Reviewerly/exHarmony huggingface dataset:
An Evasion Attack against Stacked Capsule Autoencoder

Capsule networks are a type of neural network that use the spatial relationship between features to classify images. By capturing the poses and relative positions between features, this network is better able to recognize affine transformation and surpass traditional convolutional neural networks (CNNs) when handling translation, rotation, and scaling. The stacked capsule autoencoder (SCAE) is a state-of-the-art capsule network that encodes an image in capsules which each contain poses of features and their correlations. The encoded contents are then input into the downstream classifier to predict the image categories. Existing research has mainly focused on the security of capsule networks with dynamic routing or expectation maximization (EM) routing, while little attention has been given to the security and robustness of SCAEs. In this paper, we propose an evasion attack against SCAEs. After a perturbation is generated based on the output of the object capsules in the model, it is added to an image to reduce the contribution of the object capsules related to the original category of the image so that the perturbed image will be misclassified. We evaluate the attack using an image classification experiment on the Mixed National Institute of Standards and Technology Database (MNIST), Fashion-MNIST, and German Traffic Sign Recognition Benchmark (GTSRB) datasets, and the average attack success rate can reach 98.6%. The experimental results indicate that the attack can achieve high success rates and stealthiness. This finding confirms that the SCAE has a security vulnerability that allows for the generation of adversarial samples. Our work seeks to highlight the threat of this attack and focus attention on SCAE’s security.
The InsurTechs in Brazil: a legal and regulatory analysis

The insurance industry is facing an unprecedented wave of disruption. While all stakeholders welcome the new insurance products and the improved consumer experience resulting from technological advances, jurists face a number of challenges. In particular, it could be asked: is the current Brazilian legal system able to regulate InsurTechs adequately? The following paper will analyze the legal and regulatory aspects of the operation of companies characterized as “InsurTechs” and how they are functioning in Brazil. To this aim, the following will be addressed: (i) the concept of InsurTech; (ii) the Brazilian regulatory authority’s Regulatory Sandbox approach and; (iii) The functioning of InsurTechs in Brazil.
Wavelet analysis of auditory stimuli for ASSR

The Auditory Steady State Responses (ASSR) are constant responses to constant stimuli, which can be generated under signals with fast frequencies. They follow a behavior similar to quasi-sinusoidal signals. They capture from the bioelectric activity caused by the stimulus, but they are under unwanted brain activity considered as noise, so the acquisition of these signals requires an algorithm to reduce it. In this work, a signal is analyzed as a stimulus for generation of these kinds of responses and because of its behavior similar to the response that provoked, an algorithm is studied. It includes the Wavelet Transform for reduction of noise after the signal is contaminated. Several comparisons were made and they used different kind of mother Wavelets, in order to choose one that contributes to the greatest noise reduction. The mean squared error in each approach was calculated.
Analysis and Research of Abstractive Automatic Summarization Based on Sequential Facts

Automatic summarization is a task of converting text, and the summary result obtained should be able to accurately describe the facts that occurred in the original text. But so far, there are a lot of factual errors in the results obtained by generative summary models, resulting in low quality and poor readability. We believe that adding factual information in the encoding stage can effectively improve the readability of the summary and generate more accurate facts. To this end, we propose an abstractive summary model based on sequential facts and conduct experiments on the CNN/Daily Mail dataset. Experiments have proved that the integration of factual information can effectively improve the ROUGE value and factual accuracy of the summary.
Ascendant Hierarchical Clustering for Instance Matching

With the rapid advancement of semantic web, especially of the web of data, a growing number of independently designed and structured datasets represented by ontologies, are built and need to be integrated in the Linked Open Data (LOD) cloud. In this context, instance matching is presented as a fundamental solution for ontological data sharing and integration. It aims to link co-referent instances (instances that refer to the same real world objects) from various datasets to allow them to complement each other. Traditional systems depend a lot on the quality of schema-level mappings and especially on property mappings, which are not always obvious for the LOD paradigm. In this paper, we propose a schema-free instance matching approach that is independent from property matching results. We transform the instance matching problem into a clustering problem and we solve it by Ascendant Hierarchical Clustering algorithm. Furthermore, we employ some structural information to filter-out obtained results and eliminate wrong mappings. We evaluate our approach on instance matching track from Ontology Alignment Evaluation Initiative (OAEI) benchmark. The experiments show that our approach gets prominent results compared to several participating systems in OAEI’2019 and OAEI’2020.
An Image Similarity Estimation Approach Based on Weighted Features

Recently, many attempts have been made to automatically determine error pixels from various types of input color images through image processing. In this paper, we propose a new method that effectively detects regions different from the reference image from the input image by utilizing the difference calculation and a Gaussian mixture model. The proposed method first detects the color error between the reference image and the input image using the difference operation. Then, the object representation error between the reference image and the input image is detected using a Gaussian mixture model. Finally, in this paper, the final error of the input image is determined by combining the detected color error and the object representation error effectively. The experimental results show that the proposed approach robustly detects error pixels that are not similar to the reference image from the input color image by using the difference calculation and a Gaussian mixture model.
Checking Satisfiability

You’ve seen how to use sequent calculus to check whether a sequent is universally true or has a counterexample. We’re now going to look into the problem of checking satisfiability: whether a logical expression is true for at least one combination of values for the variables, or predicates, that the expression contains.
Prediction of Perception of Security Using Social Media Content

The Perception of Security (PoS) refers to the opinion that persons have about security or insecurity in a place or situation. Real-time monitoring and the capacity of anticipation of citizen’s PoS are highly relevant for citizen’s security planning. Surveys represent the most widely used strategy to quantify PoS. Nevertheless, this approach cannot be applied continuously to obtain real-time monitoring or to predict future PoS. Recent evidence suggests that social network content may provide valuable information to quantify PoS. However, the prediction of these PoS quantifications remains poorly studied. We propose a novel strategy to quantify and anticipate PoS in short time windows using social network data. The model considers the external factors that may contribute to the publication of posts related to PoS and the retweeting phenomena. Results show that the proposed model may provide competitive predictive performances while keeps high levels of interpretability about the factors influencing PoS.
Face Sketch-Photo Synthesis and Recognition

In this paper, we present a simple Face Sketch-Photo Synthesis and Recognition system. Face Sketch Synthesis provides a way to compare and match the faces present in two different modalities (i.e face-sketches and face-photos). We aim to significantly reduce the differences between face-sketches and face-photos and also decrease the texture irregularity between them by converting photos to sketches and vice-versa. This results in effective matching between the two thus simplifying the process of facial recognition. This system is modeled using three major components: (i) For a given input face-photo, obtaining an output face-sketch. It is designed using image processing techniques like 2 scale image decomposition and color dodging. (ii) For a given input face-sketch, obtaining an output face-photo. Convolutional Neural Networks are used to model this component. (iii) For a given query face-sketch or face-photo, recognition of face-photo or face-sketch in the database. It is implemented using Fisherface Linear Discriminant Analysis.
An Analysis Method for Heating Network Leaks Monitoring

Modern district heating (DH) systems are complex engineering structures that play an essential role in large city infrastructures. DH networks have many sensors, nodes, and methods for monitoring the status of the DH network. Sensing, processing, analytical actuation (SPA) of incoming information handled by the SPA semantic Computing method can be applied to similar problems. The SPA Semantic Computing method searches for correlations between the sets of incoming data and to identify the correct scenario to respond to events. This article explores the integration of SPA functions to analyze multivariate sensing data, including data from multivariable sensors and infrared images, for creating a monitoring system for DH networks. The focus is to assess whether the SPA approach is a suitable candidate to use to monitor the emergency events of the DH network. Specific target data for the assessment are [1] multi-parameter DH network sensor data, such as water temperature, sweat rate, energy delivered, etc., and [2] infrared image data from a camera mounted on the unmanned aerial vehicle (UAV) for monitoring the location of the underground DH network leaks. A multivariate computational model, a mathematical model of meaning (MMM), and a spatial image filtering method are proposed for integrating SPA semantic computing for emergency leak detection in DH networks.
Potential Field-Based Collision Avoidance Trajectory Planning for Industrial Robot Arms to Reduce Residual Vibration

In industrial robots, preventing collisions with obstacles and creating smooth trajectories are critical issues. In previous research, an S-curve profile has been studied to reduce jerk. Many potential field method approaches have been researched to avoid collisions with obstacles in mobile robots. Typical potential fields generate a repulsive force for a robot to avoid obstacles inside the assumed boundary near the obstacle, but this repulsive force can affect robot motion that is above the maximum velocity because the repulsive force adds to the attractive force after avoiding an obstacle if the attractive and repulsive forces are in the same direction. This paper presents an asymmetric S-curve profile with a modified potential field, which generates a smooth velocity trajectory while avoiding the static obstacle and being over maximum velocity, as well as reducing residual vibration. The 6-axis robot uses a damped least square for singularity avoidance, and an augmented Jacobian is used with wrist parts added to the end-effector for a fixed pose. Simulations were conducted on MATLAB. We applied the simulation results to the NEUROMEKA INDY7 (6-axis) robot. According to the results, the proposed method produces a smoother trajectory than existing methods.
Self-Supervised Neural Topic Modeling

Topic models are useful tools for analyzing and interpreting the main underlying themes of large corpora of text.Most topic models rely on word co-occurrence for computing a topic, i.e., a weighted set of words that together represent a high-level semantic concept.In this paper, we propose a new light-weight Self-Supervised Neural Topic Model (SNTM) that learns a rich context by learning a topic representation jointly from three co-occurring words and a document that the triplet originates from.Our experimental results indicate that our proposed neural topic model, SNTM, outperforms previously existing topic models in coherence metrics as well as document clustering accuracy.Moreover, apart from the topic coherence and clustering performance, the proposed neural topic model has a number of advantages, namely, being computationally efficient and easy to train.
Secret Key Steganography: improve security level of LSB algorithm

Internet has revolutionized the way people share information, store data, communicate, learn, and do business. Internet has been used for information transmission; this has changed in the last decade. Today, governments, businesses and individuals are increasingly relying on-line applications; the need to secure data exchange over the Internet and store it on the cloud has gained more importance. Data security aims to keep information safe by using cryptography and/or steganography. Steganography is a suitable candidate for keeping information safe, hiding the existence of data itself inside another medium. The Least Significant Bit (LSB) algorithm is one of the most common techniques to hide secret messages in an image. The main problem with LSB is knowing whether such an image has a secret message inside, making it easy to retrieve it by collecting the least significant bit from the steg-image. This paper presents an attempt to improve the security level of the LSB algorithm by using a secret steganography key. The proposed algorithm <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SK</inf> LSB is encoding the secret key from the cover image, encodes the secret message using the secret key, embedding it to the cover image. Even if attackers know about the steganography, they cannot know about the secret key. The <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SK</inf> LSB algorithm helps to get a higher and better level of security, useful for keeping sensitive, relevant, and important information stored. The results regarding this paper are compared with LSB algorithm regarding its Mean Square Error (MSE) and Peak Signal to Noise Ratio (PSNR) values aiming to show this method is more secure and does not affect the image imperceptibility.
Group Public Key Encryption With Equality Test for Dynamic Membership

Group public key encryption with equality test supports group granularity authorization, which allows a tester to perform equality test only on ciphertexts of group users. If two ciphertexts are encrypted by users from two different groups, they cannot be performed the equality test. Indeed, most of the existing works either only support users to join the group but do not support group members to leave the group, or are inefficient for all the ciphertexts need to be re-encrypted after each revocation operation. In this paper, we propose a group public key encryption with equality test for dynamic membership. Compared with the existing works, (1) our scheme supports efficient member revocation function. Once a group member has malicious behavior, it can be withdrawn from the group, which improves the security of the scheme, (2) our scheme supports the verification of ciphertext encryptors, that is, it verifies whether the ciphertext encryptors have been revoked and come from the same group before an equality test, which improves the efficiency of our scheme. Besides, we prove our scheme can achieve OW-CCA security against the Type-I adversary and IND-CCA security against the Type-II adversary in the random oracle model.
Efficient Face Editing Generation Adversarial Networks Fused with Coordinate Attention

Aiming at the problems of unnatural faces, unclear image details, and too long model training time in the faces generated by the existing face editing methods, an efficient face editing generation adversarial networks embedded with coordinate attention is constructed. In order to reduce the amount of network parameters and improve the fine details of the generated image, the model integrates the ghost module with the attention normalization layer and the ELU function in the generated network layer to enhance the semantic relationship and alleviate the gradient descent simultaneously; Adding a skip connections between the encoder and the decoder and jointly selective transfer units to reduce the operation of reducing attributes and the generation of artifacts; the discriminator of the coordinate attention module is introduced, and the position information is increased to obtain more attention areas to make the content more real and natural; Using TV regularization with reconstruction loss and attribute classification loss weighting to ensure the smoothness of the generated image. Experiments on the CelebA showed significant improvements in the evaluation indicators of PSNR and SSIM, which verified the effectiveness of the method.
Informationally Redundant Utterances Elicit Pragmatic Inferences

Most theories of pragmatics and language processing predict that speakers avoid excessive informational redundancy. Informationally redundant utterances are, however, quite common in natural dialog. From a comprehension standpoint, it remains unclear how comprehenders interpret these utterances, and whether they make attempts to reconcile the ‘dips’ in informational utility with expectations of ‘appropriate’ or ‘rational’ speaker informativity. We show that informationally redundant (overinformative) utterances can trigger pragmatic inferences that increase utterance utility in line with comprehender expectations. In a series of 3 studies, we look at utterances which refer to stereotyped event sequences describing common activities ( scripts ). When comprehenders encounter utterances describing events that can be easily inferred from prior context, they interpret them as signifying that the event conveys new, unstated information (i.e. an event otherwise assumed to be habitual, such as paying the cashier when shopping , is reinterpreted as non-habitual). Further, we show that the degree to which such inferences are triggered depends on the framing of the utterance. In the absence of prosodic or discourse markers indicating the speaker’s specific intent to communicate the given information, such inferences are far less likely to arise. Overall, the results demonstrate that excessive conceptual redundancy leads to comprehenders revising the conversational common ground, in an effort to accommodate unexpected dips in informational utility.
A Study on The Critical Thinking Disposition - Focused on Comparison by Elements -

The purpose of this paper is to compare, analyze, and synthesize the elements of critical thinking disposition. Critical thinking researchers presented various elements of critical thinking disposition. It is necessary to synthesize this elements. To this end, the study was divided into two step. First step, I investigated the elements of thinking disposition suggested by Richard Paul, Robert Ennis, Kim Kwang-soo and others. Second step, based on the Paul''s elements, I compared and analyzed the elements of thinking disposition. Through this process, I was able to synthesize the seven elements : ''Openness, Objectivity, Fairness, Sincerity, Practicality, Exploration, and Skepticism''. Through this study, I was able to narrow the gap between the elements of critical thinking disposition suggested by various researchers. And I think this study will contribute to education for critical thinking disposition.
An Approach to an efficient Evaluation of SPARQL Queries in Databases

Addressing the issue of efficient SPARQL 1.1 query evaluation, this paper proposes a reordering approach that can handle any SPARQL query, regardless of the version. The presented method uses the average incoming and outgoing degree of property edges in the underlying RDF graph. Elements next to basic graph patterns available in SPARQL 1.1 were analyzed regarding their impact on the performance to formulate rules for rearranging them. We achieve promising results: Using the widely known Apache Jena and Eclipse RDF4J as triple store, we reached up to 50’000 times faster execution times than the best performing optimization approach included in Apache Jena using queries from three different well-known SPARQL benchmarks: LDBC SNB, BSBM, and SP2Bench.
Simultaneous Image Compression and Encryption Via KPCA and CS

This paper introduces a novel image encryption algorithm by means of LSS-CML, compressive sensing (CS) and K-Principle Component Analysis (KPCA) method. Firstly, the original image is compressed using CS and the KPCA method, in which LSS-CML is utilized to construct the key-controlled pseudo-random measurement matrices. Then the compressed image is shuffled and diffused by the key streams produced from both the original image and LSS-CML. The initial conditions and parameters of LSS-CML are updated with the help of the original image and the hash function SUA-S12. Thus the key streams rely on both the plain-image and LSS-CML, which make the encryption approach resistance to the 2chosen-plaintext and known-plaintext attacks. Experimental results and performance analysis have verified the validity of the presented scheme.
Fuzzy c-Means Clustering with Discriminative Projection

The clustering technique plays an important role in data mining and machine learning fields. Clustering for high-dimensional data, such as texts, images, and videos, remains a challenging task due to the existence of many noise features. The widely used methods for this issue focus on mining a effective pattern in high-dimensional data using some dimensionality reduction techniques before clustering. This strategy slightly mitigates the effects of irrelevant and redundant features, but cannot significantly improve the clustering performance because the captured pattern by dimensionality reduction is not directly related to the clustering task. In this paper, we propose a unified framework to achieve discriminative dimensionality reduction and fuzzy clustering for high-dimensional data simultaneously. The proposed framework not only utilizes the clustering results to directly guide or supervise the process of discriminative dimensionality reduction, but also controls the clustering fuzziness more easily by a <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$F$</tex> -norm regularization term. An efficient optimization algorithm is exploited to address the objective function of our method, which is proved to converge to the local optimal solution in theory. We evaluate the proposed method on three large-scale fine-grained image datasets, including Birds, Flowers, and Cars, for clustering and retrieval two tasks. The experimental results on metrics ACC, NMI, ARI and Recall@K indicate that our method achieves the comparable performance over the state-of-the-art methods.
Building Czech Textbook Corpora (UcebKo) for Word-Formation Research of Czech as a Second Language

Abstract This work-in-progress paper presents a specialized language corpus UcebKo built from textbooks of Czech for foreigners. The corpus integrates three subcorpora (UcebKo-A2, UcebKo-B1, and UcebKo-B2) which allow research of Czech as a second/foreign language at chosen language levels (A2, B1, and B2). In this case, the research is focused on word-formation, where the first results, i.e., mapping of derived words denoting persons, illustrate the approach and methodology used.