Reviewerly/exHarmony huggingface dataset:
Context, Content, and the Occasional Costs of Implicature Computation

The computation of scalar implicatures is sometimes costly relative to basic meanings. Among the costly computations are those that involve strengthening `some' to `not all' and strengthening inclusive disjunction to exclusive disjunction. The opposite is true for some other cases of strengthening, where the strengthened meaning is less costly than its corresponding basic meaning. These include conjunctive strengthenings of disjunctive sentences (e.g., free-choice inferences) and exactly-readings of numerals. Assuming that these are indeed all instances of strengthening via implicature/exhaustification, the puzzle is to explain why strengthening sometimes increases costs while at other times it decreases costs. I develop a theory of processing costs that makes no reference to the strengthening mechanism or to other aspects of the derivation of the sentence's form/meaning. Instead, costs are determined by domain-general considerations of the grammar's output, and in particular by aspects of the meanings of ambiguous sentences and particular ways they update the context. Specifically, I propose that when the hearer has to disambiguate between a sentence's basic and strengthened meaning, the processing cost of any particular choice is a function of (i) a measure of the semantic complexity of the chosen meaning and (ii) a measure of how much relevant uncertainty it leaves behind in the context. I measure semantic complexity with Boolean Complexity in the propositional case and with semantic automata in the quantificational case, both of which give a domain-general measure of the minimal representational complexity needed to express the given meaning. I measure relevant uncertainty with the information-theoretic notion of entropy; this domain-general measure formalizes how `far' the meaning is from giving a complete answer to the question under discussion, and hence gives an indication of how much representational complexity is yet to come. Processing costs thus follow from domain-general considerations of current and anticipated representational complexity. The results might also speak to functional motivations for having strengthening mechanisms in the first place. Specifically, exhaustification allows language users to use simpler forms than would be available without it to both resolve relevant uncertainties and convey complex meanings.
DABSTERS: A Privacy Preserving e-Voting Protocol for Permissioned Blockchain

With the immutability property and decentralized architecture, Blockchain technology is considered as a revolution for several topics. For electronic voting, it can be used to ensure voter privacy, the integrity of votes, and the verifiability of vote results. More precisely permissioned Blockchains could be the solution for many of the e-voting issues. In this paper, we start by evaluating some of the existing Blockchain-based e-voting systems and analyze their drawbacks. We then propose a fully-decentralized e-voting system based on permissioned Blockchain. Called DABSTERS, our protocol uses a blinded signature consensus algorithm to preserve voters privacy. This ensures several security properties and aims at achieving a balance between voter privacy and election transparency. Furthermore, we formally prove the security of our protocol by using the automated verification tool, ProVerif, with the Applied Pi-Calculus modeling language.
Multi-layer Domain Adaptation for Deep Convolutional Networks

Despite their success in many computer vision tasks, convolutional networks tend to require large amounts of labeled data to achieve generalization. Furthermore, the performance is not guaranteed on a sample from an unseen domain at test time, if the network was not exposed to similar samples from that domain at training time. This hinders the adoption of these techniques in clinical setting where the imaging data is scarce, and where the intra- and inter-domain variance of the data can be substantial. We propose a domain adaptation technique that is especially suitable for deep networks to alleviate this requirement of labeled data. Our method utilizes gradient reversal layers [4] and Squeeze-and-Excite modules [6] to stabilize the training in deep networks. The proposed method was applied to publicly available histopathology and chest X-ray databases and achieved superior performance to existing state-of-the-art networks with and without domain adaptation. Depending on the application, our method can improve multi-class classification accuracy by 5–20% compared to DANN introduced in [4].
Action recognition in freestyle wrestling using silhouette-skeleton features

Despite many advances made in Human Action Recognition (HAR), there are still challenges encouraging researchers to explore new methods. In this study, a new feature descriptor based on the silhouette skeleton called Histogram of Graph Nodes (HGN) is proposed. Unlike similar methods, which are strictly based on the articulated human body model, we extracted discriminative features solely using the foreground silhouettes. To this purpose, first, the skeletons of the silhouettes are converted into a graph, representing approximately articulated human body skeleton. By partitioning the region of the graph, the HGN is calculated in each frame. After that, we obtain the final feature vector by combining the HGNs in time. On the other hand, the recognition of two-person sports techniques is one of the areas that has not received adequate attention. To this end, we investigate the recognition of techniques in wrestling as a new computer vision application. In this regard, a dataset of the Freestyle Wrestling techniques (FSW) is introduced. We conducted extensive experiments using the proposed method on the provided dataset. In addition, we examined the proposed feature descriptor on the SBU and THETIS datasets, and the MHI-based features on the FSW dataset. We achieved 84.9% accuracy on FSW dataset while the results are 90.8% for SBU and 44% for THETIS datasets. The fact that experimental results are superior or comparable to other similar methods indicates the effectiveness of the proposed approach.
Structured Optimal Graph-Based Clustering With Flexible Embedding

In the real world, the duality of high-dimensional data is widespread. The coclustering method has been widely used because they can exploit the co-occurring structure between samples and features. In fact, most of the existing coclustering methods cluster the graphs in the original data matrix. However, these methods fail to output an affinity graph with an explicit cluster structure and still call for the postprocessing step to obtain the final clustering results. In addition, these methods are difficult to find a good projection direction to complete the clustering task on high-dimensional data. In this article, we modify the flexible manifold embedding theory and embed it into the bipartite spectral graph partition. Then, we propose a new method called structured optimal graph-based clustering with flexible embedding (SOGFE). The SOGFE method can learn an affinity graph with an optimal and explicit clustering structure and does not require any postprocessing step. Additionally, the SOGFE method can learn a suitable projection direction to map high-dimensional data to a low-dimensional subspace. We perform extensive experiments on two synthetic data sets and seven benchmark data sets. The experimental results verify the superiority, robustness, and good projection direction selection ability of our proposed method.
Using Deep Learning Based Natural Language Processing Techniques for Clinical Decision-Making with EHRs

Natural language processing (NLP) is an interdisciplinary domain of research that focuses on the interactions between human languages and computers. There has been a recent trend of solving the NLP problems using deep learning approach. The applications of deep learning in the healthcare sector are mostly considered to be related to canonical examples of applying image processing and computer vision techniques to medical scans for disease diagnoses. Electronic Health Record (EHR) is another source of data often being neglected, equally if not more important than medical scans, that can change the way we learn useful features and information from the medical records of patients. These text-based information stored within the EHR are data-rich by nature, but are often not well-understood due to its characteristics of high volume, variety, velocity and complexity. However, these specific characteristics fit right to the nature of deep learning. Therefore, we believe it is the right time to summarize the current status, to review and learn from the state-of-the-art medical-based NLP techniques. Different from the existing reviews, we examine and categorize the current deep learning-based NLP techniques in medical domain into three major purposes: representation learning, information extraction and clinical predictions. Meanwhile, we discuss whether the application of deep learning methods has tackled the problems differently and transformed these tasks revolutionarily. Based on the results, we find that the distance to revolutionize the existing healthcare sector using deep learning methods still remains long. However, the recent progress made by these proposed methods have already made a promising good start. Furthermore, we state some of the legal and ethical considerations, present the status quo of the healthcare industry applications, and provide several possible directions of future research.
Selected technical issues of deep neural networks for image classification purposes

In recent years, deep learning and especially Deep Neural Networks (DNN) have obtained amazing performance on a variety of problems, in particular in classification or pattern recognition. Among many kinds of DNNs, the Convolutional Neural Networks (CNN) are most commonly used. However, due to their complexity, there are many problems related but not limited to optimizing network parameters, avoiding overfitting and ensuring good generalization abilities. Therefore, a number of methods have been proposed by the researchers to deal with these problems. In this paper, we present the results of applying different, recently developed methods to improve deep neural network training and operating. We decided to focus on the most popular CNN structures, namely on VGG based neural networks: VGG16, VGG11 and proposed by us VGG8. The tests were conducted on a real and very important problem of skin cancer detection. A publicly available dataset of skin lesions was used as a benchmark. We analyzed the influence of applying: dropout, batch normalization, model ensembling, and transfer learning. Moreover, the influence of the type of activation function was checked. In order to increase the objectivity of the results, each of the tested models was trained 6 times and their results were averaged. In addition, in order to mitigate the impact of the selection of learning, test and validation sets, k-fold validation was applied.
Counterfactual Reasoning for Fair Clinical Risk Prediction.

The use of machine learning systems to support decision making in healthcare raises questions as to what extent these systems may introduce or exacerbate disparities in care for historically underrepresented and mistreated groups, due to biases implicitly embedded in observational data in electronic health records. To address this problem in the context of clinical risk prediction models, we develop an augmented counterfactual fairness criteria to extend the group fairness criteria of equalized odds to an individual level. We do so by requiring that the same prediction be made for a patient, and a counterfactual patient resulting from changing a sensitive attribute, if the factual and counterfactual outcomes do not differ. We investigate the extent to which the augmented counterfactual fairness criteria may be applied to develop fair models for prolonged inpatient length of stay and mortality with observational electronic health records data. As the fairness criteria is ill-defined without knowledge of the data generating process, we use a variational autoencoder to perform counterfactual inference in the context of an assumed causal graph. While our technique provides a means to trade off maintenance of fairness with reduction in predictive performance in the context of a learned generative model, further work is needed to assess the generality of this approach.
Efficient UC Commitment Extension with Homomorphism for Free (and Applications)

Homomorphic universally composable (UC) commitments allow for the sender to reveal the result of additions and multiplications of values contained in commitments without revealing the values themselves while assuring the receiver of the correctness of such computation on committed values. In this work, we construct essentially optimal additively homomorphic UC commitments from any (not necessarily UC or homomorphic) extractable commitment, while the previous best constructions require oblivious transfer. We obtain amortized linear computational complexity in the length of the input messages and rate 1. Next, we show how to extend our scheme to also obtain multiplicative homomorphism at the cost of asymptotic optimality but retaining low concrete complexity for practical parameters. Moreover, our techniques yield public coin protocols, which are compatible with the Fiat-Shamir heuristic. These results come at the cost of realizing a restricted version of the homomorphic commitment functionality where the sender is allowed to perform any number of commitments and operations on committed messages but is only allowed to perform a single batch opening of a number of commitments. Although this functionality seems restrictive, we show that it can be used as a building block for more efficient instantiations of recent protocols for secure multiparty computation and zero knowledge non-interactive arguments of knowledge.
Hybrid multi-agent optimization method of interpolation search

A new hybrid multi-agent algorithm of interpolation search is proposed to optimize the multi-extreme functions of many variables with a complex structure of level surfaces. This method is based on the construction of interpolation curves and uses the ideas of swarm intelligence. The novelty of the proposed approach consists in the application of various approximations of points sets. The construction of different interpolation polynomials allows adapting to a locally changing structure of the level surfaces of the objective function. A different role of leading points is also used to realize frontal search or deep search of an admissible solution set, thereby providing additional flexibility of the search strategy. Thus, the choice of the interpolation polynomial type and the points by which it is formed, implements two types of search — exploration and exploitation. On the basis of the algorithm developed, software that allows finding the conditional global minimum of functions of many variables is presented. With the help of this software, the efficiency of the algorithm on a set of standard test functions of two variables with a complex structure of level curves was explored. A series of 100 solutions to the problem were carried out and the statistical characteristics of the sample were calculated. Analysis of the obtained statistical characteristics showed high efficiency of the hybrid multi-agent method. The applicability of the algorithm on the applied technical optimization problem was demonstrated. The hybrid multi-agent method of interpolation search successfully coped with this task and the result was close to exact.
Sowing the Seeds for More Usable Web Archives: A Usability Study of Archive-It

In 2017, seven members of the Archive-It Mid-Atlantic Users Group (AITMA) conducted a study of fourteen participants representative of their stakeholder populations to assess the usability of Archive-It, a web archiving subscription service of the Internet Archive. While Archive-It is the most widely used tool for web archiving, little is known about how users interact with the service. This study investigated what users expect from web archives, a distinct form of archival materials. End-user participants executed four search tasks using the public Archive-It interface and the Wayback Machine to access archived information on websites from the facilitators' own harvested collections and provided feedback about their experiences. The tasks were designed to have straightforward outcomes (completed or not completed), and the facilitators took notes on the participants' behavior and commentary during the sessions. Overall, participants reported mildly positive impressions of the Archive-It public user interface based on their sessions. The study identified several key areas of improvement for the Archive-It service pertaining to metadata options, terminology display, indexing of dates, and the site's search box.
A cognitive model for managing the national innovation system parameters based on international comparisons (the case of the EU countries)

To carry out a comparative analysis of the EU countries’ national innovation systems (NIS), a feature vector has been compiled, covering three modules, namely, science, education, and innovation. The feature vector is a valid multidimensional data set of sixteen official statistics indices and two sub-indices of the Global Innovation Index. The development of a cognitive model for managing the NIS parameters required a preliminary three-stage empirical study to determine its elements. In the first stage, cluster analysis was performed (the k-means, metric – Euclidean distance algorithm was used). As a result, the EU countries were divided into four clusters (following multidimensional scaling estimates). In the second stage, a classification analysis (using decision trees) was carried out, which allowed determining three parameters that distinguish clusters (or classes) optimally. These parameters are recognized as important ones in terms of positioning the countries in the general ranking; that is, they can be considered as a priority for the NIS development and improving the countries’ positions in international comparisons. In the third stage, based on the authors’ approach, the significance (information content) of each key parameter is estimated. As a result, a cognitive model was compiled, taking into account the parameter significance. The model can be used in managing the NIS parameters, seeking to increase the system performance and improve the international position of a specific country. The model can also be used by partner countries, for example, Ukraine, as it demonstrates the landscape of EU innovative development and outlines the directions for priority development of NIS towards the European progress.
A conditional density estimation partition model using logistic Gaussian processes

Summary Conditional density estimation seeks to model the distribution of a response variable conditional on covariates. We propose a Bayesian partition model using logistic Gaussian processes to perform conditional density estimation. The partition takes the form of a Voronoi tessellation and is learned from the data using a reversible jump Markov chain Monte Carlo algorithm. The methodology models data in which the density changes sharply throughout the covariate space, and can be used to determine where important changes in the density occur. The Markov chain Monte Carlo algorithm involves a Laplace approximation on the latent variables of the logistic Gaussian process model which marginalizes the parameters in each partition element, allowing an efficient search of the approximate posterior distribution of the tessellation. The method is consistent when the density is piecewise constant in the covariate space or when the density is Lipschitz continuous with respect to the covariates. In simulation and application to wind turbine data, the model successfully estimates the partition structure and conditional distribution.
Anomaly-Based Network Intrusion Detection Using SVM

With the rapid development of new technologies and applications of Internet, much attention has been paid to the detection of anomalies in cyberspace traffic. A series of intrusion detection techniques based on machine learning have been developed. Support vector machine (SVM), as an essential approach, has been paid close attention in this filed. Nevertheless, the existing SVM-based techniques with the training features can not efficiently detect short duration intrusions and attacks in the traffic. To tackle this issue, we propose an anomaly-based SVM detection scheme by extracting and optimizing the training features. It trains the SVM with Kullback-Leibler (KL) divergence and cross-correlation calculated by the control and data planes traffic. Following this way, the novel training method can effectively enhance the detection accuracy. And the performance of the presented scheme is validated and evaluated based on a recent realistic Internet traffic dataset. Finally, relevant results indicate that the developed method establishes the relationship between Transmission Control Protocol (TCP) traffic and intrusions. It can efficiently detect short duration intrusions and attacks in the network traffic.
Social networks fake profiles detection based on account setting and activity

Social networks have become popular due to the ability to connect people around the world and share media (videos, photos, and communications). The present paper focuses on identifying fake profiles in social media. The approaches to identifying fake social media profiles can be classified into the approaches aimed on analysing profiles data. On social networks, fake profile creation is considered to cause more harm than any other form of cyber crime. This crime has to be detected even before the user is notified about the fake profile creation. Many algorithms and methods have been proposed for the detection of fake profiles. In this paper we will assess the impact of using Decision Tree (DT), and Naïve Bayes (NB) to classify the user profiles into fake and genuine.
Article 101 TFEU’s Association of Undertakings Notion and Its Surprising Potential to Help Distinguish Acceptable from Unacceptable Algorithmic Collusion

The machine learning capabilities of new technologies raise provocative questions and challenges for the development of competition law within the digital economy. Academic discussions have focused on how antitrust law should avoid, anticipate, and respond to such behavior. The predominant emerging narrative is that antitrust law, in its current form, is unable to distinguish between acceptable and unacceptable algorithmic collusion. The purpose of this article is to challenge that claim in the context of Article 101 Treaty on the Functioning of the European Union (EU). The reference within Article 101 TFEU to “associations of undertakings” plays a crucial role in that regard and offers a promising tool to better identify and regulate forms of unacceptable algorithmic collusion. Against that background, this article will propose an alternative compliance-focused way forward that could be set up without requiring modifications to the EU legal framework.