Reviewerly/exHarmony huggingface dataset:
Quantum Boosting

Suppose we have a weak learning algorithm $\mathcal{A}$ for a Boolean-valued problem: $\mathcal{A}$ produces hypotheses whose bias $\gamma$ is small, only slightly better than random guessing (this could, for instance, be due to implementing $\mathcal{A}$ on a noisy device), can we boost the performance of $\mathcal{A}$ so that $\mathcal{A}$'s output is correct on $2/3$ of the inputs? Boosting is a technique that converts a weak and inaccurate machine learning algorithm into a strong accurate learning algorithm. The AdaBoost algorithm by Freund and Schapire (for which they were awarded the G\odel prize in 2003) is one of the widely used boosting algorithms, with many applications in theory and practice. Suppose we have a $\gamma$-weak learner for a Boolean concept class $C$ that takes time $R(C)$, then the time complexity of AdaBoost scales as $VC(C)\cdot poly(R(C), 1/\gamma)$, where $VC(C)$ is the $VC$-dimension of $C$. In this paper, we show how quantum techniques can improve the time complexity of classical AdaBoost. To this end, suppose we have a $\gamma$-weak quantum learner for a Boolean concept class $C$ that takes time $Q(C)$, we introduce a quantum boosting algorithm whose complexity scales as $\sqrt{VC(C)}\cdot poly(Q(C),1/\gamma);$ thereby achieving a quadratic quantum improvement over classical AdaBoost in terms of $VC(C)$.
Black-box Certification and Learning under Adversarial Perturbations

We formally study the problem of classification under adversarial perturbations, both from the learner's perspective, and from the viewpoint of a third-party who aims at certifying the robustness of a given black-box classifier. We analyze a PAC-type framework of semi-supervised learning and identify possibility and impossibility results for proper learning of VC-classes in this setting. We further introduce and study a new setting of black-box certification under limited query budget. We analyze this for various classes of predictors and types of perturbation. We also consider the viewpoint of a black-box adversary that aims at finding adversarial examples, showing that the existence of an adversary with polynomial query complexity implies the existence of a robust learner with small sample complexity.
Scalable Deep Generative Modeling for Sparse Graphs

Learning graph generative models is a challenging task for deep learning and has wide applicability to a range of domains like chemistry, biology and social science. However current deep neural methods suffer from limited scalability: for a graph with $n$ nodes and $m$ edges, existing deep neural methods require $\Omega(n^2)$ complexity by building up the adjacency matrix. On the other hand, many real world graphs are actually sparse in the sense that $m\ll n^2$. Based on this, we develop a novel autoregressive model, named BiGG, that utilizes this sparsity to avoid generating the full adjacency matrix, and importantly reduces the graph generation time complexity to $O((n + m)\log n)$. Furthermore, during training this autoregressive model can be parallelized with $O(\log n)$ synchronization stages, which makes it much more efficient than other autoregressive models that require $\Omega(n)$. Experiments on several benchmarks show that the proposed approach not only scales to orders of magnitude larger graphs than previously possible with deep autoregressive graph generative models, but also yields better graph generation quality.
Overflow Aware Quantization: Accelerating Neural Network Inference by Low-bit Multiply-Accumulate Operations

The inherent heavy computation of deep neural networks prevents their widespread applications. A widely used method for accelerating model inference is quantization, by replacing the input operands of a network using fixed-point values. Then the majority of computation costs focus on the integer matrix multiplication accumulation. In fact, high-bit accumulator leads to partially wasted computation and low-bit one typically suffers from numerical overflow. To address this problem, we propose an overflow aware quantization method by designing trainable adaptive fixed-point representation, to optimize the number of bits for each input tensor while prohibiting numeric overflow during the computation. With the proposed method, we are able to fully utilize the computing power to minimize the quantization loss and obtain optimized inference performance. To verify the effectiveness of our method, we conduct image classification, object detection, and semantic segmentation tasks on ImageNet, Pascal VOC, and COCO datasets, respectively. Experimental results demonstrate that the proposed method can achieve comparable performance with state-of-the-art quantization methods while accelerating the inference process by about 2 times.
Cross-lingual Sentiment Analysis via AAE and BiGRU

As a significant task in nature language processing (NLP), sentiment analysis has got more and more attention. Sufficient data and helpful tools are available when doing the studying. Therefore, it is meaningful to study techniques to leverage labeled data and models valid in rich datasets language when addressing any questions in rare language. As a result, cross-lingual sentiment analysis has become more and more popular. Compared with traditional translation method, transfer learning is the main trend and using deep learning to generate a cross-lingual word embeddings in the single vector space is more useful and stable. Hence getting the high quality of cross-lingual word embedding is the big problem which needs to be settled urgently. In this paper, we use the LSTM and Adversarial Auto Encoders (AAE) to generate contextual cross-lingual word embeddings for transfer learning, then the BiGRU is used for analyzing the sentiment. Experimental results prove that our method has the best performance.
Neural Generation of Dialogue Response Timings

The timings of spoken response offsets in human dialogue have been shown to vary based on contextual elements of the dialogue.We propose neural models that simulate the distributions of these response offsets, taking into account the response turn as well as the preceding turn.The models are designed to be integrated into the pipeline of an incremental spoken dialogue system (SDS).We evaluate our models using offline experiments as well as human listening tests.We show that human listeners consider certain response timings to be more natural based on the dialogue context.The introduction of these models into SDS pipelines could increase the perceived naturalness of interactions. 1
Designing Mangrove Ecology Self-Learning Application Based on a Micro-Learning Approach

the development of a mangrove ecology self-learning application integrates the advantages of mobile-based learning and the benefits of micro-learning into the virtual learning of mangrove ecology. The system was designed based on a case study in the Leeled mangrove forest, Thailand and encour-ages young learners to understand the value of mangrove forests, and to help to preserve them. The system developed uses a virtual learning environment and accommodates young learner’s behaviours, favouring micro-learning with the content organized into the learning units which take a maximum of 15 minutes to complete. The application therefore allows the learning to be integrated into the learners’ daily activities and can contribute to their life-long learning. It allows learners to conduct self-learning and gain experience from performance in a virtual environment which simulates a real mangrove forest. This approach is better suited to the needs of present day young peo-ple than traditional approaches to environmental education.
A Dictionary Learning Algorithm Based on Dictionary Reconstruction and Its Application in Face Recognition

In recent years, dictionary learning has received more and more attention in the study of face recognition. However, most dictionary learning algorithms directly use the original training samples to learn the dictionary, ignoring noise existing in the training samples. For example, there are differences between different images of the same subject due to changes in illumination, expression, etc. To address the above problems, this paper proposes the dictionary relearning algorithm (DRLA) based on locality constraint and label embedding, which can effectively reduce the influence of noise on the dictionary learning algorithm. In our proposed dictionary learning algorithm, first, the initial dictionary and coding coefficient matrix are directly obtained from the training samples, and then the original training samples are reconstructed by the product of the initial dictionary and coding coefficient matrix. Finally, the dictionary learning algorithm is reapplied to obtain a new dictionary and coding coefficient matrix, and the newly obtained dictionary and coding coefficient matrix are used for subsequent image classification. The dictionary reconstruction method can partially eliminate noise in the original training samples. Therefore, the proposed algorithm can obtain more robust classification results. The experimental results demonstrate that the proposed algorithm performs better in recognition accuracy than some state-of-the-art algorithms.
PENERAPAN MODEL PEMBELAJARAN SAVI BERBANTUAN MEDIA FLASHCARD UNTUK MENINGKATKAN AKTIVITAS DAN HASIL BELAJAR

&lt;p class="StyleAuthorBold"&gt;&lt;strong&gt;Abstrak&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;Penelitian bertujuan untuk meningkatkan aktivitas dan hasil belajar peserta didik, serta mengetahui proses pembelajaran menggunakan model pembelajaran SAVI berbantuan &lt;em&gt;flashcard&lt;/em&gt; materi Sistem Eksresi kelas VIII SMP Negeri 5 Bunut Hulu. Subjek penelitian yaitu 22 peserta didik kelas VIII. Metode penelitian menggunakan penelitian tindakan kelas yang dilakukan dalam dua siklus. Setiap siklus terdiri dari empat tahap, yaitu perencanaan, pelaksanaan tindakan, observasi, dan refleksi. Instrumen yang digunakan meliputi lembar observasi proses pembelajaran dan aktivitas belajar, serta soal tes hasil belajar. Teknik analisis data menggunakan statistik deskriptif. Hasil penelitian menunjukkan proses pembelajaran pada siklus I sebesar 92,31% dan siklus II mencapai 100%. Aktivitas belajar peserta didik pada siklus I sebesar 77,27% dan siklus II sebesar 90,91%. Hasil belajar menunjukkan jumlah peserta didik yang tuntas pada siklus I adalah 18 orang dengan persentase 81,82% dan siklus II adalah 21 orang dengan persentase 95,45%.&lt;/p&gt;&lt;p&gt; &lt;/p&gt;&lt;p class="StyleAuthorBold"&gt;&lt;strong&gt;&lt;em&gt;Abstract&lt;/em&gt;&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;&lt;em&gt;The research aimed to improve the activities and learning outcomes of students, as well as to find out the learning process using SAVI learning models assisted by flashcard at Expression System material for Class VIII SMP Negeri 5 Bunut Hulu. The research subjects were 22 students of class VIII. The research method used classroom action research conducted in two cycles. Each cycle consists of four stages, namely planning, implementing actions, observing, and reflecting. The instruments used observation sheets of the learning process and learning activities, as well as learning achievement test questions. Data analysis techniques used descriptive statistics. The results showed that the learning process in the first cycle was 92.31% and the second cycle reached 100%. Learning activities of students in the first cycle was 77.27% and the second cycle was 90.91%. Learning outcomes show the number of students who completed in the first cycle was 18 people with a percentage of 81.82% and the second cycle was 21 people with a percentage of 95.45%.&lt;/em&gt;&lt;/p&gt;
PANORAMA DE CONEXIÓN DURANTE LAS CLASES VIRTUALES EN UNA MUESTRA DE ESTUDIANTES UNIVERSITARIOS

This paper is a compilation of information obtained directly from a group of students and teachers from two University Centers belonging to the University of Guadalajara.The aim is to provide an overview of the development of virtual classes during the second part of the 2020-A school year (January-June 2020), in which, unexpectedly, both students and teachers were forced to work from confinement due to the risk of COVID-19 pandemic.It was found that a high percentage (from 68 to 84%) of students and teachers in the studied
Comparative Analysis of Homomorphic Encryption Algorithms Based on Learning with Errors

The widespread use of cloud technology allows optimizing the economic costs of maintaining the IT infrastructure of enterprises, but this increases the likelihood of theft of confidential data. One of the mechanisms to protect data from theft is cryptography. Using the classical primitives of symmetric and asymmetric encryption does not allow processing data in encrypted form. Homomorphic encryption is used for processing confidential data. Homomorphic encryption allows performing of arithmetic operations over encrypted text and obtaining an encrypted result that corresponds to the result of operations performed over plaintext. One of the perspective directions for constructing homomorphic ciphers is homomorphic ciphers based on Learning with Errors. In this paper we examine the cryptographic properties of existing homomorphic ciphers (CKKS, BFV) based on Learning with Errors, compare their technical characteristics: cryptographic strength and data redundancy, data encoding and decoding speed, speed of arithmetic operations, such as addition and multiplication, KeySwitching operation speed.
Two-stage human hair segmentation in the wild using deep shape prior

Human hair is a crucial biometric characteristic with rich color and texture information. In this paper, we propose a novel hair segmentation approach integrating a deep shape prior into a carefully designed two-stage Fully Convolutional Neural Network (FCNN) pipeline. First, we utilize a FCNN with an Atrous Spatial Pyramid Pooling (ASPP) module to train a human hair shape prior based on a specific distance transform. In the second stage, we combine the hair shape prior and the original image to form the input of a symmetric encoder-decoder FCNN with a border refinement module to get the final hair segmentation output. Both quantitative and qualitative results show that our method achieves state-of-the-art performance on the LFW-Part and Figaro1k datasets.
Obfuscation for Privacy-preserving Syntactic Parsing

The goal of homomorphic encryption is to encrypt data such that another party can operate on it without being explicitly exposed to the content of the original data.We introduce an idea for a privacy-preserving transformation on natural language data, inspired by homomorphic encryption.Our primary tool is obfuscation, relying on the properties of natural language.Specifically, a given English text is obfuscated using a neural model that aims to preserve the syntactic relationships of the original sentence so that the obfuscated sentence can be parsed instead of the original one.The model works at the word level, and learns to obfuscate each word separately by changing it into a new word that has a similar syntactic role.The text obfuscated by our model leads to better performance on three syntactic parsers (two dependency and one constituency parsers) in comparison to an upper-bound random substitution baseline.More specifically, the results demonstrate that as more terms are obfuscated (by their part of speech), the substitution upper bound significantly degrades, while the neural model maintains a relatively high performing parser.All of this is done without much sacrifice of privacy compared to the random substitution upper bound.We also further analyze the results, and discover that the substituted words have similar syntactic properties, but different semantic content, compared to the original words.
Multi-modal Information Extraction from Text, Semi-structured, and Tabular Data on the Web

How do we surface the large amount of information present in HTML documents on the Web, from news articles to Rotten Tomatoes pages to tables of sports scores?Such information can enable a variety of applications including knowledge base construction, question answering, recommendation, and more.In this tutorial, we present approaches for information extraction (IE) from Web data that can be differentiated along two key dimensions: 1) the diversity in data modality that is leveraged, e.g.text, visual, XML/HTML, and 2) the thrust to develop scalable approaches with zero to limited human supervision.
Scalable Soft Sensor for Nonlinear Industrial Big Data via Bagging Stochastic Variational Gaussian Processes

Traditional Gaussian process regression suffers from the cubic complexity and excessive computation burdens for industrial big data. To get rid of such defect, this work proposes a scalable soft sensor called bagging stochastic variational GP regression (SVGPR). We first formulate the Gaussian process within the sparse and variational approximation framework. Then, the stochastic variational inference (SVI) mechanism is induced, which can significantly break the formidable obstacle to nonlinear big data modeling. In addition, the imposed automatic relevance determination strategy will also leverage model interpretability with relevant feature weighting. Based on that, the bagging mechanism is encompassed by combining a set of distributed predictors to form a powerful ensemble model. As the results, prediction generalization is enhanced, whereas the stability is also well guaranteed. Both SVI and bagging allow for the parallel deployment. Therefore, a distributed diagram is developed for modeling and inference so that the bagging SVGPR can explore big data effectively and efficiently. For case study demonstrations, the proposed method is first evaluated on the numerical example, and then is applied in the real-time oxygen prediction of hydrogen manufacturing unit.
Improve Offensive Language Detection with Ensemble Classifiers

Sharing content easily on social media has become an important communication choice in the world we live.However, in addition to the conveniences it provides, some problems have been emerged because content sharing is not bounded by predefined rules.Consequently, offensive language has become a big problem for both social media and its users.In this article, it is aimed to detect offensive language in short text messages on Twitter.Since short texts do not contain sufficient statistical information, they have some drawbacks.To cope with these drawbacks of the short texts, semantic word expansion based on concept and word-embedding vectors are proposed.Then for classification task, decision tree and decision tree based ensemble classifiers such as Adaptive Boosting, Bootstrap Aggregating, Random Forest, Extremely Randomized Decision Tree and Extreme Gradient Boosting algorithms are used.Also the imbalanced dataset problem is solved by oversampling.Experiments on datasets have shown that the extremely randomized trees which takes word-embedding vectors as input are the most successful with an F-score of 85.66%.
Control Strategy of the Vehicle's Active Steer by Wire

Zhang, Q.; Lin, W.; Wang, Y.; Wu, X., and Luo, Y., 2020. Control strategy of the vehicle's active steer by wire. In: Yang, Y.; Mi, C.; Zhao, L., and Lam, S. (eds.), Global Topics and New Trends in Coastal Research: Port, Coastal and Ocean Engineering. Journal of Coastal Research, Special Issue No. 103, pp. 355–360. Coconut Creek (Florida), ISSN 0749-0208.The Steer-By-Wire (SBW) system is free from the constraints of mechanical structure. Whether the system can work normally depends on the correctness of the control strategy. Therefore, this paper will study the control strategy of active steering by wire, which includes the design of ideal variable angle ratio control and active front wheel steering controller. Feed-forward control of ideal variable angle transmission ratio was determined based on constant yaw angular velocity gain. The sliding mode variable structure control theory is adopted. Based on the yaw rate deviation, the integral sliding surface is constructed, and the control strategy of equivalent and switching control is designed and active front wheel steering controller is completed. Through feedback control, the active control of the vehicle's front wheel angle is achieved, which improves the stability of the vehicle. Finally, through the typical test conditions, the designed control strategy was simulated and verified, and compared with the simulation results of uncontrolled vehicles. The results showed that the sliding mode controller of active SBW front wheel designed by yaw angular velocity feedback under the ideal variable transmission ratio can run stably under any working conditions and achieve good track following ability.
Event Arguments Extraction via Dilate Gated Convolutional Neural Network With Enhanced Local Features

Event Extraction plays an important role in information-extraction to understand the world. Event extraction could be split into two subtasks: one is event trigger extraction, the other is event arguments extraction. However, the F1-Score of event arguments extraction is much lower than that of event trigger extraction, i.e. in the most recent work, event trigger extraction achieves 80.7%, while event arguments extraction achieves only 58%. In pipelined structures, the difficulty of event arguments extraction lies in its lack of classification feature, and the much higher computation consumption. In this work, we proposed a novel Event Extraction approach based on multi-layer Dilate Gated Convolutional Neural Network (EE-DGCNN) which has fewer parameters. In addition, enhanced local information is incorporated into word features, to assign event arguments roles for triggers predicted by the first subtask. The numerical experiments demonstrated significant performance improvement beyond state-of-art event extraction approaches on real-world datasets. Further analysis of extraction procedure is presented, as well as experiments are conducted to analyze impact factors related to the performance improvement.
Inducing Cooperation through Reward Reshaping based on Peer Evaluations in Deep Multi-Agent Reinforcement Learning

We propose a deep reinforcement learning algorithm for semi-cooperative multi-agent tasks, where agents are equipped with their separate reward functions, yet with some willingness to cooperate. It is intuitive that defining and directly maximizing a global reward function leads to cooperation because there is no concept of selfishness among agents. However, it may not be the best way of inducing such cooperation due to problems that arise from training multiple agents with a single reward (e.g., credit assignment). In addition, agents may intentionally be given separate reward functions to induce task prioritization whereas a global reward function may be difficult to define without diluting the effect of different tasks and causing their reward factors to be disregarded. Our algorithm, called Peer Evaluation-based Dual DQN (PED-DQN), proposes to give peer evaluation signals to observed agents, which quantify how they strategically value a certain transition. This exchange of peer evaluation among agents over time turns out to render agents to gradually reshape their reward functions so that their action choices from the myopic best response tend to result in a more cooperative joint action.