Reviewerly/exHarmony huggingface dataset:
Self-Attention for Deep Reinforcement Learning

Reinforcement learning is concerned with how software agents ought to take actions according to the state of the environment so as to maximize some notion of cumulative reward. Therefore, in-depth study and mining of the state of the environment will be more conducive to the agent to make better decisions. Motivated by the advantages of self-attention mechanism in machine translation, this paper presents a new scheme. In this scheme, the state in deep reinforcement learning algorithms can be combined with self-attention mechanism. After that agents will pay more attention to the internal structure of state especially in a complex game environment, like real-time strategy game StarCraft. StarCraft is a huge challenge platform for AI researchers because of its huge state spaces and action spaces. Some baseline agents of reinforcement learning provided by DeepMind for mini-games in StarCraft II have not reached the level of an amateur player. Our agents use fewer features than DeepMind's baseline agents and have made significant improvement.
Towards Recognition of Textual Entailment in the Biomedical Domain

The medical literature suffers from disagreements among authors discussing the same topic or treatment. With thousands of articles published daily, there is a need to detect inconsistent and often contradictory findings. Natural language inference (NLI) gained a lot of interest in the past years, however, domain-specific NLI systems are yet to be examined in depth. In this paper, we conduct several experiments on sentence pairs extracted from PubMed abstracts, to infer whether they express entailment, contradiction or neutral meanings. The main focus of this research is to recognize textual entailment in published evidence-based medicine findings. We explore popular NLI models and sentence embeddings, adapted to the biomedical domain. We further investigate improving the inference detection abilities of the models by incorporating traditional machine learning (ML) features with deep learning (DL) architecture. The proposed model serves in capturing biomedical language's representations by combining lexical, contextual and compositional semantics.
Bidirectional LSTM-CNNs with Extended Features for Named Entity Recognition

Named Entity Recognition (NER) is vital preprocessing step for many Natural Language Processing applications such as relation extraction and question answering. NER has been studied for decades. While, much of the earlier studies for NER have focused on using powerful features and knowledge resources, recent studies using Deep Learning techniques have not needed to use these powerful features and knowledge resources. Instead, approach of these studies is learning powerful features from the data by itself. In this paper, we extend a bidirectional LSTM-CNN model by adding syntactic and word-level features. Evaluation shows that adding syntactic and semantic information about words without feature engineering to the model surpasses the baseline model tested on CoNLL-2003 English NER dataset.
Evaluation of Image Features Within and Surrounding Lesion Region for Risk Stratification in Breast Ultrasound Images

Feature extraction and classification plays a crucial role in the automated analysis of breast ultrasound (BUS) images. Due to varying sonographic characteristics of benign and malignant lesions, the texture and shape features are mostly employed for designing computer-aided diagnosis (CAD) systems of BUS images. The existing CAD systems use features that are extracted either from the lesion segmented area obtained through segmentation techniques or a rectangular region of interest (ROI) extracted under the guidance of expert Radiologists. However, the significance of features extracted from region comprising only the lesion area is still little explored. This paper investigates the significance of features extracted from the lesion area, lesion surrounding area and rectangular ROI for classification of BUS images. The experiments were conducted on the database of 294 BUS images (104 benign and 190 malignant). Initially, the acquired BUS images were preprocessed through speckle reducing anisotropic diffusion (SRAD) for speckle noise removal. The preprocessed images are segmented using a hybrid segmentation approach including a combination of region-based active contour driven by region-scalable fitting (RBACM-RSF) model and multi-scale Gaussian kernel fuzzy c-means clustering with spatial bias correction (MsGKFCM_S) for getting ROI confined area. The segmented images were further partitioned into two parts (lesion area and lesion surrounding area). Subsequently, a total of 457 texture and shape attributes were extracted from within the lesion area, lesion surrounding area and rectangular ROI comprising of both lesion and its surrounding area. The significance of these features is evaluated using different classifiers (i.e. support vector machine (SVM), Back-propagation artificial neural network (BPANN), Random Forest, AdaBoost). The results indicate that features extracted from within lesion area achieve a maximum classification accuracy of 98.980% with the lowest computational time when linear kernel-based SVM is used.
A Distributed Algorithm That Finds Almost Best Possible Estimate Under Non-Vanishing and Time-Varying Measurement Noise

In this letter, we review an existing distributed least-squares solver and share some new insights on it. Then, by the observation that an estimation of a constant vector under output noise can be translated into finding the least-squares solution, we present an algorithm for distributed estimation of the state of linear time-invariant systems under measurement noise. The proposed algorithm consists of a network of local observers, where each of them utilizes local measurements and information transmitted from the neighbors. It is proven that even under non-vanishing and time-varying measurement noise, we could obtain an almost best possible estimate with arbitrary precision. Some discussions regarding the plug-and-play operation are also given.
SoundUAV

Delivery drones may become potential targets for package theft. An adversary may launch a drone impersonation attack, where the adversary's drone purports to be a legitimate delivery drone. To protect against such attacks, authenticating drones is crucial. Existing authentication schemes based on digital certificates have been shown to be compromised by security breaches on certificate authorities. Thus, we propose SoundUAV as a second factor of authentication for drones that leverages uniqueness in acoustic emanations to fingerprint drones, even within the same make and model. This uniqueness is attributed to hardware defects in motors, making SoundUAV secure against impersonation and robust to large scale attacks. Further, SoundUAV requires no hardware modifications to drones as it utilizes the pervasive acoustic emanations. We perform preliminary evaluation on eleven drones and obtain a fingerprinting accuracy of 99.48%.
Learning Agile Scrum Methodology Using the Groupware Tool Trello $$^{\textregistered }$$ Through Collaborative Working

Agile is a project management philosophy for collaborative working which consists of a set of values and principles that can be employed in any sector. It is adopted by the software development community a long time ago and now several methodologies based on agile principles have become established as a method of software development. These agile based software development methodologies have developed as an integral part of the software engineering and software development curricula for many computing degree courses. One such agile based methodology is Scrum which is widely used in the software industry and thus in teaching. Several agile Scrum tools are available for software development, however, for teaching and learning purposes, this would not be affordable for many institutions due to its cost or learning curve. This necessitates the requirement for a free or open-source Scrum tool without any learning curve. Trello which is a free project management and collaborative working tool but not particularly designed for Scrum. However, its functionality and features can be transformed to make it a basic Scrum tool for teaching and learning purposes at no cost. This paper presents a systematic development and application of Trello-based agile Scrum methodology not just for teaching and learning purposes but for real project development. It is employed in the delivery of the software engineering module in BSc courses and subsequently, in the development of the BSc final year project at Bournemouth University, UK. This implementation of Trello-based agile Scrum methodology in the project development is compared against the simple agile Scrum based project development practice to demonstrate the success and learning improvement of this proposed methodology.
Design Of DSQLM Language Extensions

In this paper we present our continuous work on developing query language for metallurgical industry. We cover design of DSLQM language extensions for arithmetic calculations, discus features important for end users especially localization and internationalization of programming languages and provide some advises on choosing optimal programming paradigm.
Interaction Templates for Multi-Robot Systems

This letter describes a framework for multi-robot problems that require or utilize interactions between robots. Solutions consider interactions on a motion planning level to determine the feasibility and cost of the multi-robot team solution. Modeling these problems with current integrated task and motion planning (TMP) approaches typically requires reasoning about the possible interactions and checking many of the possible robot combinations when searching for a solution. We present a multi-robot planning method called Interaction Templates (ITs), which moves certain types of robot interactions from the task planner to the motion planner. ITs model interactions between a set of robots with a small roadmap. This roadmap is then tiled into the environment and connected to the robots’ individual roadmaps. The resulting combined roadmap allows interactions to be considered by the motion planner. We apply ITs to homogeneous and heterogeneous robot teams under both required and optional cooperation scenarios, which previously required a task planning method. We show improved performance over a current TMP planning approach.
Hydrological time series forecast by ARIMA+PSO-RBF combined model based on wavelet transform

Aiming at the nonlinear and time-varying complexity of hydrological time series, a hydrological time series pretreatment algorithm based on wavelet transform is designed. By analyzing laws of flow variation, non-stationary characteristics and the mechanism of ARIMA model and RBF model, we know that the ARIMA model is suitable for linear time series forecast, neural network is suitable for dealing with nonlinear problems, so we combine these two models to build the ARIMA-RBF forecast model and propose a particle swarm optimization algorithm to optimize the RBF neural network to improve the forecast accuracy and convergence rate. Finally, the forecast of hydrological time series is realized. Experiments show that the combined model with proper wavelet decomposition function and combined model parameters can significantly improve the forecast accuracy of water level compared with the traditional RBF neural network. The combined model provides a useful reference for the practical hydrological forecast.
Domino algorithm: a novel constructive heuristics for traveling salesman problem

Abstract Developing algorithms for solving complex optimisation problems has become a challenging topic recently. This study has applied a novel constructive heuristics algorithm named Domino Algorithm for the Traveling Salesman Problem (TSP) case which is aimed to efficiently reduce the calculation complexity and to find the optimal results of TSP best solution of tour lengths. As the study is a basic version of Domino Algorithm, it is decided to use the small TSP data sets consisting of 100 cities or less, such as Eil51, Berlin52, St70, Eil76, Pr76, and Rat99. This study also applied Nearest Neighbor approach to compare its result with that of Domino Algorithm. The results have shown that better tour lengths were achieved by Domino Algorithm (using 1 or 2 player (s)) for Eil51, Eil76, St70, Rat 99; and were resulted by Nearest Neighbor for Berlin52, and Pr76. For further research, the algorithm should be intensively combined with applying the improvement heuristic and should also be hybridized with meta-heuristics algorithm focusing on finding the optimal results by which the application will be moreover quite simple and easy.
Machine Learning Techniques for SIM Box Fraud Detection

In today's competitive environment, telecommunication operators and service providers need to generate revenue by designing and delivering innovative services to the subscribers. At the same time, a prime consideration is to minimize the cost and prevent the revenue leakages. In this context, the industry faces numerous challenges and different types of frauds. There is a continuous effort to tackle this problem by improving the implementation methodology and the network protocols. However, in general, fraud detection is difficult and currently addressed in a proactive manner. Fraudulent callers exploit the weaknesses of specific protocol level solutions and avoid detection of the gray traffic. Our research work is inspired by classification algorithms used in machine learning and employed in different fields of science and engineering e.g. images processing, speech recognition, spam email detection etc.. We have applied these machine learning techniques (MLTs) for the classification of normal and fraudulent subscriber (SIM Box). We have used the call detail records (CDRs) of normal and fraudulent subscriber as an input to identify the important attributes; 25 for each customers. These attribute are used for classification of the normal and fraudulent subscribers using Neural Network (NN) and Support Vector Machine (SVM). A comparative performance analysis of both techniques is also presented using various evaluation parameters. SVM using the kernel (Polynomial, Radial, and Sigmoid) show best performance with an accuracy of 99.24%. SVM Linear kernel show the worst performance with accuracy of 95.18 % and 0.19 regression. In case of NN, Bayesian Regularization and Resilient Back-Propagation algorithms show best and worst performance with an accuracy of 99.87 % and 99.53% respectively.
A Study on Various Techniques Involved in Gender Prediction System: A Comprehensive Review

Abstract Predicting gender on the foundation of handwriting investigation is a very invoking research area. Handwriting analysis also has numerous applications. It is useful for forensic experts to investigate classes of writers. This prediction is executed fundamentally by two steps: feature extraction and classification. As a whole, prediction solely depends upon the feature extraction . Distinct and most varied features make the classification accurate. This paper describes the problem comprehensively along with its foundation. It presents a survey of the available methods to solve the discussed problem. A comparative analysis of the discussed methods of feature extraction and classification along with the available databases is also presented in this paper.
Spell Checker for Punjabi Language Using Deep Neural Network

Spell Checker is an important part of language specific text processing applications. In this paper, we propose a novel hybrid approach for spell checker for Punjabi language. Available spell checkers use traditional method for error detection such as dictionary lookup technique and minimum edit distance for error correction. The proposed spell checker aims to improve performance as well accuracy. In this paper, we present the use of trie data structure to store Punjabi words dictionary and then use tree based algorithm along with n-gram analysis to detect misspelled words. To correct misspelled words, best possible suggestion is listed using Long-short term memory (LSTM) recurrent neural network along with rule based approach and minimum edit distance. Error detection using trie-based dictionary improves the performance and Error correction using LSTM improves the accuracy of proposed spell checker. In addition to error detection techniques and error correction techniques, proposed spell checker uses handcrafted rules, language syntax rules and rules regarding tokenization.
The return of software vulnerabilities in the Brazilian voting machine

This paper presents a detailed and up-to-date security analysis of the voting software used in Brazilian elections based on results obtained by the authors in a recent hacking challenge organized by the national electoral authority. During the event, multiple serious vulnerabilities were detected in the voting software, which when combined compromised the main security properties of the equipment, namely ballot secrecy and software integrity. The insecure storage of cryptographic keys, hard-coded directly in source code and shared among all machines, allowed full content inspection of the software installation memory cards, after which two shared libraries missing authentication signatures were detected. Injecting code in the libraries allowed the execution of arbitrary code in the machine, violating the integrity of the voting software. Our progress is richly described, to illustrate difficulties and limitations in the testing methodology chosen by the electoral authority, and to inform how teams participating in future challenges can optimize their performance. We trace the history of the vulnerabilities to a previous security analysis, providing some perspective about how the system evolved in the past 6 years. As far as we know, this was the most in-depth compromise of an official large-scale voting system ever performed under such severely restricted conditions.
Evaluating the Transfer of Scaffolded Inquiry: What Sticks and Does It Last?

The Next Generation Science Standards [1] expect students to master disciplinary core ideas, crosscutting concepts, and scientific practice. In prior work, we showed that students benefited from real time scaffolding of science practices such that students' inquiry competencies both improved over time and transferred to new science topics. The present study examines the robustness of adaptive scaffolding by evaluating students' inquiry performances at a very fine-grained level in order to investigate what aspects of inquiry are robust over time once scaffolding was removed. 108 middle school students in grade 6 used Inq-ITS and received adaptive scaffolding for three lab activities in the first inquiry topic they completed (i.e. Animal Cell); they then completed 10 activities without scaffolding across three new topics. Results showed that after removing scaffolding, student's inquiry performance generally improved with slight variations in performance across driving questions and over time. Overall, these findings suggest that adaptive scaffolding may support students' inquiry learning and transfer of inquiry practices over time and across topics.
Presidential Elections in Ecuador: Bot Presence in Twitter

The world's digital transformation has influenced not only the way we do business, but also the way we perform daily activities. Social media has a clear influence on people, especially during events such as elections where recent international events have shown that social media played an important role. In fact, there are three main aspects in this digital landscape: political movements and political candidates looking for popular support (number of followers), citizens' messages discussing social issues (trending topics on social media), and political propaganda appearing in the net in favor of or against politicians or political movements (advertisement). One of the issues with social media is the presence of automatic accounts (bots) that artificially fill accounts with fake followers, create false trending topics, and share fake news or simply flood the net with propaganda. All this artificially generated information may influence people and sometimes may even censor people's real opinions undermining their freedom of speech and affecting true democracy. In this paper, we present a set of observations based on two main objectives: collect evidence of digital campaigning during the Presidential Elections in Ecuador 2017 and describe the mechanisms used for this campaign. Finally, we show how candidates prepared online campaigns in Twitter and how normal patterns of new followers' subscriptions were altered. And, we collected information about more than 30,000 bots filtered only by political content used for propaganda purposes.
Compressing Audio Signals with Inpainting-Based Sparsification

Inpainting techniques are becoming increasingly important for lossy image compression. In this paper, we investigate if successful ideas from inpainting-based codecs for images can be transferred to lossy audio compression. To this end, we propose a framework that creates a sparse representation of the audio signal directly in the sample-domain. We select samples with a greedy sparsification approach and store this optimised data with entropy coding. Decoding restores the missing samples with well-known 1-D interpolation techniques. Our evaluation on music pieces in a stereo format suggests that the lossy compression of our proof-of-concept framework is quantitatively competitive to transform-based audio codecs such as mp3, AAC, and Vorbis.
Learning to Generate Questions with Adaptive Copying Neural Networks

Automatic question generation is an important problem in natural language processing. In this paper, we propose a novel adaptive copying recurrent neural network model to tackle the problem of question generation from sentences and paragraphs. The proposed model adds a copying mechanism component onto a bidirectional LSTM architecture to generate more suitable questions adaptively from the input data. Our experimental results show the proposed model can outperform the state-of-the-art question generation methods in terms of BLEU and ROUGE evaluation scores.
Preventing Data Leak through Semantic Analysis

The theft and exfiltration of sensitive data (e.g. state secrets, trade secrets, company records, etc.) represent one of the most damaging threats that can be carried by malicious insiders against institutions and organizations. In the last decade, data leak prevention (DLP) has emerged as a new mechanism to detect and block unauthorized data transfer from the organization perimeter. While DLP has gained traction and led to several commercial products in industry, there are still many unresolved challenges hampering its operation and full adoption. Existing DLP systems exhibit relatively limited accuracy and can be circumvented using various evasive tactics. In this paper, we present a new DLP model that tracks sensitive data using a summarized version of the content semantic called document semantic signature (DSS). The DSS can be updated dynamically as the protected content change and it is resilient against evasion tactics, such as content rewriting. The evaluation of the DSS model on a public dataset achieved very encouraging results in terms of detection effectiveness.