Reviewerly/exHarmony huggingface dataset:
AI Meets Austen: Towards Human-Robot Discussions of Literary Metaphor

Artificial intelligence is revolutionizing formal education, fueled by innovations in learning assessment, content generation, and instructional delivery. Informal, lifelong learning settings have been the subject of less attention. We provide a proof-of-concept for an embodied book discussion companion, designed to stimulate conversations with readers about particularly creative metaphors in fiction literature. We collect ratings from 26 participants, each of whom discuss Jane Austen’s Pride and Prejudice with the robot across one or more sessions, and find that participants rate their interactions highly. This suggests that companion robots could be an interesting entryway for the promotion of lifelong learning and cognitive exercise in future applications.
Enterprise security economics: A self‐defense versus cyber‐insurance dilemma

Abstract We propose a model that optimizes enterprise investments in cybersecurity using expected utility theory. The model allows computing (a) investment in self‐defense to reduce the risk of security breaches, (b) investment in cyber insurance to transfer the residual risk to insurance companies, and (c) investment in forensic readiness to make the insured firms capable of generating provable insurance claims about security breaches. A three‐phase–based model of vulnerability rate evolution over time is proposed and used to estimate the different planned security expenditures throughout the investment horizon. At the starting time of investment, a decision maker invests to cover the existing risk of breach and periodically spends to cover the additional risk observed due to the release of new vulnerabilities. In this work, the intermediate tranches are determined while considering three different attitudes of decision makers, namely, optimistic, pessimistic, and realistic. An analysis is conducted to assess the performance of the proposed models.
Heuristics-Based Explorer for 2D Navigation

Explorer algorithms are important in the process of creating maps in unknown environments, which is an essential functionality in scenarios with changing environments such as rescue, surveillance, cleaning and transport. This project contributes to the development with the Heuristics-Based Explorer (HBE) and the experimental framework created for it. The HBE presents a novel method that integrates consolidated heuristics with a state-of-the-art computer vision heuristic. The evaluation consists of a functional model of a simulated mobile robot with sensing capabilities and a set of indoor environments to test the algorithm. This article presents a detailed description of the HBE, the system surrounding it and the experiments designed for its characterization. The results show that the proposed method outperforms existing solutions.
Ensuring Secure Application Execution and Platform-Specific Execution in Embedded Devices

The Internet of Things (IoT) is expanding at a large rate, with devices found in commercial and domestic settings from industrial sensors to home appliances. However, as the IoT market grows, so does the number of attacks made against it with some reports claiming an increase of 600% in 2017. This work seeks to prevent code replacement, injection, and exploitation attacks by ensuring correct and platform specific application execution. This combines two previously studied problems: secure application execution and binding hardware and software. We present descriptions of both problems and requirements for ensuring both simultaneously. We then propose a scheme extending previous work that meets these requirements, and describe our implementation of the soft-core Secure Execution Processor developed and tested on Xilinx Spartan-6 FPGA. Finally, we analyse the scheme and our implementation according to performance and the requirements listed.
Sorting on Encrypted Data

Sorting is an age old problem of rearrangement. Since arrangement of items has profound influence on speed and simplicity of algorithms, sorting has attracted great deal of importance in computer science literature. Recently with the advent of cloud computing we revisit problem of sorting on encrypted data. Sorting network consists of comparators and swapping operations. The difference between classical comparison-based sorting algorithms and sorting networks on encrypted inputs are discussed in this chapter. The interesting fact for encrypted domain is that all operations must be data independent in the flow of the algorithm steps in sorting networks. Hence, in spite of the fact that data dependent algorithms may be faster, they may not suitable for encrypted data.
Move selection in MTD ( f )

In previous publications on the MTD(f) algorithm, the intricacies of the mechanisms to select (1) the best move (the move to play), (2) the move to store in the transposition table (TT), (3) the principal variation (PV), if any, and (4) the move to ponder on (the move the opponent is expected to pl ay) are left to the reader. This note11An earlier version of this note was published on the World Draughts Forum (see Van Horssen, Online a). This triggered reactions by Ed Gilbert (Kingsrow) and Gérard Taille (Damy). They independently solved the problem of move selection in MTD(f) in the presence of search inconsistencies, resulting in equivalent solutions (however, not published). During the work on this note I became aware of a number of insightful posts on the Computer Chess Club forum, by various contributors and scattered around many different discussion threads (see CCC, Online and CCCA, Online). The Chess Programming Wiki (Isenberg, Online) served as a welcome entry point. The most important post, concerning the (semi-)PV and thus the best move, is referenced explicitly (see Letouzey, Online b). In addition, a number of MTD(f) implementations found on the GitHub repository were also studied. Regarding the behavior when search inconsistencies are present, we may state that some implementations are safe (although all different) and some are unsafe, that is, capable of blundering. fills a theoretic void on these four topics, showing that in the presence of search inconsistencies there is a safe mechanism to select the best move. Selecting a ponder move is also investigated. Experimental results in 10 × 10 draughts are summarized and provided online.
A FIPA-Compliant Framework for Integrating Rule Engines into Software Agents for Supporting Communication and Collaboration in a Multiagent Platform

Production rules have been traditionally considered a good knowledge representation formalism for creating expert systems, and also as a good mechanism for building intelligence within software agents due to the flexibility of their declarative knowledge representation. But the use of rules and rule engines for implementing behaviors inside agents in the context of a multiagent environment has a number of challenges if one wants to adhere and preserve the defining characteristics of agency and support the kind of communication and collaboration mechanisms needed by multiagent systems. This paper describes a framework, and its implementation, for the integration of production rules reasoning mechanisms inside software agents in the context of a multiagent platform, preserving the defining characteristics of agency and accomplishing the well-established FIPA standards for agent interaction. The implementation has been accomplished by using either CLIPS or Jess as the rule engine, but it can be extended to other formalisms. JADE was chosen as the multiagent platform for developing and testing the solution. The proposed framework includes the uncoupled integration of the rule inference engine into the agent and the adherence to FIPA specifications about using protocols and communication processes. The resulting framework can be used to adapt legacy expert systems whose functionality can be divided to be performed by separate agents or to build new, distributed intelligent systems exploiting the capacities of a multiagent platform at different levels. To show the validity of the approach, a functional test environment was built and is available for downloading.
Appliance of Social Network Analysis and Data Visualization Techniques in Analysis of Information Propagation

This paper explains appliance of social network analysis and data visualization techniques in analysis of information propagation. Context of information (news) propagation through social network is an extremely dynamic and complex area to study. Due to topic actuality and a very small number of works on the similar topic this paper required a comprehensive and systematic approach. Thus, for practical reasons this work is based on the usage of Social Network Analysis (SNA) and visualization of social networking data obtained through Facebook covering 145 + public pages linked to 2.6 million fans. The main hypothesis is based on the premise whether is possible to find any similarities between the real-life social, economic and political entities/processes and online information propagation. The process consists of the development of the underlying model, the retrieval of data, data processing and consequential analysis & visualization which has been elaborated in detail along with the comments related to the methods of application.
Managing computational gateway resources with XDMoD

The U.S. National Science Foundation (NSF) has invested heavily in research computing, funding the XSEDE network of supercomputers and enabling their integration with science gateways. To ensure maximal return on this substantial investment, it is essential to monitor the use of these computing resources. XD Metrics on Demand (XDMoD) is an NSF-funded tool that was developed to help manage high performance computational resources. XDMoD metrics describe accounting and performance data for computational jobs, including resources consumed, wait times, and quality of service. XDMoD can provide information on individual jobs, or data aggregated over an ensemble of jobs. Its web interface offers centralized charting, exploration, and reporting of these metrics, for user-selected time ranges, and across resources. XDMoD is directly relevant to the gateways community. In this paper, we introduce XDMoD, demonstrate its utility for gateways, and outline our plans to further enhance its capabilities for the gateways community. Furthermore, we demonstrate how XDMoD can help gateway users and gateway and resource managers answer many questions about utilization, performance, and availability. In doing so, we showcase the evolution of gateways in the XSEDE ecosystem.
Semantic Explanations in Ensemble Learning

A combination method is an integral part of an ensemble classifier. Existing combination methods determine the combined prediction of a new instance by relying on the predictions made by the majority of base classifiers. This can result in incorrect combined predictions when the majority predict the incorrect class. It has been noted that in group decision-making, the decision by the majority, if lacking consistency in the reasons for the decision provided by its members, could be less reliable than the minority's decision with higher consistency in the reasons of its members. Based on this observation, in this paper, we propose a new combination method, EBCM, which considers the consistency of the features, i.e. explanations of individual predictions for generating ensemble classifiers. EBCM firstly identifies the features accountable for each base classifier's prediction, and then uses the features to measure the consistency among the predictions. Finally, EBCM combines the predictions based on both the majority and the consistency of features. We evaluated the performance of EBCM with 16 real-world datasets and observed substantial improvement over existing techniques.
Closed-form Bayesian image denoising: improving the adaptive Wiener filter through pairwise Gaussian-Markov random fields

In this paper, we propose a contextual adaptive Wiener filter by using an isotropic pairwise Gaussian Markov random field (GMRF) prior to model the spatial correlation existing in natural images. The motivation for the development of the proposed method is that, in the context of Bayesian estimation, the Wiener filter is known to be the optimal estimator under Gaussian noise. The resulting closed-form filter can be viewed as a two-stage denoising process in which the first stage considers independent pixels whereas the second stage incorporates a degree of spatial dependence by means of maximum pseudo-likelihood estimation of the coupling parameter. The obtained results show that the proposed method outperforms the classical pointwise Wiener filter, in some cases reaching results comparable to some state-of-the-art methods in denoising, but with a reduced computational cost.
Towards Content Expiry Date Determination: Predicting Validity Periods of Sentences

Knowing how long text content will remain valid can be useful in many cases such as supporting the creation of documents to prolong their usefulness, improving document retrieval or enhancing credibility estimation. In this paper we introduce a novel research task of forecasting content's validity period. Given an input sentence the task is to approximately determine until when the information stated in the content will remain valid. We propose machine learning approaches equipped with NLP and statistical features that can successfully work on a relatively small number of annotated data.
Combined graph kernels for automatic patent classification: A hybrid approach

In this study, we proposed combined kernel-based methods to leverage patent citation graph performance for patent classification. The concept is to use the combined graph kernels of the citation graph to classify patent documents, as a hybrid approach. A multiple kernel framework was used for integrating multiple datasets of various kernels into a combined kernel. We employed seven graph kernels as the baselines and the combination of random walks and Weisfeiler–Lehman subtree kernels to achieve higher performance. We calculated the kernel values of each patent pairwise and employed an SVM classifier to carry out the classification task. The investigation results demonstrate that the combined graph kernel outperforms single kernels.
PESSN: Precision Enhancement Method for Semantic Segmentation Network

Semantic segmentation to understand an image at pixel level is an important problem in the computer vision. In the traditional object detection, each object in an image is detected at its minimum bounding rectangle level, but, in the semantic segmentation, it is detected at pixel level and thus the segmentation result is more flexible and meaningful. However, the characteristics of the semantic segmentation network might make over-segmentation with a few pixels misunderstood and result in the low precision rate. In this paper, we propose a method to enhance the precision rate of the semantic segmentation network. In order to address the over-segmentation, we define confidence-based and semantic-correlation-based outliers. Confidence-based outlier is defined by the confidence value, weighted by the number of segment's pixels, of the semantic segmentation network and semantic-correlation-based outlier is defined by the distance in the Word2Vec space. If a pixel is determined as not only confidence-based but also semantic-correlation-based outlier, the pixel is pruned from the segmentation result. We evaluate the proposed method with the images of COCO dataset and show the f-score, as well as the precision rate, of the semantic segmentation is significantly improved.
Role of Electronics Devices for E-Health in Smart Cities

As the world's population ages, those suffering from diseases will increase. Researchers in electronics, computer, networking, and medical fields need to work more seriously to make the broad vision of smart healthcare/e-health system. To achieve the objectives of e-healthcare in smart cities, there is a need to create new system that allows the acquisition of health information smartly, automatically, and transparently in order to take efficient decisions provided by the supporting system. Such systems may be designed technically by embedded together communication, smart signals, internet of things, network of sensors.
A Multi-scale Recalibrated Approach for 3D Human Pose Estimation

The major challenge for 3D human pose estimation is the ambiguity in the process of regressing 3D poses from 2D. The ambiguity is introduced by the poor exploiting of the image cues especially the spatial relations. Previous works try to use a weakly-supervised method to constrain illegal spatial relations instead of leverage image cues directly. We follow the weakly-supervised method to train an end-to-end network by first detecting 2D body joints heatmaps, and then constraining 3D regression through 2D heatmaps. To further utilize the inherent spatial relations, we propose to use a multi-scale recalibrated approach to regress 3D pose. The recalibrated approach is integrated into the network as an independent module, and the scale factor is altered to capture information in different resolutions. With the additional multi-scale recalibration modules, the spatial information in pose is better exploited in the regression process. The whole network is fine-tuned for the extra parameters. The quantitative result on Human3.6m dataset demonstrates the performance surpasses the state-of-the-art. Qualitative evaluation results on the Human3.6m and in-the-wild MPII datasets show the effectiveness and robustness of our approach which can handle some complex situations such as self-occlusions.
User Testing: Gathering Data from First-Year Medical Students as They Interact with the EBSCO Discovery Service (EDS)

Researchers from the Mercer University School of Medicine and EBSCO Information Services formed a partnership to conduct user testing on the library’s EBSCO Discovery Service (EDS) interface. The goal of the study was to determine if first-year medical students encountered difficulty when asked to retrieve full-text, eBook, peer-reviewed, and print items relevant to their current research. Participants experienced some confusion when formulating search terms and selecting and applying limiters to refine searches.
Research on Personal Credit Scoring Model Based on Artificial Intelligence

Artificial intelligence is considered to be the technological commanding height of the next era. At present, after the development of China’s artificial intelligence industry ranks in the United States, its application in the financial field is also in a new stage of rapid development, and affects many aspects of the financial industry, thus strengthening its research is of great significance. The continuous development of artificial intelligence technology has been widely used in many aspects of financial services, which is of great significance for the realization of its modeling, standardization and intelligent development. However, there are still security risks hidden in the application, which requires attention to this. aspects of the research to identify effective measures for risk prevention, this paper analyzes the application of artificial intelligence in the financial sector in the personal credit score.
Patterns and Humans

The appearance of spiral structures on the human tongueTongue (geographic tongue)Geographic Tongue (GT) and skin (pathological rashesRash) are described. Affected and often migrating areas on the tongue can be bistable, patch-like fronts or waves. Pathological rashesRash with an expanding region of redness is often a result of an autoimmune disease in which the human immune system becomes hyperactive and attacks healthy tissues. In addition, we show a rotating spiralRotatingspiral created by humans, which has been described as a reaction-diffusion waves in an excitable mediumExcitablemedium/media.
Open-Set Web Genre Identification Using Distributional Features and Nearest Neighbors Distance Ratio

Web genre identification can boost information retrieval systems by providing rich descriptions of documents and enabling more specialized queries. The open-set scenario is more realistic for this task as web genres evolve over time and it is not feasible to define a universally agreed genre palette. In this work, we bring to bear a novel approach to web genre identification underpinned by distributional features acquired by doc2vec and a recently-proposed open-set classification algorithm—the nearest neighbors distance ratio classifier. We present experimental results using a benchmark corpus and a strong baseline and demonstrate that the proposed approach is highly competitive, especially when emphasis is given on precision.
Expectation Algorithm (ExA): A Socio-inspired Optimization Methodology

This paper introduces a new socio-inspired algorithm referred to as Expectation Algorithm (ExA), which is mainly inspired from the society individuals. The ExA modelled the variables of the problems as individuals of a society. The variables select their values by expecting the values of the other variables minimizing the objective function. The performance of the algorithm is validated by solving 50 unconstrained test problems with dimensions up to 30. The solutions were compared with several recent algorithms such as Covariance Matrix Adaptation Evolution Strategy, Artificial Bee Colony, Comprehensive Learning Particle Swarm Optimization, Self-adaptive Differential Evolution Algorithm, Backtracking Search Optimization Algorithm, Ideology Algorithm and Multi-Cohort Intelligence algorithm. The Wilcoxon signed-rank test was carried out for the statistical analysis and verification of the performance. The results from this study highlighted that the ExA outperformed most of the other algorithms in terms of function evaluations and computational time. The prominent features of the ExA algorithm along with the limitations are discussed as well.
Reliability-Aware Green Scheduling Algorithm in Cloud Computing

Nowadays, to a significant extent, cloud computing usage is increasing because of its enormous features such as resource sharing, on-demand resource provisioning, and virtualization. To provide the resources to the user according to their requirement, the client’s applications must be scheduled in an optimized way. A rapidly increasing number of users causes a huge amount of energy consumption while executing the task. The temperature of the system increases as there is a drastic increment in power density. Energy and temperature both are related to power consumption. Moreover, cloud servers are prone to failure, so it needs extra computation time to handle the failure. In this work, we proposed a scheduling algorithm which optimizes three conflicting objectives, i.e., reliability maximization, minimum energy consumption, and temperature consolidation in the cloud. The failure model used in our work is Weibull failure distribution that considers the effect of aging on the performance of the system. The simulation results show that the algorithm reduces the energy consumption while scheduling the task to the reliable virtual machine with less temperature consolidation.