Reviewerly/exHarmony huggingface dataset:
Deep Reinforcement Learning for Pellet Eating in Agar.io

The online game Agar.io has become massively popular on the internet due to its intuitive game design and its ability to instantly match players with others around the world.The game has a continuous input and action space and allows diverse agents with complex strategies to compete against each other.In this paper we focus on the pellet eating task in the game, in which an agent has to learn to optimize its navigation strategy to grow maximally in size within a specific time period.This work first investigates how different state representations affect the learning process of a Q-learning algorithm combined with artificial neural networks which are used for representing the Q-function.The representations examined range from raw pixel values to extracted handcrafted feature vision grids.Secondly, the effects of using different resolutions for the representations are examined.Finally, we compare the performance of different value function network architectures.The architectures examined are two convolutional Deep Q-networks (DQN) of varying depth and one multilayer perceptron.The results show that the use of handcrafted feature vision grids significantly outperforms the direct use of raw pixel input.Furthermore, lower resolutions of 42 × 42 lead to better performances than larger resolutions of 84 × 84.
Video block error recovery using modified patch-based inpainting

This paper presents a patch-based inpainting algorithm for image block recovery in block-based coding image transmission. The algorithm is based on a geometric model for patch synthesis. The lost pixels are recovered by copying pixel values from the source using a similarity criterion. We used a trained neural network to choose the “best similar” patch. Experimental results show that the proposed method outperforms widely used state-of-the-art methods in both subjective and objective measurements of image block recovery.
Real-time Hand Pose Tracking and Classification for Natural Human-Robot Control

We present a human-robot natural interaction approach based on teleoperation through body gestures. More specifically, we propose an interface where the user can use his hand to intuitively control the position and status (open/closed) of a robotic arm gripper. In this work, we employ a 6-DOF (six degrees-of-freedom) industrial manipulator which mimics user movements in real-time, positioning the end effector as if the individual was looking into a mirror, entailing a natural and intuitive interface. The controlling hand of the user is tracked using body skeletons acquired from a Microsoft Kinect sensor, while a Convolutional Neural Network recognizes whether the hand is opened or closed using depth data. The network was trained on hand images collected from several individuals, in different orientations, resulting in a robust classifier that performs well regardless of user location or orientation. There is no need for wearable devices, such as gloves or wristbands. We present results of experiments that reveal high performance of the proposed approach to recognize both the user hand position and its status (open/closed); and experiments to demonstrate the robustness and applicability of the proposed approach to industrial tasks.
Recurrently exploiting co-saliency of target for part-based visual tracking

Visual tracking in condition of occlusion has been a challenging task over years. Recently, part-based algorithms have made great progress in handling occlusion. However, the existing part-based methods neglect different importance between central parts and marginal parts. Besides, scale variation remains a difficulty for part-based tracking. In this paper, we propose a novel part-based tracker to solve the above problems. Specifically, we introduce a visual attention mechanism recurrently exploiting co-saliency of target to guide the sampling of parts, which aims to highlight the importance of salient parts and guarantee the semantic integrity so as to improve the robustness handling occlusion. Considering the drift of prediction caused by mutual influence of parts, we implement the non-maximum suppression operation to reduce the high overlaps between parts, and introduce an effective correlation filter as base tracker. To balance the global distribution and local partiality of parts, appropriate update strategy including scale estimation method inspired by particle filters and correlation filters, Hough-voting scheme for target’s center prediction, and principles of part resampling are also fused into the algorithm. The experimental results on VOT 2017 and OTB-50 benchmarks showed that the proposed method is in comparison to the state-of-the-art trackers and good at dealing with occlusion situations particularly.
Complexity Analysis of DE based CE-UD MIMO System

Today’s world is demanding high data volume communication without any major interruption in the channel. To fulfill the user’s requirements Multiple Input Multiple Output (MIMO) technology seems promising which enhance the capacity of the channel. A channel is a medium through which signal travels. MIMO sends data on multiple paths using multiple antennas. At up-link receiver end of the MIMO system, to restore data special algorithms are used. Space and Time complexity of those algorithms play an important role in these systems to increase the performance. Time Complexity plays an important role when both variations give the same Bit Error Rate (BER). This paper will discuss the Time and Space complexity of Differential Evolutionary (DE) based CE-UD MIMO system with Channel Estimation and User Detection. These analyses are verified mathematically as well as on simulations using MatLab 2017.
A Neighborhood-Assisted Framework for Differential Evolution

Differential evolution (DE), as a powerful and efficient evolutionary algorithm (EA), has shown its advantages in solving the complex optimization problems. In the literature, the utilization of neighborhood information has been attracting wide attention in the DE community due to its effectiveness in enhancing the search ability of DE. However, we have observed that no general framework is presented to provide a comprehensive way of studying the neighborhood-based DE variants. Therefore, this paper suggests a three-layer mechanism neighborhood-assisted (TLNA) DE framework to facilitate the utilization of neighborhood information. In TLNA, the mechanisms of using neighborhood information are generalized into a three-layer cooperative structure, i.e., the interaction mechanism (IM) layer, the organization mechanism (OM) layer, and utilization mechanism (UM) layer. Thus, TLNA is built to provide a synergistic effect of different layers of mechanisms for systematically utilizing neighborhood information. As a general framework, TLNA can be realized with different implementations of the three-layer mechanism. Furthermore, to demonstrate the practicality of the proposed framework, a TLNA instantiation (iTLNA) is given in detail. The performance of iTLNA is extensively evaluated on a suite of benchmark functions. The experimental results have confirmed the competitiveness of iTLNA to other DE variants and EAs, which shows that the proposed TLNA framework can pave an effective way to improve the performance of DE with neighborhood information.
An Analysis of Measurement and Metrics Tools: A Systematic Literature Review

Measurement is an important field in Software Engineering, since it allows for organizations to obtain trustworthy estimates regarding deadlines, cost, and quality for the development of their software projects.Many tools are available for the calculation and storage of metrics and therefore, choosing the best tool can be a hard task.Faced with such a problem, this study carries out an analysis of the measurement tools presented in literature.The methodology chosen for the task was the systematic literature review.The results of the systematic review present the metric tools chosen in literature, their functionalities, and the main metrics used by these tools.The primary contribution of this article is a list with the metrics used by each of these tools, and their respective classification, according to their use in academia as well as in the software industry.
The Impact of Internal and External Factors on Sustainable Procurement: A Case Study of Oil Companies

The contentious and growing sustainability concerns within the UAE's oil and gas industries have highlighted the necessity of systematically examining the internal and external organisational factors that influence sustainable procurement (SP) in the industry. This study investigates the internal and external factors most influencing SP in the UAE's oil and gas industry. Utilising questionnaires, the researcher obtained data from 100 procurement professionals working in three oil and gas companies operating in the UAE. The research framework, developed through an extensive literature review, was analysed and tested using structural equation modelling (SEM). The results reveal that both internal and external factors have a positive and statistically significant impact on sustainable procurement, although internal factors have a relatively greater effect. This study found that leadership in change management, internal management structure, management strategy, and infrastructure cost management, are the most influential internal factors affecting SP, whilst social and economic factors are the most influential external factors. An emphasis on these internal and external factors can help oil and gas companies operating in the UAE greatly improve their level of SP performance.
Incorporating External Knowledge to Boost Machine Comprehension Based Question Answering

We propose an effective knowledge representation network via a two-level attention mechanism, called KRN, to represent the background knowledge of entities in documents for boosting machine comprehension (MC). In experiments, we incorporated the KRN into several state-of-the-art MC models such as AS Reader, CAS Reader, GA Reader and BiDAF, and evaluated the performance of KRN using two datasets: WebQA and Quasar-T. Experimental results show that our KRN can improve the performance of the existing MC models.
Automatic Generation of a Sub-optimal Agent Population with Learning

Most modern solutions for video game balancing are directed towards specific games. We are currently researching general methods for automatic multiplayer game balancing. The problem is modeled as a meta-game, where game-play change the rules from another game. This way, a Machine Learning agent that learns to play a meta-game, learns how to change a base game following some balancing metric. But an issue resides in the generation of high volume of game-play training data, was agents of different skill compete against each other. For this end we propose the automatic generation of a population of surrogate agents by learning sampling. In Reinforcement Learning an agent learns in a trial error fashion where it improves gradually its policy, the mapping from world state to action to perform. This means that in each successful evolutionary step an agent follows a sub-optimal strategy, or eventually the optimal strategy. We store the agent policy at the end of each training episode. The process is evaluated in simple environments with distinct properties. Quality of the generated population is evaluated by the diversity of the difficulty the agents have in solving their tasks.
A Response to Pervez Rizvi’s Critique of the Word Adjacency Method for Authorship Attribution

Pervez Rizvi recently gave a critique of the Word Adjacency Network method for authorship attribution developed by the present authors. The two publications of ours that Rizvi explicitly addresses ...
Robust Argument Unit Recognition and Classification.

Argument mining is generally performed on the sentence-level -- it is assumed that an entire sentence (not parts of it) corresponds to an argument. In this paper, we introduce the new task of Argument unit Recognition and Classification (ARC). In ARC, an argument is generally a part of a sentence -- a more realistic assumption since several different arguments can occur in one sentence and longer sentences often contain a mix of argumentative and non-argumentative parts. Recognizing and classifying the spans that correspond to arguments makes ARC harder than previously defined argument mining tasks. We release ARC-8, a new benchmark for evaluating the ARC task. We show that token-level annotations for argument units can be gathered using scalable methods. ARC-8 contains 25\% more arguments than a dataset annotated on the sentence-level would. We cast ARC as a sequence labeling task, develop a number of methods for ARC sequence tagging and establish the state of the art for ARC-8. A focus of our work is robustness: both robustness against errors in sentence identification (which are frequent for noisy text) and robustness against divergence in training and test data.
Efficient State Management for Scaling Out Stateful Operators in Stream Processing Systems

Many big data applications require real-time analysis of continuous data streams. Stream Processing Systems (SPSs) are designed to act on real-time streaming data using continuous queries consisting of interconnected operators. The dynamic nature of data streams, for example, fluctuation in data arrival rates and uneven data distribution, can cause an operator to be a bottleneck one. Scalability is an important factor in SPS, but detecting bottleneck operator correctly and scaling it without affecting application execution are challenging. A stateful operator such as aggregation or join makes scaling operation more difficult as it involves state management. Current research does not address the issue of scaling stateful operators efficiently as mostly stop application for handling state, which results in significant overheads to the performance. In this article, the key idea is to detect bottleneck operator correctly using the runtime bottleneck detection approach and then scale out this operator and manage its internal state in a way that we can achieve almost zero latency. During the bottleneck detection process, we have defined alarming_threshold, a parameter for the operators that can be bottleneck operators in the future and scale_out_threshold, when the operator is bottleneck. To scale out, we have presented two techniques, active backup and checkpointing, the former one will start a Secondary Execution (SE) in back end by partitioning state and input streams to multiple nodes at alarming_threshold; this SE will replace primary node at scale_out_threshold. In the latter technique, a State Manager (SM) module will start state checkpointing at alarming_threshold to external store and perform scale out by managing state and input stream at scale_out_threshold. The first approach will help us to achieve almost zero latency goal, while the latter one is a resource efficient technique. Our results show that both techniques are working while providing desired goals of reducing overall latency during scale out and improving resource utilization.
Feather evolution from knotted barbules to hooked and unhooked barbules and its finite element analysis

Experiments involving scanning electron microscopy of the microstructure of barbules and optical microscopy of knotted barbules were conducted: the behaviour of knotted barbules, and their evolution to form a branch of hooked barbules and unhooked barbules were analysed. A growth model for a feather plume was proposed. MATLAB™ 2-d contour microstructures of sectioned knotted feathers and three-dimensional structural models of barbules were established, moreover, these were analysed using the finite element method. The response under load of different parts of the barbules was obtained. The results showed that there were stress concentrations in feather barbules. The study laid a foundation for finding the internal and external causes of the evolutionary transition of knotted barbules.
RSA Cryptography and Prime Numbers

This chapter presents the RSA cryptosystem, a much-heralded cryptosystem used worldwide. Large prime numbers are needed to construct an RSA cryptosystem, so the second half of the chapter is devoted to seeing how many large primes there are, and how to identify large primes with high confidence.
A New Image Watermarking Technique in Spatial Domain Using DC Coefficients and Graph Representation

In this paper, we propose a new watermarking technique that ensures a suitable compromise between the authentication level of host images and the computational complexity with maintaining a good visual quality of the watermarked image and a good robustness against common image processing attacks. The DC components of host image and image-to-graph representation are used to select particular positions of the original image as inputs of the embedding process in spatial domain. Knowing that the DC value provides a measure of the texture or smooth nature. The proposed model is based on building a directed graph from the original image after dividing it into $$ N \times N $$ blocks. Each block is represented as a vertex and the DC value obtained from Discrete Cosine Transform (DCT) process to the block is used as an edge cost. A simply connected path algorithm is suggested to process the directed graph and to find a simply connected path whose blocks will be used to hide secret data (watermark). One main contribution of this paper is achieving high robustness and low degradation of images in comparison to other existing approaches in spatial domain, by embedding watermark in high textured image blocks. Another main contribution is providing the possibility to recover the watermark even in case of cropping or rotation attacks, due to the embedding of the watermark in several blocks. The proposed model has been tested on gray scale images under several attacks scenario and the experiments results show significant ratios of robustness and bit error rates against the attacks.
Class Reconstruction in the Space of Natural System Classification

The article deals with the method of natural system classification developed within the paradigm of open systems physics. The method reveals the system ontology of classes by empirical description. Using this method we solved four classification tasks from different knowledge areas: toxicology, semiconductor manufacturing, numbers recognition, biology. For each dataset automatically were obtained: reconstructions of natural system-conditioned features of each class; reconstructions of intensional components; ideal representatives of classes; borders of class areas and referent distribution by class areas.
A critical insight into the identity authentication systems on smartphones

&lt;span&gt;The advancement of computing power on mobile devices and their popularity among people in performing sensitive data exchange is uncovering an urgent need for a highly secure solution than the existing ones. The need for such a security solution persists that should be able to thwart not only the contemporary threats but also offer continued support without bowing down to progression in technology. Though some security solutions have been contemplated, a lack of standardised or absolute security solution which can provide a feasible and secure solution to mobile phones exists. In this paper, a survey based on various biometric and non-biometric access management schemes has been performed. The copious solutions put forward by researchers so far were discovered to fail in traits like user adaptability and efficiency. Each of the works put forward by various researchers has been single-handedly contemplated followed by analysis. Ultimately, open problems and challenges were inferences from the survey conducted.&lt;/span&gt;
Towards Enhancing Historical Analogy: Clustering Users Having Different Aspects of Events

Studying history can provide numerous benefits for finding meaningful connections or analogies over time. Several researchers have studied how to support promoting historical analogy by computer supported learning; however, supporting group discussions to promote the analogy still remains unexplored. In this paper, we propose a novel clustering algorithm to form users who have different aspects of the same past event in order to ease exchange ideas of what aspects they focus to analyze the event. We implemented our algorithms and evaluated them in terms of getting accuracies of forming users having different aspects. Experimental results proved that only our algorithm creates suitable groups.
Blockchain in Decentralized Local Energy Markets

Within the context of implementing decentralized energy markets, it is necessary to integrate existing structures into this process of change and let established participants play an active role in it. The problem of the conversion is the way how the current, and sometimes still rigid, accounting systems can be adjusted to a flexible and adaptable energy market. It should be noted that today’s ordinary consumer must have the ability to feed energy into the grid, thereby taking a prosumer role. This article describes a concept developed within the context of the WindNode project to address this problem. The concept includes an approach focused on billing processes to realize a decentralized energy market. Attention was paid to scalability, flexibility, business process modeling and security. The result describes an approach to how any participant can be integrated into such a network, whether as an ordinary consumer or as a prosumer.
Dropout for Recurrent Neural Networks

Neural networks are computational structures which can be trained to perform tasks based on training examples or patterns. Recurrent neural networks are a type of network designed to process time-series data. Dropout is a neural network regularization technique. The literature advises that Dropout should not be directly applied to recurrent neural networks as its effects are too dramatic when applied recurrently. This direct approach is described as naive. Instead, there are two specialised recurrent neural network Dropout algorithms proposed by different authors. However, these specialised Dropout algorithms have not been tested against one another and the naive algorithm under identical experimental conditions. This paper compares all of these algorithms and finds that the naive approach performed as well as or better than the specialised Dropout algorithms.
A Framework of New Hybrid Features for Intelligent Detection of Zero Hour Phishing Websites

Existing machine learning based approaches for detecting zero hour phishing websites have moderate accuracy and false alarm rates and rely heavily on limited types of features. Phishers are constantly learning their features and use sophisticated tools to adopt the features in phishing websites to evade detections. Therefore, there is a need for continuous discovery of new, robust and more diverse types of prediction features to improve resilience against detection evasions. This paper proposes a framework for predicting zero hour phishing websites by introducing new hybrid features with high prediction performances. Prediction performance of the features was investigated using eight machine learning algorithms in which Random Forest algorithm performed the best with accuracy and false negative rates of 98.45% and 0.73% respectively. It was found that domain registration information and webpage reputation types of features were strong predictors when compared to other feature types. On individual features, webpage reputation features were highly ranked in terms of feature importance weights. The prediction runtime per webpage measured at 7.63 s suggest that our approach has a potential for real time applications. Our framework is able to detect phishing websites hosted in either compromised or dedicated phishing domains.