Reviewerly/exHarmony huggingface dataset:
A Study on the Democratization of Knowledge Promoted by E-Learning in China

E-learning users in China grew at a rate of 25.0% in 2015-2016, and the number of e-learning users exceeded 130 million in 2016. The number of users who take e-learning courses via mobile phone is growing at a rate of 85%, according to the 39th Report of Internet Development in China. In recent years, Chinese government has continuously issued policies to support the development of e-learning. More importantly, Premier Keqiang Li pointed out the needs of expanding e-Learning for the first time in the 2017 Chinese Government Work Report. As a new teaching mode, e-learning breaks the constraints of traditional teaching by providing access anytime, anywhere, and fast communication. It breaks many restrictions of traditional teaching mode. Various forms of e-learning are emerging, as citizens demand more e-learning. The unique attributes of e-learning and its diversified contents cannot only make individuals get access to education equally, but also better the allocation of educational resources.
Archival Photographs of the Writer D. A. Granin as a Historical Source

Daniil Alexandrovich Granin was an outstanding public figure, writer, and publicist.And yet his biography remains mostly unstudied
Construction of Resilient Boolean and Vectorial Boolean Functions with High Nonlinearity

Boolean functions and vectorial Boolean functions are the most important components of stream ciphers. Their cryptographic properties are crucial to the security of the underlying ciphers. And how to construct such functions with good cryptographic properties is a nice problem that worth to be investigated. In this paper, using two small nonlinear functions with t-1 resiliency, we provide a method on constructing t-resilient n variables Boolean functions with strictly almost optimal nonlinearity >2n-1-2n/2 and optimal algebraic degree n-t-1. Based on the method, we give another construction so that a large class of resilient vectorial Boolean functions can be obtained. It is shown that the vectorial Boolean functions also have strictly almost optimal nonlinearity and optimal algebraic degree.
Study of Information Architecture using Card Sorting Technique: A Case in Inland Revenue Board of Malaysia, Official Website

A high increase number of web applications can be seen from days to days where people foresee that searching information using a web portal make life easier and simplify daily task.Due to this demand, the increase numbers of online web applications are not only applied to e-commerce but also to academics and government field itself.Malaysia has been seen to keep in pace with this demand and many e-government websites are now accessible by the citizens online.By providing online platform, citizens are now having more options in resolving matters related to e-government and no longer need to always walk into the physical buildings.However, providing an online platform only is not reliable if the information or services placed in the web portal is not user-centred.This study will perform a study on current Information Architecture (IA) in Inland Revenue Board of Malaysia (Lembaga Hasil Dalam Negeri Malaysia (LHDN)) official website.Information Architecture (IA) is a discipline in determining an efficient way on how information in online website should be arranged in order to deliver information effectively and efficiently.From the preliminary study conducted, respondents felt that it is not easy to search for intended information on LHDN web portal as the arrangement of menu are not fulfil respondents' logical relationship.Web observation on LHDN official website has also carried out to observe and finally identified problems in term of information architecture.The objectives of this research are to investigate issues relating to information architecture on LHDN official web portal, to evaluate existing information arrangement used on LHDN official web portal using card sorting analysis and to propose a user centred Information Architecture's mainframe for LHDN official web portal.With the collected requirements from the questionnaire, this study will propose a user-centered information arrangement for LHDN official web portal.Card sorting technique will be used in this study to see respondents' logical mental model in arranging and grouping set of menus in LHDN official web portal.Findings from this study can be used a support alternative for the website architect to enhance LHDN information architecture hence all the information and services provided on the web portal can be fully utilized and improve users' browsing experience.
Perturbations are not Enough: Generating Adversarial Examples with Spatial Distortions

Deep neural network image classifiers are reported to be susceptible to adversarial evasion attacks, which use carefully crafted images created to mislead a classifier. Recently, various kinds of adversarial attack methods have been proposed, most of which focus on adding small perturbations to input images. Despite the success of existing approaches, the way to generate realistic adversarial images with small perturbations remains a challenging problem. In this paper, we aim to address this problem by proposing a novel adversarial method, which generates adversarial examples by imposing not only perturbations but also spatial distortions on input images, including scaling, rotation, shear, and translation. As humans are less susceptible to small spatial distortions, the proposed approach can produce visually more realistic attacks with smaller perturbations, able to deceive classifiers without affecting human predictions. We learn our method by amortized techniques with neural networks and generate adversarial examples efficiently by a forward pass of the networks. Extensive experiments on attacking different types of non-robustified classifiers and robust classifiers with defence show that our method has state-of-the-art performance in comparison with advanced attack parallels.
Towards Improving Solution Dominance with Incomparability Conditions: A case-study using Generator Itemset Mining

Finding interesting patterns is a challenging task in data mining. Constraint based mining is a well-known approach to this, and one for which constraint programming has been shown to be a well-suited and generic framework. Dominance programming has been proposed as an extension that can capture an even wider class of constraint-based mining problems, by allowing to compare relations between patterns. In this paper, in addition to specifying a dominance relation, we introduce the ability to specify an incomparability condition. Using these two concepts we devise a generic framework that can do a batch-wise search that avoids checking incomparable solutions. We extend the ESSENCE language and underlying modelling pipeline to support this. We use generator itemset mining problem as a test case and give a declarative specification for that. We also present preliminary experimental results on this specific problem class with a CP solver backend to show that using the incomparability condition during search can improve the efficiency of dominance programming and reduces the need for post-processing to filter dominated solutions.
Seamless design of information system architecture based on adaptive clustering method

The paper considers the concept of building the architecture of an information system that provides a seamless connection between architectural representations of various levels of abstraction. The concept is based on the application of the adaptive clustering method of information systems developed by the author. Seamless connection is understood as the presence of connections between elements of architectural models related to architectural representations of various levels of abstraction.
Adaptive video transmission over software defined networks

This paper presents the results of a study on the evaluation of adaptive transmission of video streams using the DASH technique on Software Defined Networks. There are also presented in this document, the description of the tools required for the implementation of the evaluation, as well as a description of the methodology used for the development of the experiments. In addition, the results of an adaptive transmission of a video by using DASH are presented. This transmission was carried out over a software defined network emulated on MININET. The results show that DASH technique easily allows to implement video streaming services that can adapt the quality of the transmission according to the resources available in the network.
On the role of features in human activity recognition.

Traditionally, the sliding window based activity recognition chain (ARC) has been dominating practical applications, in which features are carefully optimized towards scenario specifics. Recently, end-to-end, deep learning methods, that do not discriminate between representation learning and classifier optimization, have become very popular also for HAR using wearables, promising out-of-the-box modeling with superior recognition capabilities. In this paper, we revisit and analyze specifically the role feature representations play in HAR using wearables. In a systematic exploration we evaluate eight different feature extraction methods, including conventional heuristics and recent representation learning methods, and assess their capabilities for effective activity recognition on five benchmarks. Optimized feature learning integrated into the conventional ARC leads to comparable if not better recognition results as if using end-to-end learning methods, while at the same time offering practitioners more flexibility to optimize their systems towards specifics of wearables and their constraints and limitations.
PENGARUH KEPEMIMPINAN VISIONER KEPALA SEKOLAH DAN KINERJA MENGAJAR GURU TERHADAP EFEKTIVITAS SEKOLAH

This study was to analyze the influence of the visionary leadership and teacher performance on school effectiveness in Public Elementary Schools in Pameungpeuk District, Bandung Regency.The method used is descriptive with a quantitative approach.This research method takes samples from all populations with 30 respondents.1) visionary leadership influence on school effectiveness is in the moderate category, 2) the influence of teacher performance on school effectiveness is in the moderate category, 3) the influence of the visionary leadership and teacher performance on school effectiveness is categorized as medium other.Recommendations in this study are that schools must meet the 8 National Education Standards in order to increase school accreditation.
Efficient Symmetric Norm Regression via Linear Sketching

We provide efficient algorithms for overconstrained linear regression problems with size $n \times d$ when the loss function is a symmetric norm (a norm invariant under sign-flips and coordinate-permutations). An important class of symmetric norms are Orlicz norms, where for a function $G$ and a vector $y \in \mathbb{R}^n$, the corresponding Orlicz norm $\|y\|_G$ is defined as the unique value $\alpha$ such that $\sum_{i=1}^n G(|y_i|/\alpha) = 1$. When the loss function is an Orlicz norm, our algorithm produces a $(1 + \varepsilon)$-approximate solution for an arbitrarily small constant $\varepsilon > 0$ in input-sparsity time, improving over the previously best-known algorithm which produces a $d \cdot \mathrm{polylog} n$-approximate solution. When the loss function is a general symmetric norm, our algorithm produces a $\sqrt{d} \cdot \mathrm{polylog} n \cdot \mathrm{mmc}(\ell)$-approximate solution in input-sparsity time, where $\mathrm{mmc}(\ell)$ is a quantity related to the symmetric norm under consideration. To the best of our knowledge, this is the first input-sparsity time algorithm with provable guarantees for the general class of symmetric norm regression problem. Our results shed light on resolving the universal sketching problem for linear regression, and the techniques might be of independent interest to numerical linear algebra problems more broadly.
The efficacy of 4Cs-based reading to foster 21st-century learning competencies

The omnipresence of 4Cs at tertiary education has shifted the attention of language classroom teachings toward the mastery of multifaceted intelligences. Resultant teaching praxis subsequently calls forth students’ high literacy, which affects the nature and extent of success and failure. This study strived to scrutinize how the 4Cs approach in Reading courses scaffolds students’ multidimensional 21st-century learning competencies. Data were collected through online survey and focus group discussion, with deductive and inductive content analysis subsequently operative. The findings have shed lights on how 4Cs-based reading instructions help teachers to create learning environment commensurate with the demand of 21st-century learning, which aids students’ learning in gaining metacognitive tools for high literacy. With clear framework of collaborative work and scaffolding, teachers can trigger and further direct students’ achievement goals and social goals towards high literacy.
Contextual Local Explanation for Black Box Classifiers

We introduce a new model-agnostic explanation technique which explains the prediction of any classifier called CLE. CLE gives an faithful and interpretable explanation to the prediction, by approximating the model locally using an interpretable model. We demonstrate the flexibility of CLE by explaining different models for text, tabular and image classification, and the fidelity of it by doing simulated user experiments.
Eradicating Attacks on the Internal Network with Internal Network Policy

In this paper we present three attacks on private internal networks behind a NAT and a corresponding new protection mechanism, Internal Network Policy, to mitigate a wide range of attacks that penetrate internal networks behind a NAT. In the attack scenario, a victim is tricked to visit the attacker's website, which contains a malicious script that lets the attacker access the victim's internal network in different ways, including opening a port in the NAT or sending a sophisticated request to local devices. The first attack utilizes DNS Rebinding in a particular way, while the other two demonstrate different methods of attacking the network, based on application security vulnerabilities. Following the attacks, we provide a new browser security policy, Internal Network Policy (INP), which protects against these types of vulnerabilities and attacks. This policy is implemented in the browser just like Same Origin Policy (SOP) and prevents malicious access to internal resources by external entities.
Privacy Preserving Stochastic Channel-Based Federated Learning with Neural Network Pruning

Artificial neural network has achieved unprecedented success in a wide variety of domains such as classifying, predicting and recognizing objects. This success depends on the availability of big data since the training process requires massive and representative data sets. However, data collection is often prevented by privacy concerns and people want to take control over their sensitive information during both training and using processes. To address this problem, we propose a privacy-preserving method for the distributed system, Stochastic Channel-Based Federated Learning (SCBF), which enables the participants to train a high-performance model cooperatively without sharing their inputs. We design, implement and evaluate a channel-based update algorithm for the central server in a distributed system, which selects the channels with regard to the most active features in a training loop and uploads them as learned information from local datasets. A pruning process is applied to the algorithm based on the validation set, which serves as a model accelerator. In the experiment, our model presents equal performances and higher saturating speed than the Federated Averaging method which reveals all the parameters of local models to the server when updating. We also demonstrate that the converging rates could be increased by introducing a pruning process.
Finding a Meta-dialogue Strategy for Multi-intent Spoken Dialogue Systems

Speech is an easily accessible and highly intuitive modality of communication for humans. Maybe that is the reason why people have wanted to talk to computers almost from the moment the first computer was invented. Today several consumer-level products developed in the last few years have brought inexpensive voice assistants into everyday use. The problem is that this speech interfaces are mostly designed for certain commands. But during demanding tasks like driving a car, it can be useful to talk about several things at once, to get back to the main task as fast as possible. While talking about different things in a single utterance it is important to give the user adequate feedback, like a meta-dialogue informing about which topic is discussed at the moment. In this paper we compare several meta-dialogue approaches for a speech dialogue system capable of handling multi-intents. The aim of our study is to reveal which strategies users prefer regarding metrics such as flexibility, joy, and consistency. Our results show that explaining topic transitions and topic introductions via speech receive a high user rating and is cognitively less demanding then visual cues.
Entropy Penalty: Towards Generalization Beyond the IID Assumption

It has been shown that instead of learning actual object features, deep networks tend to exploit non-robust (spurious) discriminative features that are shared between training and test sets. Therefore, while they achieve state of the art performance on such test sets, they achieve poor generalization on out of distribution (OOD) samples where the IID (independent, identical distribution) assumption breaks and the distribution of non-robust features shifts. Through theoretical and empirical analysis, we show that this happens because maximum likelihood training (without appropriate regularization) leads the model to depend on all the correlations (including spurious ones) present between inputs and targets in the dataset. We then show evidence that the information bottleneck (IB) principle can address this problem. To do so, we propose a regularization approach based on IB, called Entropy Penalty, that reduces the model's dependence on spurious features-- features corresponding to such spurious correlations. This allows deep networks trained with Entropy Penalty to generalize well even under distribution shift of spurious features. As a controlled test-bed for evaluating our claim, we train deep networks with Entropy Penalty on a colored MNIST (C-MNIST) dataset and show that it is able to generalize well on vanilla MNIST, MNIST-M and SVHN datasets in addition to an OOD version of C-MNIST itself. The baseline regularization methods we compare against fail to generalize on this test-bed. Our code is available at https://github.com/salesforce/EntropyPenalty.
Self-reported summarizing and paraphrasing difficulties in L2 writing contexts: Some pedagogical interventions

The utility of paraphrasing and summarizing is key to effective academic writing. Both require that students have a good grasp of the source material not only in terms of understanding, analyzing, synthesizing, and evaluating the information found in the sources, but also in terms of selecting information, organizing, drafting, and revising their papers. Although several studies have identified L2 students’ difficulties when utilizing paraphrasing and summarizing, this paper examines the cognitive and metacognitive aspects of these complex activities from the students’ eyes. With essays from 120 freshman undergraduate students from a private university in Manila, this study qualitatively determined their reported difficulties when paraphrasing and summarizing, and the areas of improvements in these skills. Participant students identified four major areas of concern: 1) lack of proficiency in English, 2) poor reading comprehension skills, 3) lack of vocabulary and 4) lack of or poor documentation skills. Findings of this study provide grounds for presentation of pedagogical interventions/classroom instructions to address these students’ reported difficulties.
Softmax Is Not an Artificial Trick: An Information-Theoretic View of Softmax in Neural Networks

Despite great popularity of applying softmax to map the non-normalised outputs of a neural network to a probability distribution over predicting classes, this normalised exponential transformation still seems to be artificial. A theoretic framework that incorporates softmax as an intrinsic component is still lacking. In this paper, we view neural networks embedding softmax from an information-theoretic perspective. Under this view, we can naturally and mathematically derive log-softmax as an inherent component in a neural network for evaluating the conditional mutual information between network output vectors and labels given an input datum. We show that training deterministic neural networks through maximising log-softmax is equivalent to enlarging the conditional mutual information, i.e., feeding label information into network outputs. We also generalise our informative-theoretic perspective to neural networks with stochasticity and derive information upper and lower bounds of log-softmax. In theory, such an information-theoretic view offers rationality support for embedding softmax in neural networks; in practice, we eventually demonstrate a computer vision application example of how to employ our information-theoretic view to filter out targeted objects on images.
Protecting Access Privacy for Bitcoin Lightweight Client Using Trusted Hardware

Through decentralized ledger, Bitcoin rebuilds trust and achieves payment verification. However, the verification of Bitcoin transactions requires nodes to download the whole blockchain, which is infeasible for resource-constraint devices. Traditional schemes tried to transfer most of the storage and computation tasks from a lightweight client to a full node. Nevertheless, these schemes would severely violate users' privacy since they would leak transaction ID and public-key address when a lightweight client queries the full node. To address this, we propose a privacy-preserving approach to secure queries of simplified payment verification (SPV) clients. Specifically, we design a d-differentially private mechanism based on trusted hardware such that semi-honest adversaries cannot acquire the real access pattern during shards retrieval phase. Data-oblivious primitives are adopted to prevent the internal search pattern leakage owing to side-channel attacks. Our scheme is more secure than traditional SPV schemes, and it is proved to satisfy d-differential privacy definition. Our prototype implementation with the Bitcoin blockchain dataset shows its practicality.
How Transformer Revitalizes Character-based Neural Machine Translation: An Investigation on Japanese-Vietnamese Translation Systems

While translating between East Asian languages, many works have discovered clear advantages of using characters as the translation unit. Unfortunately, traditional recurrent neural machine translation systems hinder the practical usage of those character-based systems due to their architectural limitations. They are unfavorable in handling extremely long sequences as well as highly restricted in parallelizing the computations. In this paper, we demonstrate that the new transformer architecture can perform character-based translation better than the recurrent one. We conduct experiments on a low-resource language pair: Japanese-Vietnamese. Our models considerably outperform the state-of-the-art systems which employ word-based recurrent architectures.