to build a model with high accuracy. The model was obtained by re-modeling the inundation incident that happened on March 7 th , 2018, and validated with observed data from several locations in that period. The result shows a good agreement with the observed data. This is evident from the extent and depth of the puddle from the modeling results similar to the value of field observation data. From the results, it can be concluded that the Cikalumpang River model has been obtained with a good correlation. From field observations, it is known that flooding is caused by quite complex problems, such as decreasing river capacity due to illegal building construction, sedimentation, land cover change, and climate change.
Connecting and Mapping LOD and CMDI Through Knowledge Organization

This paper explains the connection and mapping of knowledge representations between RDF and CMDI. Therefore, the challenge is to create a bridge between Linked Open Data (LOD) and the Component MetaData Infrastructure (CMDI) to ensure that the limits of the two paradigms are compensated and strengthened to create a new hybrid approach. While on the one hand, CMDI is easier to use for modelling purposes, the Metadata is not descriptive enough for a document to be easily discoverable using Linked Data (LD) technologies to publish and to enrich the document’s content. Yet on the other hand, the explicit semantics and high interoperability of LOD have many advantages, but its modelling process is too complex for non-expert users. Here we show how knowledge organization plays a crucial role in this issue.
Evaluasi Kualitas Website Program Studi Sistem Informasi Universitas PGRI Madiun Menggunakan Webqual 4.0

&#x0D; This study aims to determine the quality of the website of the Program Studi Sistem Informasi Universitas PGRI Madiun (or later called Prodi SI UNIPMA) with the Webqual 4.0 method which has 4 variables, namely quality of use, quality of information, interaction services and overall quality. The population in this study were students of SI UNIPMA Study Program where 21 respondents were taken as samples. Multiple linear regression analysis is used to test the relationship between variables Webqual 4.0 and student satisfaction. From the results of this study, it can be concluded that the most influential variable in satisfaction is the quality of information with a value of 14.131 and the smallest is the variable quality of use with a value of 2.266. So the recommendations for the website can be obtained is to increase the dimensions of the website's usefulness to students.
Programming Smart Contracts in Ethereum Blockchain using Solidity

This workshop introduces participants to programming smart contracts using Ethereum Blockchains and the Solidity programming language. Cryptocurrencies such as Bitcoins use Blockchains and Smart Contracts to enforce transactions. Given the popularity of Bitcoins and related technologies in the press, this module provides a module for CS educators to introduce the underlying technology into their classrooms. Participants receive handouts describing sample programming techniques and worksheets for creating basic smart contracts. The workshop proceeds in three sessions in which we: present the underlying technology of Ethereum; practice the creation of smart contracts using the Solidity programming language; and discuss the implementation of this module in our classrooms in small groups. Further information, sample code and workshop handouts are at: http://maui.hawaii.edu/cybersecurity. Note: A laptop is needed for this workshop and handouts will be given out.
Enhanced Gradient-Based Local Feature Descriptors by Saliency Map for Egocentric Action Recognition

Egocentric video analysis is an important tool in healthcare that serves a variety of purposes, such as memory aid systems and physical rehabilitation, and feature extraction is an indispensable process for such analysis. Local feature descriptors have been widely applied due to their simple implementation and reasonable efficiency and performance in applications. This paper proposes an enhanced spatial and temporal local feature descriptor extraction method to boost the performance of action classification. The approach allows local feature descriptors to take advantage of saliency maps, which provide insights into visual attention. The effectiveness of the proposed method was validated and evaluated by a comparative study, whose results demonstrated an improved accuracy of around 2%.
Using YouTube as a Platform to Learn Social Expression

Social expression is an essential English language skill that every child should possess.In order to have a good command of the language, a child should have mastered the rules of thumb in the social skill.However, ESL learners having many constraints in social conventions as they are not native speakers of English language.In the Malaysian context, 21st century learning skill which is communicative skill has been emphasised and given utmost importance (Pandian & Baboo, 2015).However, the social skills among the Malaysian students are at unsatisfactory level whereby the social convention has not been given top priority in Malaysian schools (Azman, 2012).This study is intended to unveil the effectiveness of using YouTube in teaching social expression.This innovation of incorporating ICT and social media platform (You-Tube) in teaching social expression could be an effective blended learning approach.This is because usage of YouTube, a prominent and familiar social media platform among pupils, could trigger pupils' responses in more conducive manner as it caters their multiple intelligences (Bakar, 2007).In this intervention, 60 Year five pupils from three national schools in Perak were selected as participants.6 YouTube videos, covering all the four language skills (Listening, Speaking, Reading and Writing) based activities were conducted to explore the effectiveness of this platform features in learning social expression.Pre and post-test and questionnaires were used as data collection methods.The main findings showed that the numbers of correct questions in post-test results increased significantly, thus portrayed understanding of social expression.As a conclusion, visualisation through YouTube is an ideal tool which could sustain participants' interest and wanting to acquire the language more.
MaskDGA: A Black-box Evasion Technique Against DGA Classifiers and Adversarial Defenses

Domain generation algorithms (DGAs) are commonly used by botnets to generate domain names through which bots can establish a resilient communication channel with their command and control servers. Recent publications presented deep learning, character-level classifiers that are able to detect algorithmically generated domain (AGD) names with high accuracy, and correspondingly, significantly reduce the effectiveness of DGAs for botnet communication. In this paper we present MaskDGA, a practical adversarial learning technique that adds perturbation to the character-level representation of algorithmically generated domain names in order to evade DGA classifiers, without the attacker having any knowledge about the DGA classifier's architecture and parameters. MaskDGA was evaluated using the DMD-2018 dataset of AGD names and four recently published DGA classifiers, in which the average F1-score of the classifiers degrades from 0.977 to 0.495 when applying the evasion technique. An additional evaluation was conducted using the same classifiers but with adversarial defenses implemented: adversarial re-training and distillation. The results of this evaluation show that MaskDGA can be used for improving the robustness of the character-level DGA classifiers against adversarial attacks, but that ideally DGA classifiers should incorporate additional features alongside character-level features that are demonstrated in this study to be vulnerable to adversarial attacks.
Efficient resource scheduling for the analysis of Big Data streams

The emergence of Big Data has had a profound impact on how data are analyzed. Open source distributed stream processing platforms have gained popularity for analyzing streaming Big Data as they provide low latency required for streaming Big Data applications using cluster resources. However, existi ng resource schedulers are still lacking the efficiency that Big Data analytical applications require. Recent works have already considered streaming Big Data characteristics to improve the efficiency of scheduling in the platforms. Nevertheless, they have not taken into account the specific attributes of analytical applications. This study, therefore, presents Bframework, an efficient resource scheduling framework used by streaming Big Data analysis applications based on cluster resources. Bframework proposes a query model using Directed Graphs (DGs) and introduces operator assignment and operator scheduling algorithms based on a novel partitioning algorithm. Bframework is highly adaptable to the fluctuation of streaming Big Data and the availability of cluster resources. Experiments with the benchmark and well-known real-world queries show that Bframework can significantly reduce the latency of streaming Big Data analysis queries up to about 65%.
Understanding the One-Pixel Attack: Propagation Maps and Locality Analysis

Deep neural networks were shown to be vulnerable to single pixel modifications. However, the reason behind such phenomena has never been elucidated. Here, we propose Propagation Maps which show the influence of the perturbation in each layer of the network. Propagation Maps reveal that even in extremely deep networks such as Resnet, modification in one pixel easily propagates until the last layer. In fact, this initial local perturbation is also shown to spread becoming a global one and reaching absolute difference values that are close to the maximum value of the original feature maps in a given layer. Moreover, we do a locality analysis in which we demonstrate that nearby pixels of the perturbed one in the one-pixel attack tend to share the same vulnerability, revealing that the main vulnerability lies in neither neurons nor pixels but receptive fields. Hopefully, the analysis conducted in this work together with a new technique called propagation maps shall shed light into the inner workings of other adversarial samples and be the basis of new defense systems to come.
LF-PPL: A Low-Level First Order Probabilistic Programming Language for Non-Differentiable Models

We develop a new Low-level, First-order Probabilistic Programming Language (LF-PPL) suited for models containing a mix of continuous, discrete, and/or piecewise-continuous variables. The key success of this language and its compilation scheme is in its ability to automatically distinguish parameters the density function is discontinuous with respect to, while further providing runtime checks for boundary crossings. This enables the introduction of new inference engines that are able to exploit gradient information, while remaining efficient for models which are not everywhere differentiable. We demonstrate this ability by incorporating a discontinuous Hamiltonian Monte Carlo (DHMC) inference engine that is able to deliver automated and efficient inference for non-differentiable models. Our system is backed up by a mathematical formalism that ensures that any model expressed in this language has a density with measure zero discontinuities to maintain the validity of the inference engine.
Deep Adversarial Network Alignment

Network alignment, in general, seeks to discover the hidden underlying correspondence between nodes across two (or more) networks when given their network structure. However, most existing network alignment methods have added assumptions of additional constraints to guide the alignment, such as having a set of seed node-node correspondences across the networks or the existence of side-information. Instead, we seek to develop a general network alignment algorithm that makes no additional assumptions. Recently, network embedding has proven effective in many network analysis tasks, but embeddings of different networks are not aligned. Thus, we present our Deep Adversarial Network Alignment (DANA) framework that first uses deep adversarial learning to discover complex mappings for aligning the embedding distributions of the two networks. Then, using our learned mapping functions, DANA performs an efficient nearest neighbor node alignment. We perform experiments on real world datasets to show the effectiveness of our framework for first aligning the graph embedding distributions and then discovering node alignments that outperform existing methods.