Reviewerly/exHarmony huggingface dataset:
A Comparative Study of Improved Harmony Search Algorithm in Four Bar Mechanisms

There are problems that are difficult to solve through mathematical programming or by classical methods. These problems are called hard problems due to their high complexity or high dimension. On the other hand, mataheuristics intends to seek a better solution to a problem. The Improvement Harmony Search algorithm is proposed under modification of the bandwidth parameter increasing the quality of the exploitation of the solutions. That is why within the state of the art, are mentioned several versions of harmonic search. The state of the art is supports the fact that the algorithm belongs to the category of those who make modifications to its parameters. This research demonstrates the ability of ImHS to solve a problem of high complexity focused on solving four-bar mechanism designs, whose solutions imply high dimension and which are also classified as hard problems. The two problems that are solved in this investigation, are problems very attacked within the state of the art by various metaheuristics. A comparison is then made against previous solutions with traditional metaheuristics and other versions of harmony search algorithm. Finally, the effectiveness of performance is demonstrated, where proposed algorithm it exceeded five metaheuristic algorithms and five harmony search versions. An optimum is provided in an easy and useful way, the parametric statistics are improved and the number of feasible solutions is exceeded in NP-hard problems as in the case of problems with four-bar mechanisms.
PA-GAN: A Patch-Attention Based Aggregation Network for Face Recognition in Surveillance

Face recognition in unconstraint surveillance is a complicated problem on account of motion blur, expression variations and low resolution.Recent works have demonstrated that patch-attention is strictly more powerful than convolution in recognition models.In this study, we investigate the task of unconstraint surveillance face recognition.First, a Patch-Attention Generative Adversarial Network (PA-GAN) model is devised to aggregate some robust features on behalf of a set of raw surveillance frames, which not only increases the recognition accuracy but also reduces the computational costs of face matching.Second, an improved center loss function combined with abundant unlabeled surveillance faces is utilized to accurately classify the known identities.With the proposed method, the discriminativeness of the face representations is largely enhanced.Finally, the proposed method is verified in two widely used datasets, IJB-A dataset and QMUL-SurvFace dataset to demonstrate the effectiveness.Evaluation of the algorithm performances in comparison with other state-of-the-art methods indicates that the proposed design can achieve competitive accuracy on both the verification and identification protocols.
Ground-to-Aerial Viewpoint Localization via Landmark Graphs Matching

The capability of associating an image to its geographical location is a significant concern in journalism and digital forensics. Given the availability of geo-tagged satellite imagery for most of the Earth's surface, retrieving the location of a generic picture can be addressed as a cross-view image matching between aerial and ground views. In this paper, we outline some initial steps toward the development of a fully-unsupervised algorithm for ground-to-aerial image matching, exploiting the view-invariant adjacency relationships of the landmarks appearing in both views. We introduce a graph-based strategy that, given a set of pre-extracted landmarks, localizes the viewpoint of a ground-level 360-degree image within a broad aerial view of the same area, by matching the respective landmark graphs according to a specifically designed likelihood model.
Key Exchange Protocol Defined over a Non-Commuting Group Based on an NP-Complete Decisional Problem

In this paper we present a cryptographic primitive based on non-commutative cryptography. This primitive is used for key exchange protocol (KEP) construction. We prove that the security of this primitive relies on a nondeterministic polynomial complete (NP-Complete) decisional problem. Recently there are no known quantum cryptanalysis algorithms effectively solving NP-Complete problems. So far, KEPs are widely used in secure communication channel creation, e.g., in hypertext transfer protocol secure (https://) and are based on traditional cryptographic primitives representing commutative cryptography. However, the security of these protocols does not rely on NP-Complete problems and hence, according to P. W. Shorr, they are vulnerable to quantum cryptanalysis. We use one of seven non-commuting groups of order 16 which is not isomorphic to any other group to define a platform group for a key exchange protocol based on previously considered matrix power function (MPF). By investigating basic properties on the group M16 and their implementation for our goals we fix the order of actions in MPF from left to right. Furthermore, we define a special form of the base matrix and separate templates for left and right power matrices. Using properties of the specified templates and Schaeffer criteria we prove that the security of the proposed key exchange relies on an NP-Complete decisional problem.
Mitigator: Privacy policy compliance using trusted hardware

Abstract Through recent years, much research has been conducted into processing privacy policies and presenting them in ways that are easy for users to understand. However, understanding privacy policies has little utility if the website’s data processing code does not match the privacy policy. Although systems have been proposed to achieve compliance of internal software to access control policies, they assume a large trusted computing base and are not designed to provide a proof of compliance to an end user. We design Mitigator, a system to enforce compliance of a website’s source code with a privacy policy model that addresses these two drawbacks of previous work. We use trusted hardware platforms to provide a guarantee to an end user that their data is only handled by code that is compliant with the privacy policy. Such an end user only needs to trust a small module in the hardware of the remote back-end machine and related libraries but not the entire OS. We also provide a proof-of-concept implementation of Mitigator and evaluate it for its latency. We conclude that it incurs only a small overhead with respect to an unmodified system that does not provide a guarantee of privacy policy compliance to the end user.
Impact of Current Phishing Strategies in Machine Learning Models for Phishing Detection

Phishing is one of the most widespread attacks based on social engineering. The detection of Phishing using Machine Learning approaches is more robust than the blacklist-based ones, which need regular reports and updates. However, the datasets currently used for training the Supervised Learning approaches have some drawbacks. These datasets only have the landing page of legitimate domains and they do not include the login forms from the websites, which is the most common situation in a real case of Phishing. This makes the performance of Machine Learning-based models to drop, especially when they are tested using login pages. In this paper, we demonstrate that a machine learning model trained with datasets collected some years ago, could have high performance when tested with the same outdated datasets, but its performance decreases notably with current datasets, using in both cases the same features. We also demonstrate that, among the commonly applied machine learning algorithms, SVM is the most resilient to the new strategies used by the current phishing attacks. To prove these statements, we created a new dataset, Phishing Index Login URL dataset (PILU-60K), containing 60K URLs from legitimate index and login URLs, together with Phishing samples. We evaluated several machine learning methods with the known datasets PWD2016, Ebbu2017 and also with two subsets of PILU, PIU-40K and PLU-40K, which contains only index pages and only login pages respectively, showing that the accuracy decreases remarkably. We also found that Random Forest is the recommended approach among all the evaluated methods with the newly created dataset.
Rule Algebras for Adhesive Categories.

We demonstrate that the most well-known approach to rewriting graphical structures, the Double-Pushout (DPO) approach, possesses a notion of sequential compositions of rules along an overlap that is associative in a natural sense. Notably, our results hold in the general setting of $\mathcal{M}$-adhesive categories. This observation complements the classical Concurrency Theorem of DPO rewriting. We then proceed to define rule algebras in both settings, where the most general categories permissible are the finitary (or finitary restrictions of) $\mathcal{M}$-adhesive categories with $\mathcal{M}$-effective unions. If in addition a given such category possess an $\mathcal{M}$-initial object, the resulting rule algebra is unital (in addition to being associative). We demonstrate that in this setting a canonical representation of the rule algebras is obtainable, which opens the possibility of applying the concept to define and compute the evolution of statistical moments of observables in stochastic DPO rewriting systems.
Image Recognition and Analysis of Intrauterine Residues Based on Deep Learning and Semi-Supervised Learning

Residual placenta is one of the common types of postpartum complications in clinical practice. Residual placenta is also the main and most common cause of late postpartum hemorrhage. This article proposes a spatial pyramid loop module, which solves the problem that the existing network structure cannot effectively extract the semantic information and category information in the image at the same time. The spatial pyramid structure is used to effectively extract the semantic information and category information. In addition, this article proposes to use cyclic convolutional network to realize the transfer function of information at different scales, and build it in the spatial pyramid structure to further strengthen the ability to extract semantic information and category information. This article proposes a feature fusion module to solve the impact of image classification network used in the base network in the existing network structure. The attention mechanism is used to achieve the effective fusion of high-dimensional features and low-dimensional features in the base network to reduce the influence of the base network, so as to better recover the recognition and prediction results. A semantic category loss function is proposed to supervise the categories of objects in images. This article builds it on the feature layer with the smallest scale, which not only increases the intermediate supervision to make the network fully converge, but also reduces the difficulty of extracting category information, and makes full use of the information transfer function of the cyclic convolutional network. This article introduces uncertainty information into the field of image segmentation to provide the accuracy of segmentation. For the purpose of uncertainty information, this article improves the network structure. At the same time for the image segmentation task, this article improves the Bayesian cross entropy loss function. The experiment verifies the necessity of improving the Bayesian crossover function in this article and the effectiveness of the conditional random field used in this article, and also verifies the effectiveness of the proposed semi-supervised learning method.
Generic Structure and Rhetorical Relations of Online Book Reviews in English, Japanese and Chinese

Abstract We examine the generic structure and rhetorical relations that characterise online book reviews in English, Japanese and Chinese to describe the pragmatic features of this emerging genre in a contrastive light. The corpus we analyse contains online book reviews written by consumers for consumers. The purpose of the study is two-fold. First, we seek to identify the generic structure of online book reviews. Second, we investigate the cross-cultural variation in the rhetorical organisation of opinions and evaluations in written reviews across language communities. The reviews are analysed in terms of the generic stages of book reviews, which are determined by their overall communicative goals (Motta-Roth, 1995; Taboada, 2011). The stages are then mapped against rhetorical relations that capture the coherence and meaningful organisation of the text (Mann and Thompson, 1988). Results show that online book reviews in all three languages share a common generic structure comprising three broad stages: Metapragmatic Comment, Evaluation (Book Overall, Author, Plot, Character) and Recommendation. While Evaluation is the only obligatory stage, Metapragmatic Comment serves to prepare the reader for the Evaluation that follows. The recommendation stage is common in both English and Chinese reviews but is conspicuously absent in their Japanese counterpart. In terms of rhetorical patterns, Contrast, Concession and Antithesis relations are preferred in Metapragmatic Comment and Evaluation, while Motivation is typically present in the recommendation stage. This paper proposes a methodology for the contrastive analyses of pragmatic phenomena, illustrating this methodology through the study of an emerging online genre.
Gathering Effective Information for Real-Time Material Recognition

Material recognition is a fundamental problem in the field of computer vision.Material recognition is still challenging because of varying camera perspectives, light conditions, and illuminations.Feature learning or feature engineering helps build an important foundation for effective material recognition.Most traditional and deep learning-based features usually point to the same or similar material semantics from diverse visual perspectives, indicating the implicit complementary information (or cross-modal semantics) among these heterogeneous features.However, only a few studies focus on mining the cross-modal semantics among heterogeneous image features, which can be used to boost the final recognition performance.To address this issue, we first improve the well-known multiset discriminant correlation analysis model to fully mine the cross-modal semantics among heterogeneous image features.Then, we propose a novel hierarchical multi-feature fusion (HMF 2 ) model to gather effective information and create novel yet more effective and robust features.Finally, a general classifier is employed to train a new material recognition model.Experimental results demonstrate the simplicity, effectiveness, robustness, and efficiency of the HMF 2 model on two benchmark datasets.Furthermore, based on the HMF 2 model, we design an end-to-end online system for real-time material recognition.
Document Preprocessing with TF-IDF to Improve the Polarity Classification Performance of Unstructured Sentiment Analysis

Sentiment analysis in terms of polarity classification is very important in everyday life, with the existence of polarity, many people can find out whether the respected document has positive or negative sentiment so that it can help in choosing and making decisions. Sentiment analysis usually done manually. Therefore, an automatic sentiment analysis classification process is needed. However, it is rare to find studies that discuss extraction features and which learning models are suitable for unstructured sentiment analysis types with the Amazon food review case. This research explores some extraction features such as Word Bags, TF-IDF, Word2Vector, as well as a combination of TF-IDF and Word2Vector with several machine learning models such as Random Forest, SVM, KNN and Naïve Bayes to find out a combination of feature extraction and learning models that can help add variety to the analysis of polarity sentiments. By assisting with document preparation such as html tags and punctuation and special characters, using snowball stemming, TF-IDF results obtained with SVM are suitable for obtaining a polarity classification in unstructured sentiment analysis for the case of Amazon food review with a performance result of 87,3 percent.
Iterative Residual Network for Image Dehazing

In this paper, we propose an Iterative Residual Network. By designing the calculation unit, we put the hazy image into the calculation unit to perform an iterative operation that can stitch the hazy image in stages with the unit output and substitute it into the calculation. After multiple iterations, a clean image can be generated. We introduce Long Short-Term Memory network and Residual ideas in the design process of the computing unit to further optimize the model. The Long Short-Term Memory network can be used to connect computing units at different stages. The use of residual block connection in the deep processing of the computing unit can preserve the original features of the image and prevent the model from overfitting. This model directly generates hazy-free images in an end-to-end manner and does not rely on the estimations of the transmission map and atmospheric light. Experiments show that Iterative Residual Network can effectively remove the haze in the image. In the test of the synthetic dataset and the real dataset, Iterative Residual Network is superior to the existing methods in terms of PSNR, SSIM, FADE and subjective visual effects.
The iLearning Journal Center: Education Startup to Enhance Lecturer Research

Research is one of the pillars of the College Catur Dharma that contributes to improving the academic atmosphere.Essential capital to enhance the nation's competitiveness is through increasing the ability of science and technology innovation that manifested in research.However, in its application in the era of the development of education 4.0, this is still not running optimally.To embody the vision of the 2014-2045 National Research Master Plan (RIRN), Indonesia, in 2045, which is competitive and sovereign based on research.This research aims to improve the nation's competitiveness through a digital platform iLearning Journal Center (iJC) with focus on online journal management as a step to strengthen research in Indonesia.This program is an original work of Indonesian children, which in the end applied to replace the conventional (printed) journal management system, as well as to provide convenience, speed, and integrity in the management of journals packaged in the digital platform iLearning Journal Center (iJC).The method used in this study is the business model of canvas, vasTmind, and TKT measurements.The result is concluded as an education startup application implemented to improve the academic community research.
Electronic health record data mining for artificial intelligence healthcare

Continued advancements in both data aggregation and computer science have brought an emergence of artificial intelligence (AI) that has changed the way many industries operate in the modern era. The aviation industry uses AI for autopiloting; online retail uses it to understand consumer interest; and the auto industry has made massive strides in AI to bring forth a new age of driverless cars. Despite the widespread use of AI to improve performance and efficiency in other industries, the adoption of AI into healthcare has remained relatively slow. However, with the increasing volume and scope of data collected in electronic health records (EHRs), healthcare has become ripe with opportunity to leverage AI to improve elements of the care delivery process. In this chapter, we will discuss current implementations of AI in EHRs, EHR-specific limitations to safeguard against, and a path forward for continued integration of AI and healthcare.
Multitask Learning for Arabic Offensive Language and Hate-Speech Detection

Offensive language and hate-speech are phenomena that spread with the rising popularity of social media. Detecting such content is crucial for understanding and predicting conflicts, understanding polarisation among communities and providing means and tools to filter or block inappropriate content. This paper describes the SMASH team submission to OSACT4’s shared task on hate-speech and offensive language detection, where we explore different approaches to perform these tasks. The experiments cover a variety of approaches that include deep learning, transfer learning and multitask learning. We also explore the utilisation of sentiment information to perform the previous task. Our best model is a multitask learning architecture, based on CNN-BiLSTM, that was trained to detect hate-speech and offensive language and predict sentiment.
An Improved Grasshopper Optimizer for Global Tasks

The grasshopper optimization algorithm (GOA) is a metaheuristic algorithm that mathematically models and simulates the behavior of the grasshopper swarm. Based on its flexible, adaptive search system, the innovative algorithm has an excellent potential to resolve optimization problems. This paper introduces an enhanced GOA, which overcomes the deficiencies in convergence speed and precision of the initial GOA. The improved algorithm is named MOLGOA, which combines various optimization strategies. Firstly, a probabilistic mutation mechanism is introduced into the basic GOA, which makes full use of the strong searchability of Cauchy mutation and the diversity of genetic mutation. Then, the effective factors of grasshopper swarm are strengthened by an orthogonal learning mechanism to improve the convergence speed of the algorithm. Moreover, the application of probability in this paper greatly balances the advantages of each strategy and improves the comprehensive ability of the original GOA. Note that several representative benchmark functions are used to evaluate and validate the proposed MOLGOA. Experimental results demonstrate the superiority of MOLGOA over other well-known methods both on the unconstrained problems and constrained engineering design problems.
Multilingual Probing of Deep Pre-Trained Contextual Encoders

Encoders that generate representations based on context have, in recent years, benefited from adaptations that allow for pre-training on large text corpora. Earlier work on evaluating fixed-length sentence representations has included the use of ‘probing’ tasks, that use diagnostic classifiers to attempt to quantify the extent to which these encoders capture specific linguistic phenomena. The principle of probing has also resulted in extended evaluations that include relatively newer word-level pre-trained encoders. We build on probing tasks established in the literature and comprehensively evaluate and analyse – from a typological perspective amongst others – multilingual variants of existing encoders on probing datasets constructed for 6 non-English languages. Specifically, we probe each layer of a multiple monolingual RNN-based ELMo models, the transformer-based BERT’s cased and uncased multilingual variants, and a variant of BERT that uses a cross-lingual modelling scheme (XLM).