Reviewerly/exHarmony huggingface dataset:
Learning to Generate Word- and Phrase-Embeddings for Efficient Phrase-Based Neural Machine Translation

Neural machine translation (NMT) often fails in one-to-many translation, e.g., in the translation of multi-word expressions, compounds, and collocations. To improve the translation of phrases, phrase-based NMT systems have been proposed; these typically combine word-based NMT with external phrase dictionaries or with phrase tables from phrase-based statistical MT systems. These solutions introduce a significant overhead of additional resources and computational costs. In this paper, we introduce a phrase-based NMT model built upon continuous-output NMT, in which the decoder generates embeddings of words or phrases. The model uses a fertility module, which guides the decoder to generate embeddings of sequences of varying lengths. We show that our model learns to translate phrases better, performing on par with state of the art phrase-based NMT. Since our model does not resort to softmax computation over a huge vocabulary of phrases, its training time is about 112x faster than the baseline.
Benchmark Dataset for Propaganda Detection in Czech Newspaper Texts

Propaganda of various pressure groups ranging from big economies to ideological blocks is often presented in a form of objective newspaper texts.However, the real objectivity is here shaded with the support of imbalanced views and distorted attitudes by means of various manipulative stylistic techniques.In the project of Manipulative Propaganda Techniques in the Age of Internet, a new resource for automatic analysis of stylistic mechanisms for influencing the readers' opinion is developed.In its current version, the resource consists of 7,494 newspaper articles from four selected Czech digital news servers annotated for the presence of specific manipulative techniques.In this paper, we present the current state of the annotations and describe the structure of the dataset in detail.We also offer an evaluation of bag-of-words classification algorithms for the annotated manipulative techniques.
Feature Selection for Facebook Feed Ranking System via a Group-Sparsity-Regularized Training Algorithm

In modern production platforms, large scale online learning models are applied to data of very high dimension. To save computational resource, it is important to have an efficient algorithm to select the most significant features from an enormous feature pool. In this paper, we propose a novel neural-network-suitable feature selection algorithm, which selects important features from the input layer during training. Instead of directly regularizing the training loss, we inject group-sparsity regularization into the (stochastic) training algorithm. In particular, we introduce a group sparsity norm into the proximally regularized stochastical gradient descent algorithm. To fully evaluate the practical performance, we apply our method to Facebook News Feed dataset, and achieve favorable performance compared with state-of-the-arts using traditional regularizers.
Optimization of Physical Activity Recognition for Real-Time Wearable Systems: Effect of Window Length, Sampling Frequency and Number of Features

The aim of this study was to develop an optimized physical activity classifier for real-time wearable systems with the focus on reducing the requirements on device power consumption and memory buffer. Classification parameters evaluated in this study were the sampling frequency of the acceleration signal, window length of the classification fragment, and the number of classification features, found with different feature selection methods. For parameter evaluation, a decision tree classifier was created based on the acceleration signals recorded during tests, where 25 healthy test subjects performed various physical activities. Overall average F1-score achieved in this study was about 0.90. Similar F1-scores were achieved with the evaluated window lengths of 5 s (0.92 ± 0.02) and 3 s (0.91 ± 0.02), while classification performance with 1 s were lower (0.87 ± 0.02). Tested sampling frequencies of 50 Hz, 25 Hz, and 13 Hz had similar results with most classified activity types, with an exception of outdoor cycling, where differences were significant. Using forward sequential feature selection enabled the decreasing of the number of features from initial 110 features to about 12 features without lowering the classification performance. The results of this study have been used for developing more efficient real-time physical activity classifiers.
Pattern Filtering Attention for Distant Supervised Relation Extraction via Online Clustering

Distant supervised relation extraction has been widely used to extract relational facts in large-scale corpus but inevitably suffers from the wrong label problem. Many methods use attention mechanisms to address this issue. However, the attention weights in these models are not discriminative and precise enough to fully filter out noise. In this paper, we propose a novel Pattern Filtering Attention (PFA), which can filter noise effectively. Firstly, we adopt an online clustering algorithm on the instances labeled with the same relation to extract potential semantic centers (positive patterns) of each relation, and these patterns have less noise statistically. Then, we build a sentence-level attention based on the similarities of instances and positive patterns. Due to the large differences between these similarities, our model can assign more discriminative weights to instances to reduce the influence of noisy data. Experimental results on the New York Times (NYT) dataset show that our model can effectively improve the performance of relation extraction compared with state-of-the-art methods.
An improved ACS algorithm for data clustering

&lt;span lang="EN-GB"&gt;Data clustering is a data mining technique that discovers hidden patterns by creating groups (clusters) of objects. Each object in every cluster exhibits sufficient similarity to its neighbourhood, whereas objects with insufficient similarity are found in other clusters. Data clustering techniques minimise intra-cluster similarity in each cluster and maximise inter-cluster dissimilarity amongst different clusters. Ant colony optimisation for clustering (ACOC) is a swarm algorithm inspired by the foraging behaviour of ants. This algorithm minimises deterministic imperfections in which clustering is considered an optimisation problem. However, ACOC suffers from high diversification in which the algorithm cannot search for best solutions in the local neighbourhood. To improve the ACOC, this study proposes a modified ACOC, called M-ACOC, which has a modification rate parameter that controls the convergence of the algorithm. Comparison of the performance of several common clustering algorithms using real-world datasets shows that the accuracy results of the proposed algorithm surpasses other algorithms. &lt;/span&gt;
APPLICATION OF ARTIFICIAL INTELLIGENCE FOR SUCCESSFUL STRATEGY IMPLEMENTATION IN INDIAS BANKING SECTOR

Banks have been already offering a wide variety of products and services, integrated with technology and automation, the most familiar being ATM machines all around us.Now moving to the next level in the present Industry 4.0 era, Banking sector is all set to amplify its strategy implementation by leveraging latest digital technologies so that its customers may experience swift and secure processing of transactions.This study examined the evolution, adoption, implementation and future opportunities of leveraging Artificial Intelligence (AI) for successful strategy implementation in India"s banking sector.In pursuance of this study, authors reviewed extensive literature, explored secondary data about the research work done on "Artificial Intelligence in Banking Sector" to find out the current status of research as well as practical application of AI in various aspects of banking strategies e.g.surveillance, fraud detection, ensuring compliances, credit assessment, customer service, handling monotonous voluminous tasks etc. in general and focusses on recent developments in context of India"s banking sector, urging banking leadership to shift their focus proactively towards leveraging AI with an objective to bring in delight for customers.Copy Right, IJAR, 2019.
Cyber Threat Discovery from Dark Web

In the darknet, hackers are constantly sharing information with each other and learning from each other. These conversations in online forums for example can contain data that may help assist in the discovery of cyber threat intelligence. Cyber Threat Intelligence (CTI) is information or knowledge about threats that can help prevent security breaches in cyberspace. In addition, monitoring and analysis of this data manually is challenging because forum posts and other data on the darknet are high in volume and unstructured. This paper uses descriptive analytics and predicative analytics using machine learning on forum posts dataset from darknet to discover valuable cyber threat intelligence. The IBM Watson Analytics and WEKA machine learning tool were used. Watson Analytics showed trends and relationships in the data. WEKA provided machine learning models to classify the type of exploits targeted by hackers from the form posts. The results showed that Crypter, Password cracker and RATs (Remote Administration Tools), buffer overflow exploit tools, and Keylogger system exploits tools were the most common in the darknet and that there are influential authors who are frequent in the forums. In addition, machine learning helps build classifiers for exploit types. The Random Forest classifier provided a higher accuracy than the Random Tree and Naïve Bayes classifiers. Therefore, analyzing darknet forum posts can provide actionable information as well as machine learning is effective in building classifiers for prediction of exploit types. Predicting exploit types as well as knowing patterns and trends on hackers’ plan helps defend the cyberspace proactively.
Fuzzy jump wavelet neural network based on rule induction for dynamic nonlinear system identification with real data applications

Aim Fuzzy wavelet neural network (FWNN) has proven to be a promising strategy in the identification of nonlinear systems. The network considers both global and local properties, deals with imprecision present in sensory data, leading to desired precisions. In this paper, we proposed a new FWNN model nominated “Fuzzy Jump Wavelet Neural Network” (FJWNN) for identifying dynamic nonlinear-linear systems, especially in practical applications. Methods The proposed FJWNN is a fuzzy neural network model of the Takagi-Sugeno-Kang type whose consequent part of fuzzy rules is a linear combination of input regressors and dominant wavelet neurons as a sub-jump wavelet neural network. Each fuzzy rule can locally model both linear and nonlinear properties of a system. The linear relationship between the inputs and the output is learned by neurons with linear activation functions, whereas the nonlinear relationship is locally modeled by wavelet neurons. Orthogonal least square (OLS) method and genetic algorithm (GA) are respectively used to purify the wavelets for each sub-JWNN. In this paper, fuzzy rule induction improves the structure of the proposed model leading to less fuzzy rules, inputs of each fuzzy rule and model parameters. The real-world gas furnace and the real electromyographic (EMG) signal modeling problem are employed in our study. In the same vein, piecewise single variable function approximation, nonlinear dynamic system modeling, and Mackey–Glass time series prediction, ratify this method superiority. The proposed FJWNN model is compared with the state-of-the-art models based on some performance indices such as RMSE, RRSE, Rel ERR%, and VAF%. Results The proposed FJWNN model yielded the following results: RRSE (mean±std) of 10e-5±6e-5 for piecewise single-variable function approximation, RMSE (mean±std) of 2.6–4±2.6e-4 for the first nonlinear dynamic system modelling, RRSE (mean±std) of 1.59e-3±0.42e-3 for Mackey–Glass time series prediction, RMSE of 0.3421 for gas furnace modelling and VAF% (mean±std) of 98.24±0.71 for the EMG modelling of all trial signals, indicating a significant enhancement over previous methods. Conclusions The FJWNN demonstrated promising accuracy and generalization while moderating network complexity. This improvement is due to applying main useful wavelets in combination with linear regressors and using fuzzy rule induction. Compared to the state-of-the-art models, the proposed FJWNN yielded better performance and, therefore, can be considered a novel tool for nonlinear system identification.
A Framework for Allocating Server Time to Spot and On-Demand Services in Cloud Computing

Cloud computing delivers value to users by facilitating their access to servers at any time period needed. An approach is to provide both on-demand and spot services on shared servers. The former allows users to access servers on demand at a fixed price and users occupy different time periods on servers. The latter allows users to bid for the remaining unoccupied time periods via dynamic pricing; however, without appropriate design, such time periods may be arbitrarily short since on-demand users arrive randomly. This is also the current service model adopted by Amazon Elastic Cloud Compute. In this article, we provide the first integral framework for sharing time on servers between on-demand and spot services while optimally pricing spot service. It guarantees that on-demand users can get served quickly while spot users can stably use servers for a properly long period once accepted, which is a key feature in making both on-demand and spot services accessible. Simulation results show that, by complementing the on-demand market with a spot market, a cloud provider can improve revenue by up to 461.5%. The framework is designed under assumptions that are met in real environments. It is a new tool that other cloud operators can use to quantify the advantage of a hybrid spot and on-demand service, making the case for eventually integrating this service model into their own infrastructures.
Evaluating the web‐application resiliency to business‐layer DoS attacks

A denial-of-service (DoS) attack is a serious attack that targets web applications. According to Imperva, DoS attacks in the application layer comprise 60% of all the DoS attacks. Nowadays, attacks have grown into application- and business-layer attacks, and vulnerability-analysis tools are unable to detect business-layer vulnerabilities (logic-related vulnerabilities). This paper presents the business-layer dynamic application security tester (BLDAST) as a dynamic, black-box vulnerability-analysis approach to identify the business-logic vulnerabilities of a web application against DoS attacks. BLDAST evaluates the resiliency of web applications by detecting vulnerable business processes. The evaluation of six widely used web applications shows that BLDAST can detect the vulnerabilities with 100% accuracy. BLDAST detected 30 vulnerabilities in the selected web applications; more than half of the detected vulnerabilities were new and unknown. Furthermore, the precision of BLDAST for detecting the business processes is shown to be 94%, while the generated user navigation graph is improved by 62.8% because of the detection of similar web pages.
Research on Parallelization of Microblog Emotional Analysis Algorithms Using Deep Learning and Attention Model Based on Spark Platform

Emotional analysis of microblog can discover that the public's attitude towards hot events can grasp the network public opinion, so it has become a hot research topic in text mining. In view of the current situation that most of the existing affective analysis methods separate the deep learning model from the emotional symbols, this paper proposes a microblog affective analysis method based on dual attention model. This method uses Word2Vec tool to express microblog platform and build emotional dictionary; uses deep belief network and attention model to construct microblog emotional classification model; and trains the emotional classification model based on the constructed semantic representation. By combining attention model with emotional symbols, this method effectively enhances the ability of capturing emotional semantics of microblog text and improves the performance of microblog emotional classification. Based on Spark cluster, the deep confidence neural network is processed in parallel. The experimental results show that the model achieves the best results in three indicators. The recall and precision of the model are more than 0.04 higher than the traditional shallow learning method.
A Path Planning Method of Robot Arm Obstacle Avoidance Based on Dynamic Recursive Ant Colony Algorithm

In order to improve the efficiency and accuracy of obstacle avoidance path planning, the characteristics of obstacle avoidance path planning are analyzed. Based on the traditional ant colony algorithm, sliding window and forgetting factor are introduced. By adjusting the correlation parameters and pheromone rules, the fixed value ant colony parameters cannot meet the performance of the whole calculation process. Through the cooperation of ants, an optimal path was established to avoid obstacles. An optimal moving path of manipulator based on dynamic recursive ant colony algorithm was proposed, and the practicability and validity of the method were verified by an example. It provides a reference for finding the optimal solution to the manipulator path planning in the shortest time.
i-Eclat: performance enhancement of eclat via incremental approach in frequent itemset mining

One example of the state-of-the-art vertical rule mining technique is called equivalence class transformation (Eclat) algorithm. Neither horizontal nor vertical data format, both are still suffering from the huge memory consumption. In response to the promising results of mining in a higher volume of data from a vertical format, and taking consideration of dynamic transaction of data in a database, the research proposes a performance enhancement of Eclat algorithm that relies on incremental approach called an Incremental-Eclat (i-Eclat) algorithm. Motivated from the fast intersection in Eclat, this algorithm of performance enhancement adopts via my structured query language (MySQL) database management system (DBMS) as its platform. It serves as the association rule mining database engine in testing benchmark frequent itemset mining (FIMI) datasets from online repository. The MySQL DBMS is chosen in order to reduce the preprocessing stages of datasets. The experimental results indicate that the proposed algorithm outperforms the traditional Eclat with 17% both in chess and T10I4D100K, 69% in mushroom, 5% and 8% in pumsb_star and retail datasets. Thus, among five (5) dense and sparse datasets, the average performance of i-Eclat is concluded to be 23% better than Eclat.
Developing a digital application (EVALOE-DSS) for the professional development of teachers aiming to improve their students’ linguistic competence

This paper reports the development and validation of a digital application (Assessment Scale of Oral Language at School - Decision Support System, EVALOE-DSS) for the professional development of teachers aiming to improve their students’ linguistic competence. We performed a multiple case study involving seven teachers and their students from seven different schools. Over a six–month period, the teachers used the digital application to self-assess their classes, make decisions and introduce changes into their teaching practices. The researchers observed four class sessions of each teacher and assessed them with the same digital application. Each teacher-researcher dyad discussed the tool and its use over three meetings during the semester. Despite some differences between the teachers, use of the digital application produced progress in the competences of both the teachers and the students. Thus, the digital application can be used for professional development in teachers.
Review Sharing via Deep Semi-Supervised Code Clone Detection

Code review as a typical type of user feedback has recently drawn increasing attentions for improving code quality. To carry out research on code review, sufficient review data is normally required. As a result, recent efforts commonly focus on analysis for projects with sufficient reviews (called “s-projects”), rather than projects with extremely few ones (called “f-projects”). Actually, through statistics on public platforms, the latter ones dominate open source software, in which novel approaches should be explored to improve their review-based code improvement. In this paper, we try to address the problem via building a review sharing channel where the informative review can be reasonably delivered from s-projects to the f-projects. To ensure the accuracy of shared reviews, we introduce a novel code clone detection model based on Convolutional Neural Network (CNN), and build suitable “s-projects, f-projects” pairs through the clone detection. Especially, to alleviate the dataset heterogeneity between the training and testing, an autoencoder-based semi-supervised learning strategy is employed. Furthermore, to improve the sharing experience, heuristic filtering tactics are applied to reduce the time cost. Meanwhile, the LDA (Latent Dirichlet Allocation)-based ranking algorithm is used for presenting diverse review themes. We have implemented the sharing channel as a prototype system RSharer+, which contains three representative modules: data preprocessing, code clone detection and review presentation. The collected datasets are first transformed into context-sensitive numerical vectors in the data proprecessing. Then in the clone detection, data vectors are trained and tested on the BigCloneBench and real code-review pairs. At last, the presentation module provides review classification and theme extraction for better sharing experience. Extensive comparative experiments on hundreds of real labelled code fragments demonstrate the precision of clone detection and the effectiveness of review sharing.
Web service QoS prediction using improved software source code metrics

Due to the popularity of Web-based applications, various developers have provided an abundance of Web services with similar functionality. Such similarity makes it challenging for users to discover, select, and recommend appropriate Web services for the service-oriented systems. Quality of Service (QoS) has become a vital criterion for service discovery, selection, and recommendation. Unfortunately, service registries cannot ensure the validity of the available quality values of the Web services provided online. Consequently, predicting the Web services’ QoS values has become a vital way to find the most appropriate services. In this paper, we propose a novel methodology for predicting Web service QoS using source code metrics. The core component is aggregating software metrics using inequality distribution from micro level of individual class to the macro level of the entire Web service. We used correlation between QoS and software metrics to train the learning machine. We validate and evaluate our approach using three sets of software quality metrics. Our results show that the proposed methodology can help improve the efficiency for the prediction of QoS properties using its source code metrics.