Reviewerly/exHarmony huggingface dataset:
Adversarial Stylometry in the Wild: Transferable Lexical Substitution Attacks on Author Profiling

Written language contains stylistic cues that can be exploited to automatically infer a variety of potentially sensitive author information.Adversarial stylometry intends to attack such models by rewriting an author's text.Our research proposes several components to facilitate deployment of these adversarial attacks in the wild, where neither data nor target models are accessible.We introduce a transformerbased extension of a lexical replacement attack, and show it achieves high transferability when trained on a weakly labeled corpusdecreasing target model performance below chance.While not completely inconspicuous, our more successful attacks also prove notably less detectable by humans.Our framework therefore provides a promising direction for future privacy-preserving adversarial attacks.
Identification of Vessels on Inland Waters Using Low-Quality Video Streams

Video surveillance can be used to monitor recreational and commercial vessels on inland waters.It is difficult to identify vessels on the basis of registration numbers alone, especially when structure and size are not standardized in a way that allows for easy recognition.In this paper, we propose a novel vessel identification method that can work continuously on streams with a high compression rate or visible compression artifacts.This method can identify all types of moving vessels during daylight.It should work with existing video monitoring systems used to monitor inland waters.When a vessel has hull inscriptions, the method can identify that vessel.If hull inscriptions are not present, the method identifies the direction of movement of the vessel.The tests were performed by real-time video playback of prerecorded samples in the test environment.The method correctly identifies 94% of vessels and the performance indicates that it is suitable for practical application.
Adaptive and Generative Zero-Shot Learning

We address the problem of generalized zero-shot learning (GZSL) where the task is to predict the class label of a target image whether its label belongs to the seen or unseen category. Similar to ZSL, the learning setting assumes that all class-level semantic features are given, while only the images of seen classes are available for training. By exploring the correlation between image features and the corresponding semantic features, the main idea of the proposed approach is to enrich the semantic-to-visual (S2V) embeddings via a seamless fusion of adaptive and generative learning. To this end, we extend the semantic features of each class by supplementing image-adaptive attention so that the learned S2V embedding can account for not only inter-class but also intra-class variations. In addition, to break the limit of training with images only from seen classes, we design a generative scheme to simultaneously generate virtual class labels and their visual features by sampling and interpolating over seen counterparts. In inference, a testing image will give rise to two different S2V embeddings, seen and virtual. The former is used to decide whether the underlying label is of the unseen category or otherwise a specific seen class; the latter is to predict an unseen class label. To demonstrate the effectiveness of our method, we report state-of-the-art results on four standard GZSL datasets, including an ablation study of the proposed modules.
Fast Approximation of the Gauss--Newton Hessian Matrix for the Multilayer Perceptron

We introduce a fast algorithm for entrywise evaluation of the Gauss--Newton Hessian (GNH) matrix for the fully connected feed-forward neural network. The algorithm has a precomputation step and a sampling step. While it generally requires $\mathcal{O}(Nn)$ work to compute an entry (and the entire column) in the GNH matrix for a neural network with $N$ parameters and $n$ data points, our fast sampling algorithm reduces the cost to $\mathcal{O}(n+d/\epsilon^2)$ work, where $d$ is the output dimension of the network and $\epsilon$ is a prescribed accuracy (independent of $N$). One application of our algorithm is constructing the hierarchical-matrix ($\mathcal{H}$-matrix) approximation of the GNH matrix for solving linear systems and eigenvalue problems. It generally requires $\mathcal{O}(N^2)$ memory and $\mathcal{O}(N^3)$ work to store and factorize the GNH matrix, respectively. The $\mathcal{H}$-matrix approximation requires only $\mathcal{O}(N r_o)$ memory footprint and $\mathcal{O}(N r_o^2)$ work to be factorized, where $r_o \ll N$ is the maximum rank of off-diagonal blocks in the GNH matrix. We demonstrate the performance of our fast algorithm and the $\mathcal{H}$-matrix approximation on classification and autoencoder neural networks. (A corrected version is attached.)
Is Making Mistakes Human? On the Perception of Typing Errors in Chatbot Communication

The increasing application of Conversational Agents (CAs) changes the way customers and businesses interact during a service encounter.Research has shown that CA equipped with social cues (e.g., having a name, greeting users) stimulates the user to perceive the interaction as human-like, which can positively influence the overall experience.Specifically, social cues have shown to lead to increased customer satisfaction, perceived service quality, and trustworthiness in service encounters.However, many CAs are discontinued because of their limited conversational ability, which can lead to customer dissatisfaction.Nevertheless, making errors and mistakes can also be seen as a human characteristic (e.g., typing errors).Existing research on humancomputer interfaces lacks in the area of CAs producing human-like errors and their perception in a service encounter situation.Therefore, we conducted a 2x2 online experiment with 228 participants on how CAs typing errors and CAs human-like behavior treatments influence user's perception, including perceived service quality.
3MNet: Multi-task, multi-level and multi-channel feature aggregation network for salient object detection

Salient object detection is a hot spot of current computer vision. The emergence of the convolutional neural network (CNN) greatly improves the existing detection methods. In this paper, we present 3MNet, which is based on the CNN, to make the utmost of various features of the image and utilize the contour detection task of the salient object to explicitly model the features of multi-level structures, multiple tasks and multiple channels, so as to obtain the final saliency map of the fusion of these features. Specifically, we first utilize contour detection task for auxiliary detection and then utilize use multi-layer network structure to extract multi-scale image information. Finally, we introduce a unique module into the network to model the channel information of the image. Our network has produced good results on five widely used datasets. In addition, we also conducted a series of ablation experiments to verify the effectiveness of some components in the network.
Self-supervised Dimensionality Reduction with Neural Networks and Pseudo-labeling

Dimensionality reduction (DR) is used to explore high-dimensional data in many applications.Deep learning techniques such as autoencoders have been used to provide fast, simple to use, and high-quality DR.However, such methods yield worse visual cluster separation than popular methods such as t-SNE and UMAP.We propose a deep learning DR method called Self-Supervised Network Projection (SSNP) which does DR based on pseudo-labels obtained from clustering.We show that SSNP produces better cluster separation than autoencoders, has out-of-sample, inverse mapping, and clustering capabilities, and is very fast and easy to use.
Multicriteria Optimization of Virtual Machine Placement in Cloud Data Centers

The problem of virtual machine placement on physical servers in cloud data centers is considered. The resource management system has a two-level architecture consisting of global and local controllers. Local controllers analyze the state of physical servers on which they are located and determine possible underloading, overloading, and overheating states based on the forecast for the next observation window. The global controller gathers the information from local controllers and start the process of destination server selecting and virtual machines migrating. In this paper we propose to place virtual machines based on the criteria of minimum resource wastage and SLA-violation. The mathematical formulation of the optimization problem is given, which is equivalent to the known main assignment problem in terms of structure, necessary conditions, and the nature of variables. Reducing the assignment problem to a closed transport problem allowed us to effectively solve the problem of multicriteria virtual machine placement in real time. We could significantly increase its dimension compared to heuristic algorithms, which makes it possible to maintain the quality of cloud services in conditions of rapid resource demand growth of data centers. The developed mathematical formulation of the problem and the results of computational experiments can be included in the mathematical software of virtual machine live migration.
Ensemble Pruning via Quadratic Margin Maximization

Ensemble models refer to methods that combine a typically large number of weak learners into a stronger composite model. The output of an ensemble method is the result of fitting a base-learning algorithm to a given data set, and obtaining diverse answers by re-weighting the observations or by re-sampling them using a given probabilistic selection. A key challenge of using ensembles in large-scale multidimensional data lies in the complexity and the computational burden associated with them. The models created by ensembles are often difficult, if not impossible, to interpret and their implementation requires more computational power than individual learning algorithms. Recent research effort in the field has concentrated on reducing ensemble size, while maintaining predictive accuracy. We propose a method to prune an ensemble solution by optimizing its margin distribution, while increasing its diversity. The proposed algorithm results in an ensemble that uses only a fraction of the original weak learners, with generally improved estimated generalization performance. We analyze and test our method on both synthetic and real data sets. The analysis shows that the proposed method compares favorably to the original ensemble solutions and to other existing ensemble pruning methodologies.
Data Mining preparation: Process, Techniques and Major Issues in Data Analysis

Abstract Data preparation is an essential stage in data analysis. Many institutions or companies are interested in converting data into pure forms that can be used for scientific and profit purposes. It helps you set goals regarding system capabilities and features or the benefits your company expects from its investment. This purpose creates an immediate need to review and prepare the data to clean the raw data. In this paper, we highlight the importance of data preparation in data analysis and data extraction techniques, in addition to an integrated overview of relevant recent studies dealing with mining methodology, data types diversity, user interaction, and data mining. Finally, we suggest some potential suggestions for future research and development.
Detecting adversarial examples through nonlinear dimensionality reduction

Deep neural networks are vulnerable to adversarial examples, i.e., carefully-perturbed inputs aimed to mislead classification. This work proposes a detection method based on combining non-linear dimensionality reduction and density estimation techniques. Our empirical findings show that the proposed approach is able to effectively detect adversarial examples crafted by non-adaptive attackers, i.e., not specifically tuned to bypass the detection method. Given our promising results, we plan to extend our analysis to adaptive attackers in future work.
Does <scp><i>Alice</i></scp> Target Patent Trolls?

The five years since Alice Corp. v. CLS Bank International, 573 U.S. 208 (2014) have witnessed an explosion in patentable subject matter decisions ‐‐ and in political controversy over the doctrine. To its critics, Alice renders important inventions in medical diagnostics, biotechnology, and information technology unpatenable and therefore discouraging investment in critical market sector innovation. To its defenders, Alice offers a reliable, quick, and cheap way to invalidate weak patents owned by patent trolls, reducing patent holdup and protecting innovators. In response to these debates, we explore how the courts actually use Alice. We construct a unique dataset of every district court and federal circuit decision on patentable subject matter from July 2014 through June 2019 and categorize the patent plaintiffs in each case using the Stanford NPE Litigation Database. Once in court, patentable subject matter challenges are far more common in software/IT cases, and biotech/life science innovations are more likely to survive. Surprisingly, the entities most likely to lose their patents are not patent trolls but individual inventors and inventor‐started companies. Our findings have important implications for current legislative and judicial disputes over patent reform as some of the patent owners most impacted may be overlooked.
Adoption of Mobile Learning: The Influence And Opportunities For Learning During The Covid-19 Pandemic

Research It is designed using quantitative and qualitative approaches. The sampling technique used in this study was purposive sampling. The samples in this study were lecturers and students. Data collection techniques using questionnaires and interviews. Interviews were conducted by interviewing lecturers at several different campuses. The results of the interview show that mobile learning is the learning that students are most interested in because it can be accessed through their cellphones. Quantitative data analysis used the product-moment correlation test. The research subjects consisted of 110 students. The significance level of 0.001 (p &amp;lt;0.05) indicates that there is a significant influence between the adoption of mobile learning on student interest in learning during the Covid pandemic 19.
Evolving Fully Automated Machine Learning via Life-Long Knowledge Anchors

Automated machine learning (AutoML) has achieved remarkable progress on various tasks, which is attributed to its minimal involvement of manual feature and model designs. However, most of existing AutoML pipelines only touch parts of the full machine learning pipeline, e.g., neural architecture search or optimizer selection. This leaves potentially important components such as data cleaning and model ensemble out of the optimization, and still results in considerable human involvement and suboptimal performance. The main challenges lie in the huge search space assembling all possibilities over all components, as well as the generalization ability over different tasks like image, text, and tabular etc. In this paper, we present a first-of-its-kind fully AutoML pipeline, to comprehensively automate data preprocessing, feature engineering, model generation/selection/training and ensemble for an arbitrary dataset and evaluation metric. Our innovation lies in the comprehensive scope of a learning pipeline, with a novel “life-long” knowledge anchor design to fundamentally accelerate the search over the full search space. Such knowledge anchors record detailed information of pipelines and integrates them with an evolutionary algorithm for joint optimization across components. Experiments demonstrate that the result pipeline achieves state-of-the-art performance on multiple datasets and modalities. Specifically, the proposed framework was extensively evaluated in the NeurIPS 2019 AutoDL challenge, and won the only champion with a significant gap against other approaches, on all the image, video, speech, text and tabular tracks.
Virtual Sample Generation and Ensemble Learning Based Image Source Identification With Small Training Samples

Nowadays, source camera identification, which aims to identify the source camera of images, is quite important in the field of forensics. There is a problem that cannot be ignored that the existing methods are unreliable and even out of work in the case of the small training sample. To solve this problem, a virtual sample generation-based method is proposed in this paper, combined with the ensemble learning. In this paper, after constructing sub-sets of LBP features, the authors generate a virtual sample-based on the mega-trend-diffusion (MTD) method, which calculates the diffusion range of samples according to the trend diffusion theory, and then randomly generates virtual sample according to uniform distribution within this range. In the aspect of the classifier, an ensemble learning scheme is proposed to train multiple SVM-based classifiers to improve the accuracy of image source identification. The experimental results demonstrate that the proposed method achieves higher average accuracy than the state-of-the-art, which uses a small number of samples as the training sample set.
Fighting Hate with Speech Law: Media and German Visions of Democracy

In Germany, far-right groups have revived Nazi terminology like Lügenpresse (lying press) or Systempresse (system press) to decry the media today. German politicians, journalists, and the public have turned to numerous methods to try to combat the reinsertion of Nazi language into everyday German life. One key method is the law. Prior to the 2017 German election, the German parliament swiftly passed the Netzwerkdurchsetzungsgesetz (Network Enforcement Law, NetzDG for short). While English-language press has often called this act a hate speech law, it actually enforces 22 statutes of extant German speech law online. Spearheaded by the SPD-led Justice Ministry, NetzDG represented the most public effort by the German government to push back against the AfD, the far right, and the rise of hate speech in Germany. NetzDG attracted huge global attention as the first major law to fine American-based social media companies for not adhering to national statutes. This article examines why German politicians turned to law as a way to combat the rise of the far right. I explore how NetzDG represented German political understandings of the relationship between freedom of expression and democracy and argue that NetzDG followed a longer historical pattern of German attempts to use media law to raise Germany's profile on the international stage. The article examines the irony that NetzDG was meant to defend democracy in Germany, but may have unintentionally undermined it elsewhere, as authoritarian regimes like Russia seized upon the law to justify their own curtailments of free expression. Finally, I explain the difficulties of measuring whether NetzDG has achieved its goals and showcase a few other approaches to the problems of information in democracies.
Approximate Data Deletion from Machine Learning Models

Deleting data from a trained machine learning (ML) model is a critical task in many applications. For example, we may want to remove the influence of training points that might be out of date or outliers. Regulations such as EU's General Data Protection Regulation also stipulate that individuals can request to have their data deleted. The naive approach to data deletion is to retrain the ML model on the remaining data, but this is too time consuming. In this work, we propose a new approximate deletion method for linear and logistic models whose computational cost is linear in the the feature dimension $d$ and independent of the number of training data $n$. This is a significant gain over all existing methods, which all have superlinear time dependence on the dimension. We also develop a new feature-injection test to evaluate the thoroughness of data deletion from ML models.
A Novel Method for Despeckling of Ultrasound Images Using Cellular Automata-Based Despeckling Filter

Ultrasound images have an inherent property termed as speckle noise that is the outcome of interference between incident and reflected ultrasound waves which reduce image resolution and contrast and could lead to improper diagnosis of any disease. In different approaches for reducing the speckle noise, there exists a class of filters that convert multiplicative noise into additive noise by using algorithmic functions. The current study proposes a cellular automata-based despeckling filter (CABDF) that implements a local spatial filtering framework for the restoration of the noisy image. In the proposed CABDF filter, a dual transition function has been designed which emphasizes the calculation of nearby weighted separation whose loads originate from the CABDF filtered image, including spatial separation, extend inconsistency, and statistical dispersion. The proposed filter found efficient both in terms of filtering and restoration of the original structure of the ultrasound images.
Assessing the Robustness of Cluster Solutions in Emotionally-Annotated Pictures Using Monte-Carlo Simulation Stabilized K-Means Algorithm

Clustering is a very popular machine-learning technique that is often used in data exploration of continuous variables. In general, there are two problems commonly encountered in clustering: (1) the selection of the optimal number of clusters, and (2) the undecidability of the affiliation of border data points to neighboring clusters. We address both problems and describe how to solve them in application to affective multimedia databases. In the experiment, we used the unsupervised learning algorithm k-means and the Nencki Affective Picture System (NAPS) dataset, which contains 1356 semantically and emotionally annotated pictures. The optimal number of centroids was estimated, using the empirical elbow and silhouette rules, and validated using the Monte-Carlo simulation approach. Clustering with k = 1–50 centroids is reported, along with dominant picture keywords and descriptive statistical parameters. Affective multimedia databases, such as the NAPS, have been specifically designed for emotion and attention experiments. By estimating the optimal cluster solutions, it was possible to gain deeper insight into affective features of visual stimuli. Finally, a custom software application was developed for study in the Python programming language. The tool uses the scikit-learn library for the implementation of machine-learning algorithms, data exploration and visualization. The tool is freely available for scientific and non-commercial purposes.
The Modern Greek Language on the Social Web: A Survey of Data Sets and Mining Applications

Mining social web text has been at the heart of the Natural Language Processing and Data Mining research community in the last 15 years. Though most of the reported work is on widely spoken languages, such as English, the significance of approaches that deal with less commonly spoken languages, such as Greek, is evident for reasons of preserving and documenting minority languages, cultural and ethnic diversity, and identifying intercultural similarities and differences. The present work aims at identifying, documenting and comparing social text data sets, as well as mining techniques and applications on social web text that target Modern Greek, focusing on the arising challenges and the potential for future research in the specific less widely spoken language.