Reviewerly/exHarmony huggingface dataset:
Multi-objective Pruning for CNNs Using Genetic Algorithm

In this work, we propose a heuristic genetic algorithm (GA) for pruning convolutional neural networks (CNNs) according to the multi-objective trade-off among error, computation and sparsity. In our experiments, we apply our approach to prune pre-trained LeNet across the MNIST dataset, which reduces 95.42% parameter size and achieves 16 times speedups of convolutional layer computation with tiny accuracy loss by laying emphasis on sparsity and computation, respectively. Our empirical study suggests that GA is an alternative pruning approach for obtaining a competitive compression performance. Additionally, compared with state-of-the-art approaches, GA can automatically pruning CNNs based on the multi-objective importance by a pre-defined fitness function.
Sentiment Analysis on Movie Reviews using Information Gain and K-Nearest Neighbor

The huge resources need effectiveness and efficiency, it can be processed by machine learning. There have been many studies conducted using machine learning method and produced quite good performance in sentiment analysis. Some machine learning methods that are often used in general are Naive bayes (NB), K-nearest neighbor (KNN), Support vector machine (SVM), and Random forest methods. Mostly, KNN did not achieve better performance than other machine learning methods in sentiment analysis. In this study, the Polarity v2.0 from Cornell movie review dataset will be used to test KNN with Information gain features selection in order to achieve good performance. The purpose of this research are to find the optimum K for KNN and compare KNN with other methods. KNN with the help of Information gain feature selection becomes the best performance method with 96.8% accuracy compared to the NB, SVM, and Random forest while the optimum K is 3.
Discourse Relation Prediction: Revisiting Word Pairs with Convolutional Networks

Word pairs across argument spans have been shown to be effective for predicting the discourse relation between them.We propose an approach to distill knowledge from word pairs for discourse relation classification with convolutional neural networks by incorporating joint learning of implicit and explicit relations.Our novel approach of representing the input as word pairs achieves state-of-theart results on four-way classification of both implicit and explicit relations as well as one of the binary classification tasks.For explicit relation prediction, we achieve around 20% error reduction on the four-way task.At the same time, compared to a two-layered Bi-LSTM-CRF model, our model is able to achieve these results with half the number of learnable parameters and approximately half the amount of training time.
Spatial Transformer Generative Adversarial Network for Robust Image Super-Resolution

Recently, there have been significant advances in image super-resolution based on generative adversarial networks (GANs) to achieve breakthroughs in generating more images with high subjective quality. However, there are remaining challenges needs to be met, such as simultaneously recovering the finer texture details for large upscaling factors and mitigating the geometric transformation effects. In this paper, we propose a novel robust super-resolution GAN (i.e. namely RSR-GAN) which can simultaneously perform both the geometric transformation and recovering the finer texture details. Specifically, since the performance of the generator depends on the discreminator, we propose a novel discriminator design by incorporating the spatial transformer module with residual learning to improve the discrimination of fake and true images through removing the geometric noise, in order to enhance the super-resolution of geometric corrected images. Finally, to further improve the perceptual quality, we introduce an additional DCT loss term into the existing loss function. Extensive experiments, measured by both PSNR and SSIM measurements, show that our proposed method achieves a high level of robustness against a number of geometric transformations, including rotation, translation, a combination of rotation and scaling effects, and a cobmination of rotaion, transalation and scaling effects. Benchmarked by the existing state-of-the-arts SR methods, our proposed delivers superior performances on a wide range of datasets which are publicly available and widely adopted across research communities.
Special issue on bibliographic data sources

Research in quantitative science studies relies on an increasingly broad range of data sources, providing data on scholarly publications, social media activity, peer review, research funding, the scholarly workforce, scientific prizes, and so on.However, there is one type of data source that remains at the heart of research in quantitative science studies: bibliographic databases.These data sources have increasingly diversified over the last decade.Several organizations now provide large-scale databases of metadata on scholarly publications.For this special issue of Quantitative Science Studies, we invited the providers of major bibliographic data sources to provide insights on how their data can be used to support research in quantitative science studies.This special issue comprises six papers.Three papers cover the most important commercial bibliographic data sources: Web of Science (Clarivate Analytics), Scopus (Elsevier), and Dimensions (Digital Science).Three other papers cover open data sources: Microsoft Academic, Crossref and OpenCitations. 1 There are of course many other bibliographic data sources.However, for this special issue, we have chosen to consider only data sources that cover publications from all fields of science and from all parts of the world.Data sources that focus on specific scientific fields (e.g., PubMed from the US National Library of Medicine) or specific countries (e.g., national databases of scholarly publications) are therefore not included.We hope that this special issue will help authors of submissions to Quantitative Science Studies to choose the most suitable bibliographic data source for their research.In the past, Web of Science and Scopus often were the only data sources between which researchers could choose.Researchers typically used the data source to which their institution happened to have a subscription.In recent years, however, the number of options has increased considerably.This special issue aims to characterize the most important data sources currently available and to show how they differ in various dimensions, for instance in the data they provide, their level of openness, and their support for making research reproducible.As editors of Quantitative Science Studies, we consider openness and reproducibility to be of major importance.Research published in Quantitative Science Studies is expected to be as reproducible as possible, and reproducibility can be promoted by making use of open data.Below we provide a brief overview of some of the key differences between the bibliographic data sources considered in this special issue, focusing on three questions: What does the data source provide?How to get access to the data?And how can the data be used?1 The team of Google Scholar was also invited to contribute to this special issue, but they did not respond to our invitation.
DNNGuard: An Elastic Heterogeneous DNN Accelerator Architecture against Adversarial Attacks

Recent studies show that Deep Neural Networks (DNN) are vulnerable to adversarial samples that are generated by perturbing correctly classified inputs to cause the misclassification of DNN models. This can potentially lead to disastrous consequences, especially in security-sensitive applications such as unmanned vehicles, finance and healthcare. Existing adversarial defense methods require a variety of computing units to effectively detect the adversarial samples. However, deploying adversary sample defense methods in existing DNN accelerators leads to many key issues in terms of cost, computational efficiency and information security. Moreover, existing DNN accelerators cannot provide effective support for special computation required in the defense methods.
Deep Learning for Robust Automatic Modulation Recognition Method for IoT Applications

In the scenarios of non-cooperative wireless communications, automatic modulation recognition (AMR) is an indispensable algorithm to recognize various types of signal modulations before demodulation in many internet of things applications. Convolutional neural network (CNN)-based AMR is considered as one of the most promising methods to achieve good recognition performance. However, conventional CNN-based methods are often unstable and also lack of generalized capabilities under varying noise conditions, because these methods are merely trained on specific dataset and can only work at the corresponding noise condition. Hence, it is hard to apply these methods directly in practical systems. In this paper, we propose a CNN-based robust automatic modulation recognition (RAMR) method to recognize three types of modulation signals, i.e., frequency shift key (FSK), phase shift key (PSK), and quadrature amplitude modulation (QAM). The proposed method is trained on a mixed dataset for extracting common features under varying noise scenarios. Simulation results show that our proposed generalized CNN-based architecture can achieve higher robustness and convenience than conventional ones.
Morphological, Texture, and Color Feature Analysis for Erythrocyte Classification in Thalassemia Cases

Abnormal erythrocytes have diverse shapes. The appearance of specific erythrocyte shapes in a person's blood can indicate certain diseases, including thalassemia. We used thalassemia peripheral blood smear images and applied a segmentation process to produce single erythrocyte sub-images. Each erythrocyte has a unique shape. The selection of appropriate features to represent erythrocytes is critical for classification accuracy. We used morphological features such as moment invariants, geometry parameters of the cell and central pallor, and distance angle signature (DAS) morphological features of the cell and central pallor. We combined morphological features with texture and color features to increase the accuracy of erythrocyte classification. In this study, the multi-layer perceptron is used to classify nine shapes of erythrocytes present in thalassemia cases. The experimental results of 7108 erythrocytes indicated an accuracy of 98.11% based on the combination of features. The experimental results also show that the combination of features we proposed produced higher classification accuracy than previous work, which yielded an accuracy of 93.77%.
Iterative Robust Semi-Supervised Missing Data Imputation

In many real-world applications scientists are often confronted with the problem of incomplete datasets due to several reasons. The direct analysis of datasets with missing values in attributes inevitably results in inaccurate learning models and erroneous results. Facing effectively the challenge of missing values is an essential step of the data mining process. Imputation is often employed to overcome the shortcomings incurred by missing data during the pre-process stage of data analysis. Therefore, a plethora of statistical and machine learning methods have been proposed and employed with a view to imputing the missing values in incomplete data with their potential or actual values. In this context, the main objective of this paper is to put forward an iterative stepwise imputation method based on the semi-supervised learning approach, called IRSSI. Semi-supervised methods have proved to be particularly effective for exploiting incomplete or partially labeled data with regard to the values of the target attribute. The proposed algorithm was experimentally evaluated on real-world benchmark datasets and artificially generated datasets using different high ratios of missing data. The experimental results demonstrate the efficiency of IRSSI algorithm compared to typical imputation methods.
Efficient adaptive inference for deep convolutional neural networks using hierarchical early exits

Early exits are capable of providing deep learning models with adaptive computational graphs that can readily adapt on-the-fly to the available resources. Despite their advantages, existing early exit methods suffer from many limitations which limit their performance, e.g., they ignore the information extracted from previous exit layers, they are unable to efficiently handle feature maps with large sizes, etc. To overcome these limitations we propose a Bag-of-Features (BoF)-based method that is capable of constructing efficient hierarchical early exit layers with minimal computational overhead, while also providing an adaptive inference method that allows for early stopping the inference process when the network is confident enough for its output, leading to significant performance benefits. To this end, the BoF model is extended and adapted to the needs of early exits by constructing additive shared histogram spaces that gradually refine the information extracted from the various layers of a network, in a hierarchical manner, while also employing a classification layer reuse strategy to further reduce the number of parameters needed per exit layer. Note that the proposed method is generic and can be readily combined with any neural network architecture. The effectiveness of the proposed method is demonstrated using five different image datasets, proving that early exits can be readily transformed into a practical tool, which can be effectively used in various real-world embedded applications.
Semantic Guided and Response Times Bounded Top-k Similarity Search over Knowledge Graphs

Recently, graph query is widely adopted for querying knowledge graphs. Given a query graph G <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Q</sub> , the graph query finds subgraphs in a knowledge graph G that exactly or approximately match G <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Q</sub> . We face two challenges on graph query: (1) the structural gap between G <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Q</sub> and the predefined schema in G causes mismatch with query graph, (2) users cannot view the answers until the graph query terminates, leading to a longer system response time (SRT). In this paper, we propose a semantic-guided and response-time-bounded graph query to return the top-k answers effectively and efficiently. We leverage a knowledge graph embedding model to build the semantic graph SG <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Q</sub> , and we define the path semantic similarity (pss) over SG <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Q</sub> as the metric to evaluate the answer's quality. Then, we propose an A* semantic search on SG <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Q</sub> to find the top-k answers with the greatest pss via a heuristic pss estimation. Furthermore, we make an approximate optimization on A* semantic search to allow users to trade off the effectiveness for SRT within a user- specific time bound. Extensive experiments over real datasets confirm the effectiveness and efficiency of our solution.
Implicit Regularization of Random Feature Models

Random Feature (RF) models are used as efficient parametric approximations of kernel methods. We investigate, by means of random matrix theory, the connection between Gaussian RF models and Kernel Ridge Regression (KRR). For a Gaussian RF model with $P$ features, $N$ data points, and a ridge $\lambda$, we show that the average (i.e. expected) RF predictor is close to a KRR predictor with an effective ridge $\tilde{\lambda}$. We show that $\tilde{\lambda} > \lambda$ and $\tilde{\lambda} \searrow \lambda$ monotonically as $P$ grows, thus revealing the implicit regularization effect of finite RF sampling. We then compare the risk (i.e. test error) of the $\tilde{\lambda}$-KRR predictor with the average risk of the $\lambda$-RF predictor and obtain a precise and explicit bound on their difference. Finally, we empirically find an extremely good agreement between the test errors of the average $\lambda$-RF predictor and $\tilde{\lambda}$-KRR predictor.
Benefits of Intermediate Annotations in Reading Comprehension

Complex, compositional reading comprehension datasets require performing latent sequential decisions that are learned via supervision from the final answer.A large combinatorial space of possible decision paths that result in the same answer, compounded by the lack of intermediate supervision to help choose the right path, makes the learning particularly hard for this task.In this work, we study the benefits of collecting intermediate reasoning supervision along with the answer during data collection.We find that these intermediate annotations can provide two-fold benefits.First, we observe that for any collection budget, spending a fraction of it on intermediate annotations results in improved model performance, for two complex compositional datasets: DROP and Quoref.Second, these annotations encourage the model to learn the correct latent reasoning steps, helping combat some of the biases introduced during the data collection process.
Adaptive Density Peaks Clustering Based on K-Nearest Neighbor and Gini Coefficient

Density Peaks Clustering (DPC) is a density-based clustering algorithm that has the advantage of not requiring clustering parameters and detecting non-spherical clusters. The density peaks algorithm obtains the actual cluster center by inputting the cutoff distance and manually selecting the cluster center. Thus, the clustering center point is not selected on the basis of considering the whole data set. This paper proposes a method called G-KNN-DPC to calculate the cutoff distance based on the Gini coefficient and K-nearest neighbor. G-KNN-DPC first finds the optimal cutoff distance with Gini coefficient, and then the center point with the K-nearest neighbor. The automatic clustering center method can not only avoid the error that a cluster detects two center points but also effectively solve the traditional DPC algorithm defect that cannot handle complex data sets. Compared with DPC, Fuzzy C-Means, K-means, KDPC and DBSCAN, the proposed algorithm creates better clusters on different data sets.
Generative topographic mapping in drug design

This is a review article of Generative Topographic Mapping (GTM) – a non-linear dimensionality reduction technique producing generative 2D maps of high-dimensional vector spaces – and its specific applications in Drug Design (chemical space cartography, compound library design and analysis, virtual screening, pharmacological profiling, de novo drug design, conformational space & docking interaction cartography, etc.) Written by chemoinformaticians for potential users among medicinal chemists and biologists, the article purposely avoids all underlying mathematics. First, the GTM concept is intuitively explained, based on the strong analogies with the rather popular Self-Organizing Maps (SOMs), which are well established library analysis tools. GTM is basically a fuzzy-logics-based generalization of SOMs. The second part of the review, some of published GTM applications in drug design are briefly revisited.
Contesting algorithms: Restoring the public interest in content filtering by artificial intelligence

In recent years, artificial intelligence has been deployed by online platforms to prevent the upload of allegedly illegal content or to remove unwarranted expressions. These systems are trained to spot objectionable content and to remove it, block it, or filter it out before it is even uploaded. Artificial intelligence filters offer a robust approach to content moderation which is shaping the public sphere. This dramatic shift in norm setting and law enforcement is potentially game-changing for democracy. Artificial intelligence filters carry censorial power, which could bypass traditional checks and balances secured by law. Their opaque and dynamic nature creates barriers to oversight, and conceals critical value choices and tradeoffs. Currently, we lack adequate tools to hold them accountable. This paper seeks to address this gap by introducing an adversarial procedure— – Contesting Algorithms. It proposes to deliberately introduce friction into the dominant removal systems governed by artificial intelligence. Algorithmic content moderation often seeks to optimize a single goal, such as removing copyright-infringing materials or blocking hate speech, while other values in the public interest, such as fair use or free speech, are often neglected. Contesting algorithms introduce an adversarial design which reflects conflicting values, and thereby may offer a check on dominant removal systems. Facilitating an adversarial intervention may promote democratic principles by keeping society in the loop. An adversarial public artificial intelligence system could enhance dynamic transparency, facilitate an alternative public articulation of social values using machine learning systems, and restore societal power to deliberate and determine social tradeoffs.
Building high throughput permissioned blockchain fabrics

Since the introduction of Bitcoin---the first widespread application driven by blockchains---the interest in the design of blockchain-based applications has increased tremendously. At the core of these applications are consensus protocols that securely replicate client requests among all replicas, even if some replicas are Byzantine faulty. Unfortunately, these consensus protocols typically have low throughput, and this lack of performance is often cited as the reason for the slow wider adoption of blockchain technology. Consequently, many works focus on designing more efficient consensus protocols to increase throughput of consensus. We believe that this focus on consensus protocols only explains part of the story. To investigate this belief, we raise a simple question: Can a well-crafted system using a classical consensus protocol outperform systems using modern protocols? In this tutorial, we answer this question by diving deep into the design of blockchain systems. Further, we take an in-depth look at the theory behind consensus, which can help users select the protocol that best-fits their requirements. Finally, we share our vision of high-throughput blockchain systems that operate at large scales.
Privacy in emergency circumstances: data protection and the COVID-19 pandemic

The way we conceive our privacy and the importance which we attach to the protection of our personal data has been heavily impacted by the COVID-19 pandemic. By first providing legal insights on the general discussion about the balance between the fundamental right to privacy and the general public interest, this article describes the most critical and controversial processing operations employed by states to contain the pandemic and mitigate its effects. A focus on the increase in cybercrime during the pandemic then provides insights on the relevant risks and remedies for the security of personal data.