Reviewerly/exHarmony huggingface dataset:
The Coin Problem with Applications to Data Streams

Consider the problem of computing the majority of a stream of n i.i.d. uniformly random bits. This problem, known as the coin problem, is central to a number of counting problems in different data stream models. We show that any streaming algorithm for solving this problem with large constant advantage must use Ω(log n) bits of space. We extend our lower bound to proving tight lower bounds for solving multiple, randomly interleaved copies of the coin problem, as well as for solving the OR of multiple copies of a variant of the coin problem. Our proofs involve new measures of information complexity that are well-suited for data streams. We use these lower bounds to obtain a number of new results for data streams. In each case there is an underlying d dimensional vector x with additive updates to its coordinates given in a stream of length m. The input streams arising from our coin lower bound have nice distributional properties, and consequently for many problems for which we only had lower bounds in general turnstile streams, we now obtain the same lower bounds in more natural models, such as the bounded deletion model, in which ||x|| <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> never drops by a constant fraction of what it was earlier, or in the random order model, in which the updates are ordered randomly. In particular, in the bounded deletion model, we obtain nearly tight lower bounds for approximating ||x|| <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> up to additive error [1/(√k)]||x|| <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> , approximating ||x|| <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> up to a multiplicative ( 1+ε) factor (resolving a question of Jayaram and Woodruff in PODS 2018), and solving the Point Query and ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> -Heavy Hitters Problems. In the random order model, we also obtain new lower bounds for the Point Query and ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> -Heavy Hitters Problems. We also give new algorithms complementing our lower bounds and illustrating the tightness of the models we consider, including an algorithm for approximating ||x|| <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> up to additive error [1/(√k)]||x||2 in turnstile streams (resolving a question of Cormode in a 2006 IITK Workshop), and an algorithm for finding ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> -heavy hitters in randomly ordered insertion streams (which for random order streams, resolves a question of Nelson in a 2018 Warwick Workshop).
Recognizing Emotions from Texts using a Bert-Based Approach

The popularity of using pre-trained models results from the training ease and superior accuracy achieved in relatively shorter periods. The paper analyses the efficacy of utilizing transformer encoders on the ISEAR dataset for detecting emotions (i.e., anger, disgust, sadness, fear, joy, shame, and guilt). This work proposes a two-stage architecture. The first stage has the Bidirectional Encoder Representations from Transformers (BERT) model, which outputs into the second stage consisting of a Bi-LSTM classifier for predicting their emotion classes accordingly. The results, outperforming that of the state-of-the-art, with a higher weighted average F1 score of 0.73, become the new state-of-the-art in detecting emotions on the ISEAR dataset.
Abnormal crowd density estimation in aerial images based on the deep and handcrafted features fusion

Abnormal crowd analysis has always been an interesting research area since it ensures people’s safety and prevents crowd disasters in large scale events. In particular, crowd density estimation in aerial images has the advantage of monitoring open areas by covering a very large view and reaching difficult access areas. In this context, we proposed a new method for crowd density estimation in aerial images in order to detect crowded areas showing abnormal densities. The proposed method consists of two phases: an offline phase and an inference phase. The offline phase aimed to generate a crowd model using a fusion of relevant deep and handcrafted features selected via the minimum-redundancy maximum-relevance (mRMR) technique. However, in the inference phase, we made use of the already generated model to classify the aerial images patches into four classes: None, Sparse, Medium and Dense. Our main contributions lie in the fact we relied on the fusion of semantic and low-level information to encode crowd density patches, as well as, the definition of the most salient crowd features to reduce confusion between crowd density classes. The experimental evaluation of the proposed method proves the validity of these contributions as well as the effectiveness and efficiency of our method compared to the state-of-the-art reference methods.
Sentiment Analysis Implementing BERT-based Pre-trained Language Model for Vietnamese

Continuous Improvement Process Model contributes effectively to the educational development of any school. Sentiment analysis of student feedback is a step in this model to find suitable solutions to enhance the performance of instructors and the quality of material facilities. However, most of the state-of-the-art sentiment classification models only focus on English, by which some disadvantages in Vietnamese researches are brought on. We study a sentiment analysis model using PhoBERT pre-trained model for Vietnamese, which is a robust optimization for Vietnamese of the prominent BERT model. We then employ alternative fine-tuning techniques to generalize the model for multi-class classification other than the binary task. Our method achieves state-of-the-art results on the UIT-VSFC dataset with an F1-score of 93.92% and an accuracy of 94.28%. This is expected to be helpful for the improvement of Vietnam's education and set the foundation for researching in Vietnamese which is the language that lacks resources.
Secure Card-Based Cryptographic Protocols Using Private Operations Against Malicious Players

This paper shows new card-based cryptographic protocols using private operations that are secure against malicious players. Physical cards are used in card-based cryptographic protocols instead of computers. Operations that a player executes in a place where the other players cannot see are called private operations. Using several private operations, calculations of two variable boolean functions and copy operations were realized with the minimum number of cards. Though the private operations are very powerful in card-based cryptographic protocols, there is a problem that it is very hard to prevent malicious actions during private operations. Though most card-based protocols are discussed in the semi-honest model, there might be cases when the semi-honest model is not enough. Thus, this paper shows new protocols that are secure against malicious players. We show logical XOR, logical AND, and copy protocols, since we can execute any logical computations with a combination of these protocols. We use envelopes as an additional tool that can be easily prepared and used by people.
A Comparative Analysis of Machine Learning Models for Prediction of Passing Bachelor Admission Test in Life-Science Faculty of a Public University in Bangladesh

The usage of neoteric technology like data mining and machine learning in the interest of the educational welfare of the students has been dramatically soared. In this work, we concentrate on the educational amelioration of university admission test candidates using state of the art data mining and machine learning methods. From the outset, a dataset has been prepared by analyzing and utilizing the limited features which are most relevant for prediction. In this regard, the data collection process is performed based on the vis-à-vis survey, and data are taken from the students who participated in the bachelor admission test exam of the Life-Science faculty of Bangabandhu Sheikh Mujibur Rahman Science and Technology University (BSMRSTU). The dataset includes 343 instances with 10 variables. As machine learning methods are robust on balanced datasets, we defined two balanced datasets generated from the original set on the basis of two mostly used re-sampling techniques. Using five selected supervised machine learning classification methods; Logistic Regression, Support Vector Machines (SVM), Artificial Neural Networks (ANN), Random Forest, and Adaboost the prediction has been performed. To evaluate our models as well as check generalization capability to previously unseen data, we specified the most significant method called Stratified K-fold cross-validation and to measure the performance mostly usable matric like AUC Score, Precision, Recall, F-Measure has been specified. By performing a comprehensive analysis, it can be included Adaboost performs best in both datasets contrariwise SVM shows second-highest performance and dataset equipped through oversampling approach become more representative.
“Hieroglyphs of Protest”: Internet Memes and the Protest Movement in Russia

Political Internet memes are an underresearched phenomenon situated at the intersection of digital and political communication. Regarded as a unit of cultural information transmitted online, such a meme can be considered as both a manifestation of anonymous networked creativity and a mechanism of political participation. The article presents the results of an investigation into Internet memes generated by protest discourses on Runet (Russian Internet). The examination of Internet content allows us to draw conclusions as to the thematic emphases of protest actions represented in Runet's memosphere and the specifics of the image of Russian protest as reflected in memes.
Image captioning via proximal policy optimization

Image captioning is the task of generating captions of images in natural language. The training typically consists of two phases, first minimizing the XE (cross-entropy) loss, and then with RL (reinforcement learning) over CIDEr scores. Although there are many innovations in neural architectures, fewer works are proposed for the RL phase. Motivated by one recent state-of-the-art architecture X-Transformer [Pan et al., CVPR 2020], we apply PPO (Proximal Policy Optimization) to it to establish a further improvement. However, trivially applying a vanilla policy gradient objective function with the clipping form of PPO would not improve the result. Therefore, we introduce certain modifications. We show that PPO is capable of enforcing trust-region constraints effectively. Also, experimentally performance decreases when PPO is combined with the regularization technique dropout. We analyze the possible reason in terms of KL-divergence of RL policies. As to the baseline adopted in the policy gradient estimator of RL, it is generally sentence-level. So all words in the same sentence use the same baseline in the gradient estimator. We instead use a word-level baseline via Monte-Carlo estimation. Thus, different words can have different baseline values. With all these, by fine-tuning a pre-trained X-Transformer, we train a single model achieving a competitive result of 133.3% on the MSCOCO Karpathy test set. Source code is available at https://github.com/lezhang-thu/xtransformer-ppo.
An Improved UAV Aerial Image Mosaic Algorithm Based on GMS-RANSAC

The aerial image mosaic algorithm needs to ensure the real-time performance of the algorithm and the natural transition of image fusion. In order to improve the matching performance of the algorithm, an improved UAV aerial image registration algorithm based on GMS-RANSAC is proposed. The improved algorithm introduces the idea of partitioning, divides the image into meshes and then performs feature extraction on each mesh, uses the bidirectional BF algorithm and the GMS algorithm to perform accurate feature value matching, and finally uses the improved RANSAC algorithm for further feature purification to obtain a high-quality correct interior point set. This paper combines the characteristics of GMS algorithm to improve the RANSAC algorithm, reduce the number of iterations of the algorithm, and reduce the time complexity of the algorithm. The improved image registration algorithm has higher accuracy and shorter running time. After the registration is completed, the image is merged to obtain a mosaic image.
Partial Multi-label Learning using Label Compression

Partial multi-label learning (PML) aims at learning a robust multi-label classifier from partial multi-label data, where a sample is annotated with a set of candidate labels, while only a subset of those labels is valid. The existing PML algorithms generally suffer from the high computational cost when learning with large label spaces. In this paper, we introduce a PML approach (PML-LCom) that uses Label Compression to efficiently learn from partial multi-label data. PML-LCom firstly splits the observed label data matrix into a latent relevant label matrix and an irrelevant one, and then factorizes the relevant label matrix into two low-rank matrices, one encodes the compressed labels of samples, and the other explores the underlying label correlations. Next, it optimizes the coefficient matrix of the multi-label predictor with respect to the compressed label matrix. In addition, it regularizes the compressed label matrix with respect to the feature similarity of samples, and optimizes the label matrix and predictor in a coherent manner. Experimental results on both semi-synthetic and real-world PML datasets show that PML-LCom achieves a performance superior to the state-of-the-art solutions on predicting the labels of unlabeled samples with a large label space. The label compression improves both the effectiveness and efficiency, and the coherent optimization mutually benefits the label matrix and predictor.
Common dynamic factors for cryptocurrencies and multiple pair-trading statistical arbitrages

Abstract In this paper, we apply dynamic factor analysis to model the joint behaviour of Bitcoin, Ethereum, Litecoin and Monero, as a representative basket of the cryptocurrencies asset class. The empirical results suggest that the basket price is suitably described by a model with two dynamic factors. More precisely, we detect one integrated and one stationary factor until the end of August 2019 and two integrated factors afterwards. Based on this evidence, we define a multiple long-short trading strategy which proves profitable when the second factor is stationary.
Task-Adaptive Robot Learning From Demonstration With Gaussian Process Models Under Replication

Learning from Demonstration (LfD) is a paradigm that allows robots to learn complex manipulation tasks that can not be easily scripted, but can be demonstrated by a human teacher. One of the challenges of LfD is to enable robots to acquire skills that can be adapted to different scenarios. In this paper, we propose to achieve this by exploiting the variations in the demonstrations to retrieve an adaptive and robust policy, using Gaussian Process (GP) models. Adaptability is enhanced by incorporating task parameters into the model, which encode different specifications within the same task. With our formulation, these parameters can be either real, integer, or categorical. Furthermore, we propose a GP design that exploits the structure of replications, i.e., repeated demonstrations with identical conditions within data. Our method significantly reduces the computational cost of model fitting in complex tasks, where replications are essential to obtain a robust model. We illustrate our approach through several experiments on a handwritten letter demonstration dataset.
Web Scraping: From Tools to Related Legislation and Implementation Using Python

The Internet is the largest database of information ever built by mankind. It contains a wide variety of self-explanatory substances obtainable in varied designs such as audio/video, text, and others. However, the poorly designed data that largely fills up the Internet is difficult to extract and hard to use in an automated process. Web scraping cuts this manual job of extracting information and organizing information and provides an easy-to-use way to collect data from the webpages, convert it into some desired format, and store it in some local repository. Owing to the vast scope of applications of Web scraping ranging from lead generation to reputation and brand monitoring, from sentiment analysis to data augmentation in machine learning, many organizations use various tools to extract useful data. This study deals with different Web scraping tools and libraries, categorized into (i) Partial tools, (ii) Libraries and frameworks, and (iii) complete tools that have been developed over the last few years and is extensively used to collect data and convert into structured data to be used for text-processing applications. This paper explores the terms Web scraping and Web crawling, categorizes the tools available in the current market, and enables the reader to make their Web scraper using one such tool. The paper also comments on the legality associated with Web scraping at the end.
Revealing the Disintermediation Concept of Blockchain Technology

Typically, people have a misconception about blockchain as they associate this technology with cryptocurrency. This chapter does not focus, however, on bitcoin or cryptocurrencies that pertain to its intrinsic value. Rather, the authors focus on the disintermediation feature of blockchain technology by providing insights into how this technology could substitute for the functions and roles of the intermediary. The findings show that blockchain technology is not equipped with financing and physical distribution functions. The current research further demonstrates that most of the blockchain service providers that are listed in the Liechtenstein Blockchain Act are required to perform the traditional roles of an intermediary. Thus, blockchain technology is not found to support a full concept of disintermediation. This chapter is vital in order for existing intermediaries to gain a deeper understanding of how to analyze and optimize their existing functions and roles while adjusting their business model in the token-based economy.
PMLF: Prediction-Sampling-Based Multilayer-Structured Latent Factor Analysis

A latent factor (LF) model can implement efficient analysis for a high-dimensional and sparse (HiDS) matrix from recommender systems (RSs). However, an LF model's representation learning ability to a targeted HiDS matrix is heavily proportional to its known data density. Unfortunately, an HiDS matrix's known data are limited due to users' activity limitations in RSs. Motivated by this observation, this paper proposes a Prediction-sampling-based Multilayer-structured Latent Factor (PMLF) model. Following the principle of Deep Forest [1], PMLF implements a loosely-connected multilayered LF structure, where each layer generates synthetic ratings to enrich the input for the next layer. Such an injection process is carefully monitored through a random sampling process and nonlinear activations to avoid overfitting. Thus, PMLF's representation learning ability to an HiDS matrix is significantly enhanced owing to the carefully injected estimates and its generalized multilayer-structure. Experimental results on four HiDS matrices from industrial RSs indicate that compared with six state-of-the-art LF-based and deep neural networks-based models, PMLF well balances the prediction accuracy and computational efficiency, making it satisfy demands of fast and accurate industrial applications.
A new deep auto-encoder using multiscale reconstruction errors and weight update correlation

Deep Auto-Encoder (DAE) is a promising deep learning model to extract the features of data. However, it is difficult to achieve the desired network parameters by training due to the issue of inappropriate learning rate, which usually affects its performance of extracting features. In order to solve such an issue, in this paper we propose a new Variable Learning Speed DAE (VLSDAE) using Multiscale Reconstruction Errors (MRE) and Weights Update Correlation (WUC) to adaptively adjust its learning rate. Specifically, the reconstruction errors based on the multiscales, i.e. mini-batch, epoch and phase, are considered to reflect the macroscopic training effect. Moreover, for the sake of further representing the microscopic training state, WUC is proposed to depict the training effect of each neuron. We first give the complexity analysis and the theoretical proof for the convergence of VLSDAE. To further exhibit its performance, a parameter sensitivity analysis is then provided to investigate the effects of two key parameters. In addition, the proposed MRE, WUC and our integrated method are studied individually to reveal their learning effects. Finally, we compare VLSDAE with eight state-of-the-art algorithms. The experimental results demonstrate that VLSDAE outperforms the other eight competitors in terms of training error, classification accuracy, precision, recall and macro-F1.
Constrained Multi-scale Dense Connections for Accurate Biomedical Image Segmentation

Biomedical image segmentation plays a critical role in clinical diagnosis and medical intervention. Recently, a variety of deep neural networks have boosted the biomedical image segmentation performance with a large margin, which adopts dense connections to explore rich representations in multiple scales. In multi-scale dense connections, features from all or most scales are fused or iteratively aggregated. In this paper, we propose constrained multi-scale dense connections (CMDC) for accurate biomedical image segmentation, which only fuse features from the nearest scales containing the most relevant appearance or semantic information. Based on CMDC, we further construct constraint multi-scale dense networks (CMD-Net) by applying CMDC to existing segmentation networks. Experiments across various architectures (including FCN-8s, U-Net, and DeepLabV3) and datasets (including GlaS, CRAG, KID, and ECS) demonstrate that CMD-Net not only outperforms existing schemes on both accuracy and efficiency but also can be easily generalized to a variety of segmentation networks. In addition, CMD-Net achieves state-of-the-art performance on two instance segmentation datasets, GlaS and CRAG.