Reviewerly/exHarmony huggingface dataset:
Coverage-Based Classification Using Association Rule Mining

Building accurate and compact classifiers in real-world applications is one of the crucial tasks in data mining nowadays. In this paper, we propose a new method that can reduce the number of class association rules produced by classical class association rule classifiers, while maintaining an accurate classification model that is comparable to the ones generated by state-of-the-art classification algorithms. More precisely, we propose a new associative classifier that selects “strong” class association rules based on overall coverage of the learning set. The advantage of the proposed classifier is that it generates significantly smaller rules on bigger datasets compared to traditional classifiers while maintaining the classification accuracy. We also discuss how the overall coverage of such classifiers affects their classification accuracy. Performed experiments measuring classification accuracy, number of classification rules and other relevance measures such as precision, recall and f-measure on 12 real-life datasets from the UCI ML repository (Dua, D.; Graff, C. UCI Machine Learning Repository. Irvine, CA: University of California, 2019) show that our method was comparable to 8 other well-known rule-based classification algorithms. It achieved the second-highest average accuracy (84.9%) and the best result in terms of average number of rules among all classification methods. Although not achieving the best results in terms of classification accuracy, our method proved to be producing compact and understandable classifiers by exhaustively searching the entire example space.
Privacy Risk and Preservation in Contact Tracing of COVID‐19

A variety of types of information has been collected during the COVID-19 pandemic that can lead to privacy concerns Depending on data type, the approaches and measures taken to mitigate privacy concerns can be different The privacy issues incurred by collecting and sharing location and contact tracing data have their own importance While contact tracing has proved useful in tracking and slowing down the spread of COVID-19 and plays an important role in fighting the pandemic, major newspapers such as the Washington Post and Forbes;the Reuters news agency;and government agencies have raised red flags about the high privacy risk associated with this process The information collected during contact tracing often includes detailed and frequent location data that lead to inferences about the private social life and health status of individuals
Contextual Multi-Scale Feature Learning for Person Re-Identification

Representing features at multiple scales is significant for person re-identification (Re-ID). Most existing methods learn the multi-scale features by stacking streams and convolutions without considering the cooperation of multiple scales at a granular level. However, most scales are more discriminative only when they integrate other scales as contextual information. We termed that contextual multi-scale. In this paper, we proposed a novel architecture, namely contextual multi-scale network (CMSNet), for learning common and contextual multi-scale representations simultaneously. The building block of CMSNet obtains contextual multi-scale representations by bidirectionally hierarchical connection groups: the forward hierarchical connection group for stepwise inter-scale information fusion and the backward hierarchical connection group for leap-frogging inter-scale information fusion. Too rich scale features without a selection will confuse the discrimination. Additionally, we introduced a new channel-wise scale selection module to dynamically select scale features for corresponding input image. To the best of our knowledge, CMSNet is the most lightweight model for person Re-ID and it achieves state-of-the-art performance on four commonly used Re-ID datasets, surpassing most large-scale models.
Optimized recurrent neural network with fuzzy classifier for data prediction using hybrid optimization algorithm: Scope towards diverse applications

Today, data processing has become a challenging task due to the significant increase in the amount of data collected using various sensors. To put up knowledge and forecast the data, the existing data mining techniques compute all numerical attributes in the memory simultaneously. However, the over-abundance of entire factors in the data makes accurate prediction infeasible. This paper attempts to implement a new data prediction model using an optimized machine learning algorithm. The proposed data prediction model involves four main phases: (a) data acquisition, (b) feature extraction, (c) data normalization, and (d) prediction. Initially, few data from the UCI repository like Bike Sharing Dataset, Carbon Nanotubes, Concrete Compressive Strength, Electrical Grid Stability Simulated Data, and SkillCraft-1 Master Table are collected. Further, the feature extraction process extracts the first-order statistics like mean, median, standard deviation, the maximum value of entire data, and the minimum value of entire data, and the second-order statistics like kurtosis, skewness, energy, and entropy. Next, the data or feature normalization is done to arrange the data within a certain limit. The normalized features are then subjected to a hybrid prediction system by integrating the Recurrent Neural Network (RNN) and Fuzzy Regression model. As a modification, the number of hidden neurons in the RNN and membership limits of the Fuzzy Regression model are optimized by a hybrid optimization algorithm by merging the concepts of Whale Optimization Algorithm (WOA) and Cat Swarm Optimization (CSO), which is called the Whale Updated Seek Mode-based CSO (WS-CSO) algorithm. Then, the efficiency of the optimized hybrid classifier for all-time prediction of data in different applications is confirmed based on its valuable performance and comparative analysis.
Advanced Multi-Instance Learning Method with Multi-features Engineering and Conservative Optimization for Engagement Intensity Prediction

This paper proposes an advanced multi-instance learning method with multi-features engineering and conservative optimization for engagement intensity prediction. It was applied to the EmotiW Challenge 2020 and the results demonstrated the proposed method's good performance. The task is to predict the engagement level when a subject-student is watching an educational video under a range of conditions and in various environments. As engagement intensity has a strong correlation with facial movements, upper-body posture movements and overall environmental movements in a given time interval, we extract and incorporate these motion features into a deep regression model consisting of layers with a combination of long short-term memory(LSTM), gated recurrent unit (GRU) and a fully connected layer. In order to precisely and robustly predict the engagement level in a long video with various situations such as darkness and complex backgrounds, a multi-features engineering function is used to extract synchronized multi-model features in a given period of time by considering both short-term and long-term dependencies. Based on these well-processed engineered multi-features, in the 1st training stage, we train and generate the best models covering all the model configurations to maximize validation accuracy. Furthermore, in the 2nd training stage, to avoid the overfitting problem attributable to the extremely small engagement dataset, we conduct conservative optimization by applying a single Bi-LSTM layer with only 16 units to minimize the overfitting, and split the engagement dataset (train + validation) with 5-fold cross validation (stratified k-fold) to train a conservative model. The proposed method, by using decision-level ensemble for the two training stages' models, finally win the second place in the challenge (MSE: 0.061110 on the testing set).
A Watermarking Protocol Based on Blockchain

Digital watermarking can be used to implement mechanisms aimed at protecting the copyright of digital content distributed on the Internet. Such mechanisms support copyright identification and content tracking by enabling content providers to embed perceptually invisible watermarks into the distributed copies of content. They are employed in conjunction with watermarking protocols, which define the schemes of the web transactions by which buyers can securely purchase protected digital content distributed by content providers. In this regard, the “buyer friendly” and “mediated” watermarking protocols can ensure both a correct content protection and an easy participation of buyers in the transactions by which to purchase the distributed content. They represent a valid alternative to the classic “buyer and seller” watermarking protocols documented in the literature. However, their protection schemes could be further improved and simplified. This paper presents a new watermarking protocol able to combine the “buyer friendly” and “mediated” design approach with the blockchain technology. The result is a secure protocol that can support a limited and balanced participation of both buyers and content providers in the purchase transactions of protected digital content. Moreover, the protocol can avoid the direct involvement of trusted third parties in the purchase transactions. This can reduce the actual risk that buyers or sellers can violate the protocol by illicitly interacting with trusted third parties. In fact, such peculiarities make the proposed protocol suited for the current web context.
An Improved Method of Detecting Macro Malware on an Imbalanced Dataset

In spear-phishing attacks, macro malware written in VBA (Visual Basic for Applications) is often used to compromise the target computers. Macro malware is often obfuscated in several ways to evade detection. To detect new macro malware, several methods with machine learning techniques have been proposed. While many methods were evaluated with the inadequate or balanced dataset with the same number of benign and malicious samples, practical performance is still open to discussion. In reality, the population of VBA macros consists of wide variety of samples. To evaluate practical performance, an imbalanced dataset which contains many benign samples is required. In this paper, we propose an improved method of detecting macro malware on an imbalanced dataset. Our method uses 2 language models (Doc2vec and Latent Semantic Indexing (LSI)) and 4 popular classifiers. These language models are used to extract features and mitigate the class imbalance problem by selecting important features. We create an imbalanced dataset with more than 30,000 samples and evaluate the practical performance. The experimental result demonstrates that our method mitigates the class imbalance problem and could detect completely new malware regardless of the family type. The result also reveals that LSI is more robust than Doc2vec to the class imbalance problem.
Multi-Unit Transformers for Neural Machine Translation

Transformer models (Vaswani et al., 2017) achieve remarkable success in Neural Machine Translation.Many efforts have been devoted to deepening the Transformer by stacking several units (i.e., a combination of Multihead Attentions and FFN) in a cascade, while the investigation over multiple parallel units draws little attention.In this paper, we propose the Multi-Unit TransformErs (MUTE), which aim to promote the expressiveness of the Transformer by introducing diverse and complementary units.Specifically, we use several parallel units and show that modeling with multiple units improves model performance and introduces diversity.Further, to better leverage the advantage of the multi-unit setting, we design biased module and sequential dependency that guide and encourage complementariness among different units.Experimental results on three machine translation tasks, the NIST Chinese-to-English, WMT'14 English-to-German and WMT'18 Chinese-to-English, show that the MUTE models significantly outperform the Transformer-Base, by up to +1.52, +1.90 and +1.10 BLEU points, with only a mild drop in inference speed (about 3.1%).In addition, our methods also surpass the Transformer-Big model, with only 54% of its parameters.These results demonstrate the effectiveness of the MUTE, as well as its efficiency in both the inference process and parameter usage. 1
Long-Short Term Masking Transformer: A Simple but Effective Baseline for Document-level Neural Machine Translation

Many document-level neural machine translation (NMT) systems have explored the utility of context-aware architecture, usually requiring an increasing number of parameters and computational complexity.However, few attention is paid to the baseline model.In this paper, we research extensively the pros and cons of the standard transformer in document-level translation, and find that the auto-regressive property can simultaneously bring both the advantage of the consistency and the disadvantage of error accumulation.Therefore, we propose a surprisingly simple long-short term masking self-attention on top of the standard transformer to both effectively capture the long-range dependence and reduce the propagation of errors.We examine our approach on the two publicly available document-level datasets.We can achieve a strong result in BLEU and capture discourse phenomena.
Feature Adaptation of Pre-Trained Language Models across Languages and Domains with Robust Self-Training

Adapting pre-trained language models (PrLMs) (e.g., BERT) to new domains has gained much attention recently.Instead of fine-tuning PrLMs as done in most previous work, we investigate how to adapt the features of PrLMs to new domains without fine-tuning.We explore unsupervised domain adaptation (UDA) in this paper.With the features from PrLMs, we adapt the models trained with labeled data from the source domain to the unlabeled target domain.Self-training is widely used for UDA, and it predicts pseudo labels on the target domain data for training.However, the predicted pseudo labels inevitably include noise, which will negatively affect training a robust model.To improve the robustness of self-training, in this paper we present class-aware feature self-distillation (CFd) to learn discriminative features from PrLMs, in which PrLM features are self-distilled into a feature adaptation module and the features from the same class are more tightly clustered.We further extend CFd to a cross-language setting, in which language discrepancy is studied.Experiments on two monolingual and multilingual Amazon review datasets show that CFd can consistently improve the performance of self-training in cross-domain and cross-language settings.
Unsupervised Parsing via Constituency Tests

We propose a method for unsupervised parsing based on the linguistic notion of a constituency test.One type of constituency test involves modifying the sentence via some transformation (e.g.replacing the span with a pronoun) and then judging the result (e.g.checking if it is grammatical).Motivated by this idea, we design an unsupervised parser by specifying a set of transformations and using an unsupervised neural acceptability model to make grammaticality decisions.To produce a tree given a sentence, we score each span by aggregating its constituency test judgments, and we choose the binary tree with the highest total score.While this approach already achieves performance in the range of current methods, we further improve accuracy by fine-tuning the grammaticality model through a refinement procedure, where we alternate between improving the estimated trees and improving the grammaticality model.The refined model achieves 62.8 F1 on the Penn Treebank test set, an absolute improvement of 7.6 points over the previous best published result.
Not Low-Resource Anymore: Aligner Ensembling, Batch Filtering, and New Datasets for Bengali-English Machine Translation

Despite being the seventh most widely spoken language in the world, Bengali has received much less attention in machine translation literature due to being low in resources.Most publicly available parallel corpora for Bengali are not large enough; and have rather poor quality, mostly because of incorrect sentence alignments resulting from erroneous sentence segmentation, and also because of a high volume of noise present in them.In this work, we build a customized sentence segmenter for Bengali and propose two novel methods for parallel corpus creation on low-resource setups: aligner ensembling and batch filtering.With the segmenter and the two methods combined, we compile a high-quality Bengali-English parallel corpus comprising of 2.75 million sentence pairs, more than 2 million of which were not available before.Training on neural models, we achieve an improvement of more than 9 BLEU score over previous approaches to Bengali-English machine translation.We also evaluate on a new test set of 1000 pairs made with extensive quality control.We release the segmenter, parallel corpus, and the evaluation set, thus elevating Bengali from its low-resource status.To the best of our knowledge, this is the first ever large scale study on Bengali-English machine translation.We believe our study will pave the way for future research on Bengali-English machine translation as well as other low-resource languages.Our data and code are available at https: //github.com/csebuetnlp/banglanmt.
PAC-Bayes Analysis Beyond the Usual Bounds

We focus on a stochastic learning model where the learner observes a finite set of training examples and the output of the learning process is a data-dependent distribution over a space of hypotheses. The learned data-dependent distribution is then used to make randomized predictions, and the high-level theme addressed here is guaranteeing the quality of predictions on examples that were not seen during training, i.e. generalization. In this setting the unknown quantity of interest is the expected risk of the data-dependent randomized predictor, for which upper bounds can be derived via a PAC-Bayes analysis, leading to PAC-Bayes bounds. Specifically, we present a basic PAC-Bayes inequality for stochastic kernels, from which one may derive extensions of various known PAC-Bayes bounds as well as novel bounds. We clarify the role of the requirements of fixed 'data-free' priors, bounded losses, and i.i.d. data. We highlight that those requirements were used to upper-bound an exponential moment term, while the basic PAC-Bayes theorem remains valid without those restrictions. We present three bounds that illustrate the use of data-dependent priors, including one for the unbounded square loss.
KeyGames: A Game Theoretic Approach to Automatic Keyphrase Extraction

In this paper, we introduce two advancements in the automatic keyphrase extraction (AKE) space -KeyGames and pke+.KeyGames is an unsupervised AKE framework that employs the concept of evolutionary game theory and consistent labelling problem to ensure consistent classification of candidates into keyphrase and non-keyphrase.Pke+ is a python based pipeline built on top of the existing pke library to standardize various AKE steps, namely candidate extraction and evaluation, to ensure truly systematic and comparable performance analysis of AKE models.In the experiments section, we compare the performance of KeyGames across three publicly available datasets (Inspec 2001, SemEval 2010, DUC 2001) against the results quoted by the existing state-of-the-art models as well as their performance when reproduced using pke+.The results show that KeyGames outperforms most of the state-of-the-art systems while generalizing better on input documents with different domains and length.Further, pke+'s pre-processing brings out improvement in several other system's quoted performance as well.
Hy-NLI: a Hybrid system for Natural Language Inference

Despite the advances in Natural Language Inference through the training of massive deep models, recent work has revealed the generalization difficulties of such models, which fail to perform on adversarial datasets with challenging linguistic phenomena.Such phenomena, however, can be handled well by symbolic systems.Thus, we propose Hy-NLI, a hybrid system that learns to identify an NLI pair as linguistically challenging or not.Based on that, it uses its symbolic or deep learning component, respectively, to make the final inference decision.We show how linguistically less complex cases are best solved by robust state-of-the-art models, like BERT and XLNet, while hard linguistic phenomena are best handled by our implemented symbolic engine.Our thorough evaluation shows that our hybrid system achieves state-of-the-art performance across mainstream and adversarial datasets and opens the way for further research into the hybrid direction.
ReNets: Statically-Optimal Demand-Aware Networks

Previous chapter Next chapter Full AccessProceedings Symposium on Algorithmic Principles of Computer Systems (APOCS)ReNets: Statically-Optimal Demand-Aware NetworksChen Avin and Stefan SchmidChen Avin and Stefan Schmidpp.25 - 39Chapter DOI:https://doi.org/10.1137/1.9781611976489.3PDFBibTexSections ToolsAdd to favoritesExport CitationTrack CitationsEmail SectionsAboutAbstract This paper studies the design of self-adjusting datacenter networks whose physical topology dynamically adapts to the workload, in an online and demand-aware manner. We propose ReNet, a self-adjusting network which does not require any predictions about future demands and amortizes reconfigurations: it performs as good as a hypothetical static algorithm with perfect knowledge of the future demand. In particular, we show that for arbitrary sparse communication demands, ReNets achieve static optimality, a fundamental property of learning algorithms, and that route lengths in ReNets are proportional to existing lower bounds, which are known to relate to an entropy metric of the demand. ReNets provide additional desirable properties such as compact and local routing and flat addressing therefore ensuring scalability and further reducing the overhead of reconfiguration. To achieve these properties, ReNets combine multiple self-adjusting tree topologies which are optimized toward individual sources, called ego-trees in this paper. Previous chapter Next chapter RelatedDetails Published:2021eISBN:978-1-61197-648-9 https://doi.org/10.1137/1.9781611976489Book Series Name:ProceedingsBook Code:APOSC21Book Pages:ii + 158