Reviewerly/exHarmony huggingface dataset:
Defense-VAE: A Fast and Accurate Defense Against Adversarial Attacks

Deep neural networks (DNNs) have been enormously successful across a variety of prediction tasks. However, recent research shows that DNNs are particularly vulnerable to adversarial attacks, which poses a serious threat to their applications in security-sensitive systems. In this paper, we propose a simple yet effective defense algorithm Defense-VAE that uses variational autoencoder (VAE) to purge adversarial perturbations from contaminated images. The proposed method is generic and can defend white-box and black-box attacks without the need of retraining the original CNN classifiers, and can further strengthen the defense by retraining CNN or end-to-end finetuning the whole pipeline. In addition, the proposed method is very efficient compared to the optimization-based alternatives, such as Defense-GAN, since no iterative optimization is needed for online prediction. Extensive experiments on MNIST, Fashion-MNIST, CelebA and CIFAR-10 demonstrate the superior defense accuracy of Defense-VAE compared to Defense-GAN, while being 50x faster than the latter. This makes Defense-VAE widely deployable in real-time security-sensitive systems. Our source code can be found at https://github.com/lxuniverse/defense-vae .
Solar cooking thermal image processing applied to time series analysis of fuzzy stage and inconsiderable Fourier transform method

Natural food cooking (solar cooker) process of foiling material reaction of thermal image has been applied to time series of a new fractional Fourier transform based on image fusion method. Evaluations of a solar box cooker have been fashioned for pious ability, strong, secure and soothing act economical of any procedure we strait a failing unreserved conduct. By doing thermal image outgrowth of defected solar cooking food the faults due to territorial ardent blemish or abortive disgrace or shatter or crevice due to lack and study we have to create a current zone of Veltech Multitech Engineering College, Avadi Chennai, Tamilnadu, India. Previous methods utility was not capable to distinguish between dark regions and defected regions. This system clearly glorifies between the perpendicular province and deficiency station. A solar cooker is used for the characteristic birth of the image and image reconstruction to order the image as a deficiency or not fault. All the ascent likeness is dissolved by fractional Fourier transform, and then the insignificant arrangement is selected suited. The experiments show that this algorithmic program is especially effectual of thermal images (foodstuff), but also have a commendable manifestation for the infrared and visual images fusion.
Consistency and asymptotic normality of Latent Block Model estimators

The Latent Block Model (LBM) is a model-based method to cluster simultaneously the d columns and n rows of a data matrix.Parameter estimation in LBM is a difficult and multifaceted problem.Although various estimation strategies have been proposed and are now well understood empirically, theoretical guarantees about their asymptotic behavior is rather sparse and most results are limited to the binary setting.We prove here theoretical guarantees in the valued settings.We show that under some mild conditions on the parameter space, and in an asymptotic regime where log(d)/n and log(n)/d tend to 0 when n and d tend to infinity, (1) the maximum-likelihood estimate of the complete model (with known labels) is consistent and (2) the log-likelihood ratios are equivalent under the complete and observed (with unknown labels) models.This equivalence allows us to transfer the asymptotic consistency, and under mild conditions, asymptotic normality, to the maximum likelihood estimate under the observed model.Moreover, the variational estimator is also consistent and, under the same conditions, asymptotically normal.
Mind the Composition: Birthday Bound Attacks on EWCDMD and SoKAC21

In an early version of CRYPTO'17, Mennink and Neves proposed EWCDMD, a dual of EWCDM, and showed n-bit security, where n is the block size of the underlying block cipher. In CRYPTO'19, Chen et al. proposed permutation based design SoKAC21 and showed 2n/3-bit security, where n is the input size of the underlying permutation. In this paper we show birthday bound attacks on EWCDMD and SoKAC21, invalidating their security claims. Both attacks exploit an inherent composition nature present in the constructions. Motivated by the above two attacks exploiting the composition nature, we consider some generic relevant composition based constructions of ideal primitives (possibly in the ideal permutation and random oracle model) and present birthday bound distinguishers for them. In particular, we demonstrate a birthday bound distinguisher against (1) a secret random permutation followed by a public random function and (2) composition of two secret random functions. Our distinguishers for SoKAC21 and EWCDMD are direct consequences of (1) and (2) respectively.
Broadcasting Steganography in the Blockchain

Conventional steganography embeds secret data into an innocent cover object such as image and video. The resulting stego object will be sent to the desired receiver via an insecure channel. Though the channel monitor cannot distinguish between normal objects and objects containing hidden information, he has the ability to intercept and alter the objects so as to break down the covert communication. It inspires us to introduce new steganography in Blockchain in order to overcome the aforementioned problem since an attacker cannot tamper Blockchain data once a block was generated, meaning that, a receiver will always be able to fully retrieve the secret data with the secret key. For the proposed work, the miner serves as the steganographer, who embeds secret data into the transactions within a block during the process of generating the block. To secure the data embedding process within a block, we choose a part of transactions in a block according to a secret key, and embed the secret data by repeatable-address arrangement. Our analysis demonstrates that, it is difficult for an attacker to extract the embedded data. Since the miner collects normal transactions for generating a block and does not generate abnormal transactions, the data embedding process will not arouse suspicion, providing a high level of security.
Fake opinion detection: how similar are crowdsourced datasets to real data?

Identifying deceptive online reviews is a challenging tasks for Natural Language Processing (NLP). Collecting corpora for the task is difficult, because normally it is not possible to know whether reviews are genuine. A common workaround involves collecting (supposedly) truthful reviews online and adding them to a set of deceptive reviews obtained through crowdsourcing services. Models trained this way are generally successful at discriminating between 'genuine' online reviews and the crowdsourced deceptive reviews. It has been argued that the deceptive reviews obtained via crowdsourcing are very different from real fake reviews, but the claim has never been properly tested. In this paper, we compare (false) crowdsourced reviews with a set of 'real' fake reviews published on line. We evaluate their degree of similarity and their usefulness in training models for the detection of untrustworthy reviews. We find that the deceptive reviews collected via crowdsourcing are significantly different from the fake reviews published online. In the case of the artificially produced deceptive texts, it turns out that their domain similarity with the targets affects the models' performance, much more than their untruthfulness. This suggests that the use of crowdsourced datasets for opinion spam detection may not result in models applicable to the real task of detecting deceptive reviews. As an alternative method to create large-size datasets for the fake reviews detection task, we propose methods based on the probabilistic annotation of unlabeled texts, relying on the use of meta-information generally available on the e-commerce sites. Such methods are independent from the content of the reviews and allow to train reliable models for the detection of fake reviews.
An Overview of Reviews as a Trend Maker in the Field

The authors consider the methodology basics of reviews as a prospective type of scholarly publications, their taxonomy and most popular review types (narrative reviews, bibliometric reviews, systematic reviews, reviews of reviews, scoping reviews, meta-analysis), as well as specific features of procedures and algorithms for conducting reviews. Top 100 of highly cited reviews on higher education from 2010 to 2019 published in high-ranking peer-reviewed journals and indexed in the international database Scopus is based on the traditional methodology that aims to sample the most essential and influential publications of the kind in a well-represented and unbiased way and to subject the sampled reviews to content, bibliometric, and linguistic analyses. based on the inclusion criteria, keywords and methods of objective selection and sampling of the publications to be reviewed and analyzed, the authors singled out the essential thematic clusters in Top-100 list (educational technologies, university, student, teaching, learning, assessment, etc.) and determined the key directions in the review field of study. Each cluster contains a brief description of the most important aspects and approaches to various topics related to higher education, an analysis of their novelty and existing gaps in the field. According to the rhetoric theory of scholarly text by John Swales named ‘Moves and steps’, the authors offer a uniform rhetoric schema of reviews, commenting on the text components and their contents. Such a schema may serve as a guideline for authors of reviews made up for international peer-reviewed journals. The most popular publications by citations and number of publications entail reviews devoted to the culture of higher education; educational technologies and peculiarities of their application in the new educational landscape; online education as a new dimension of education requiring a special ecosystem; academic ethics of university teachers; soft skills development necessary for successful professional development; academic and scientific libraries as new centers for scientific and academic communication.
Classification of Aviation Safety Reports using Machine Learning

The growing aviation industry in both number of passengers and aircraft movements in combination with the implementation of Just Culture facilitates a growing number of occurrence reports being submitted by service providers to their competent authority. Still, actually using the sheer amount of safety data being collected with these reports for safety initiatives has proven to be difficult as analysing the reports is a time consuming activity. Moreover, reports usually suffer from missing data and labelling. This paper demonstrates an automatic text classification method by means of machine learning. By automatically classifying occurrence reports analysts may be able to work more efficiently as conducting queries on reporting databases become more accurate thanks to the categorization of reports. In this paper, a text classifier is trained by means of a Random Forest algorithm on the ICAO Occurrence Category. The classifier is used to classify more than 45,000 reports while reaching accuracy between the 80–93 %.
Doctoral Programs Are in Anticipation of Change: Postgraduates and Their Scientific Supervisors’ Readiness

Nowadays a postgraduate school reform is expected. It aims at enhancing the scientific preparation of postgraduate students and increasing the number of dissertations. It is planned that the educational process will be carried out in accordance with federal state requirements, and a dissertation will be presented as the final qualification work. Also, the educational accreditation of doctoral programs is going to be cancel. The article discusses the question how far universities are ready for these innovations, and presents the results of a survey aimed at studying the attitude of postgraduate students and their supervisors to the educational process in postgraduate school. The empirical data obtained indicate that the existing model of scientific and pedagogical personnel training is more focused on the educational activities rather than research, there is a mismatch between content of training courses and cognitive needs of students, and there are no necessary material and technical conditions for conducting experimental work. The expected increase in hours for research activities can lead to a reduction in the disciplines that ensure the formation of pedagogical competencies in postgraduate students. The authors believe that pedagogical education for postgraduate students of industrial universities, including technical ones, is necessary, since teachers of special disciplines are not massively trained anywhere. The solution of the question of the optimal ratio of scientific and educational training is possible through the introduction of individual educational paths, which are built taking into account the orientation of postgraduate students towards teaching or research activities.
Pre-Trained BERT-GRU Model for Relation Extraction

Existing works on entity relation extraction are based on neural networks and achieve state-of-the art performance by leveraging extra lexical and syntactic features from external NLP pre-processing tools. Feature based methods are hard to be generalized in new language, and the pre-processing procedure may lead to additional error. To overcome this problem, we propose BERT-GRU(Bidirectional Encoder Representations from Transformer with Bidirectional Gated Recurrent Unit), which exploits pre-trained deep language representations to obtain the latent linguistic information for relation extraction and without using any high-level linguistic resources extracted by NLP tools. We conduct our experiment on GPU environment which can enhance the training procedure, results on SemEval-2010 task 8 show that our model outperforms existing methods without any external features.
Event-Based Predictive Maintenance on Top of Sensor Data in a Real Industry 4.0 Case Study

We deal with a real predictive maintenance case study encountered in modern Industry 4.0 settings: based on logs of past failures, we train a model to predict critical failures of equipment without any other domain expert knowledge well in advance. We propose a novel methodology that combines and extends the state-of-the-art in event-based predictions with advanced time-series analytics. This renders our technique applicable directly onto the sensor data, as it is produced in a modern factory setting. Further, we show that employing unsupervised learning techniques, such as continuous outlier monitoring, is a competitive approach. Although our techniques are developed and tested in a specific case study, they can be transferred to arbitrary settings.
Surgeon–robot interface development framework

The progress of robotic medicine leads to the emergence of an increasing number of highly specialized automated systems based on specialized software. In any such system, there is the task of translating the surgeon’s requests into the process of automated procedure execution. The hardware and software system that provides the translation is the interface between the surgeon and the robot. This paper proposes a generalized framework architecture for the development of such software — the surgeon–robot interface. Existing implementations of such an interface are considered, solutions for the internal structure design of the framework are proposed. Experiments were performed using a prototype of the proposed framework. Such a development framework will allow one to effectively implement the surgeon–robot interfaces at all stages of the robotization of medical procedures, from prototype to final use in the operating room.
Gland Segmentation in Histopathological Images by Deep Neural Network

Histology method is vital in the diagnosis and prognosis of cancers and many other diseases. For the analysis of histopathological images, we need to detect and segment all gland structures. These images are very challenging, and the task of segmentation is even challenging for specialists. Segmentation of glands determines the grade of cancer such as colon, breast, and prostate. Given that deep neural networks have achieved high performance in medical images, we propose a method based on the LinkNet network for gland segmentation. We found the effects of using different loss functions. By using Warwick-Qu dataset, which contains two test sets and one train set, we show that our approach is comparable to state-of-the-art methods. Finally, it is shown that enhancing the gland edges and the use of hematoxylin components can improve the performance of the proposed model.
Range Based Confusion Matrix for Imbalanced Time Series Classification

The following topics are dealt with: learning (artificial intelligence); pattern classification; image classification; natural language processing; neural nets; decision making; feature extraction; neural net architecture; image segmentation; text analysis.
Analysis of Criteria for the Selection of Machine Learning Frameworks

With the many achievements of Machine Learning in the past years, it is likely that the sub-area of Deep Learning will continue to deliver major technological breakthroughs [1]. In order to achieve best results, it is important to know the various different Deep Learning frameworks and their respective properties. This paper provides a comparative overview of some of the most popular frameworks. First, the comparison methods and criteria are introduced and described with a focus on computer vision applications: Features and Uses are examined by evaluating papers and articles, Adoption and Popularity is determined by analyzing a data science study. Then, the frameworks TensorFlow, Keras, PyTorch and Caffe are compared based on the previously described criteria to highlight properties and differences. Advantages and disadvantages are compared, enabling researchers and developers to choose a framework according to their specific needs.
Which are the influential publications in the Web of Science subject categories over a long period of time? CRExplorer software used for big-data analyses in bibliometrics

What are the landmark papers in scientific disciplines? Which papers are indispensable for scientific progress? These are typical questions which are of interest not only for researchers (who frequently know the answers – or guess to know them) but also for the interested general public. Citation counts can be used to identify very useful papers since they reflect the wisdom of the crowd – in this case, the scientists using published results for their research. In this study, we identified with recently developed methods for the program CRExplorer landmark publications in nearly all Web of Science subject categories (WoS-SCs). These are publications which belong more frequently than other publications during the citing years to the top-1‰ in their subject area. As examples, we show the results of five subject categories: ‘Information Science &amp; Library Science’, ‘Computer Science, Information Systems’, ‘Computer Science, Software Engineering’, ‘Psychology, Social’ and, ‘Chemistry, Physical’. The results of the other WoS-SCs can be found online at http://crexplorer.net . An analyst of the results should keep in mind that the identification of landmark papers depends on the used methods and data. Small differences in methods and/or data may lead to other results.
#MeTooMaastricht: Building a Chatbot to Assist Survivors of Sexual Harassment

Inspired by the recent social movement of #MeToo, we are building a chatbot to assist survivors of sexual harassment cases (designed for the city of Maastricht but can easily be extended). The motivation behind this work is twofold: properly assist survivors of such events by directing them to appropriate institutions that can offer them help and increase the incident documentation so as to gather more data about harassment cases which are currently under reported. We break down the problem into three data science/machine learning components: harassment type identification (treated as a classification problem), spatio-temporal information extraction (treated as Named Entity Recognition problem) and dialogue with the users (treated as a slot-filling based chatbot). We are able to achieve a success rate of more than 98% for the identification of a harassment-or-not case and around 80% for the specific type harassment identification. Locations and dates are identified with more than 90% accuracy and time occurrences prove more challenging with almost 80%. Finally, initial validation of the chatbot shows great potential for the further development and deployment of such a beneficial for the whole society tool.
Adaptively Secure Constrained Pseudorandom Functions in the Standard Model

Constrained pseudorandom functions (CPRFs) allow learning "constrained" PRF keys that can evaluate the PRF on a subset of the input space, or based on some predicate. First introduced by Boneh and Waters [AC'13], Kiayias et al. [CCS'13] and Boyle et al. [PKC'14], they have shown to be a useful cryptographic primitive with many applications. These applications often require CPRFs to be adaptively secure, which allows the adversary to learn PRF values and constrained keys in an arbitrary order. However, there is no known construction of adaptively secure CPRFs based on a standard assumption in the standard model for any non-trivial class of predicates. Moreover, even if we rely on strong tools such as indistinguishability obfuscation (IO), the state-of-the-art construction of adaptively secure CPRFs in the standard model only supports the limited class of $$\mathbf{NC} ^1$$ predicates. In this work, we develop new adaptively secure CPRFs for various predicates from different types of assumptions in the standard model. Our results are summarized below. The first and second constructions are the first CPRFs for any non-trivial predicates to achieve adaptive security outside of the random oracle model or relying on strong cryptographic assumptions. Moreover, the first construction is also the first to achieve any notion of collusion-resistance in this setting. Besides, we prove that the first and second constructions satisfy weak 1-key privacy, which roughly means that a constrained key does not reveal the corresponding constraint. The third construction is an improvement over previous adaptively secure CPRFs for less expressive predicates based on IO in the standard model.
Active Contour Model in Deep Learning Era: A Revise and Review

Active Contour (AC)-based segmentation has been widely used to solve many image processing problems, specially image segmentation. While these AC-based methods offer object shape constraints, they typically look for strong edges or statistical modeling for successful segmentation. Clearly, AC-based approaches lack a way to work with labeled images in a supervised machine learning framework. Furthermore, they are unsupervised approaches and strongly depend on many parameters which are chosen by empirical results. Recently, Deep Learning (DL) has become the go-to method for solving many problems in various areas. Over the past decade, DL has achieved remarkable success in various artificial intelligence research areas. DL is supervised methods and requires large volume ground-truth. This paper first provides a fundamental of both Active Contour techniques and Deep Learning framework. We then present the state-of-the-art approaches of Active Contour techniques incorporating in Deep Learning framework.