Reviewerly/exHarmony huggingface dataset:
Function Word Adjacency Networks and Early Modern Plays

The Word Adjacency Network method underpinning the New Oxford Shakespeare's attribution of the Henry VI plays to Christopher Marlowe as co-author has not been independently tested and is only now being subjected to critiques.The response of Segarra et al. (
SUBSTITUTION IN RELEVANT LOGICS

Abstract This essay discusses rules and semantic clauses relating to Substitution—Leibniz’s law in the conjunctive-implicational form $s\dot{ = }t \wedge A\left( s \right) \to A\left( t \right)$ —as these are put forward in Priest’s books In Contradiction and An Introduction to Non-Classical Logic: From If to Is . The stated rules and clauses are shown to be too weak in some cases and too strong in others. New ones are presented and shown to be correct. Justification for the various rules is probed and it is argued that Substitution ought to fail.
Is Bigger Data Better for Defect Prediction: Examining the Impact of Data Size on Supervised and Unsupervised Defect Prediction

Defect prediction could help software practitioners to predict the future occurrence of bugs in the software code regions. In order to improve the accuracy of defect prediction, dozens of supervised and unsupervised methods have been put forward and achieved good results in this field. One limiting factor of defect prediction is that the data size of defect data is not big, which restricts the scope of application with defect prediction models. In this study, we try to construct bigger defect datasets by merging available datasets with the same measurement dimension and check whether bigger data will bring better defect prediction performance with supervised and unsupervised models or not. The results of our experiment reveal that larger-scale dataset doesn't bring improvements of both supervised and unsupervised classifiers.
Smart bus stop made with interactive surfaces

This is a video prototype of Smart Bus Stop which consists of interactive surfaces. The authors have envisioned a bus stop which shows necessary information for bus passengers on its wall and floor surfaces. Rapid prototypes were used to visualize functions of its navigation floor and personal walls. A functional prototype using image recognition and projection has been tested at a lab environment to specify technical details. Then a scenario skit was tested in an arrangement with a real bus. The authors assume that this concept will be applied not only for buses but also for many types of mobility solutions (e.g. ride-hailing app pickup zone, autonomous vehicle pickup zone).
Input modelling for multimodal data

Multimodal data occurs frequently in discrete-event simulation input analysis, typically arising when an input sample stream comes from different sources. A finite mixture distribution is a simple input model for representing such data, but fitting a mixture distribution is not straightforward as the problem is well-known to be statistically non-standard. Even though much studied, the most common fitting approach, Bayesian reversible jump Markov Chain Monte Carlo (RJMCMC), is not very satisfactory for use in setting up input models. We describe an alternative Bayesian approach, MAPIS, which uses maximum a posteriori estimation with importance sampling, showing it overcomes the main problems encountered with RJMCMC. We demonstrate use of a publicly available implementation of MAPIS, which we have called FineMix, applying it to practical examples coming from finance and manufacturing.
Non-Cooperative and Cooperative Game Approaches for Load Balancing in Distributed Systems

Distributed system is a system consisting of groups of servers and clients where they are connected together to do specific tasks. In order to optimize the system, a load balancer can be used to mitigate the effect of imbalance of the number of clients and servers. Without the load balancer, the servers might be overwhelmed due to response many requests of clients. To implement a load balancer, several methods or algorithms, such as Round-Robin, Least Connection, and Response Time have been applied. In this paper, we focus on game-theoretic load balancing algorithms in a cloud system. We formulate non-cooperative and cooperative game-theoretic models to be used as load balancing algorithms. Clients are the players in each game and the players' strategies are based on the current status of resource utilization of the servers. To obtain the solution, iterative algorithms combined with Discrete-Time Markov Chain (DTMC) are presented. Moreover, the comparison between non-cooperative and cooperative approaches is presented. The evaluation results of both the games are compared with the ones from common techniques, such as round-robin, randomization method, and least connection for effectiveness in terms of request completion time and balancing time.
Document-Level Named Entity Recognition by Incorporating Global and Neighbor Features

State-of-the-art named entity recognition models mostly process sentences within a document separately. Sentence-level named entity recognition is easy to cause tagging inconsistency problems for long text documents. In this paper, we first propose to use the neural network to encode global consistency and neighbor relevance among occurrences of a particular token within a document. We first encode sentences within a document independently by a sentence-level BiLSTM layer, then we design a document-level module to encode the relation between occurrences of a particular token. In our document-level module, we use CNN to encode global consistency features and apply BiLSTM to model neighbor relevance features. We further apply a gate to effectively fuse these two non-local features and use a CRF layer to decode labels. We evaluate our model on the CoNLL-2003 dataset. Experimental results show that our model outperforms existing methods.
Scoping Low-Cost Measures to Nowcast Sub-Hourly Solar Radiations for Buildings

Abstract Solar energy, as a clear, renewable and promising resource, has brought tremendous value for carbon emission reduction in the last three decades. However, the uncertainty of solar radiation brings significant challenges to the renewable heat and electrical system for buildings. Thus, many different solar radiation forecasting methods, such as Numerical Weather Prediction Methods (NWP), Statistical Methods, Top-Down Methods, Bottom-Up Methods and Hybrid Methods have been developed to make the best guess on future solar radiations. Based on the meteorology definition, nowcasting refers to the forecast of the temporal horizon from the next few seconds up to six hours. Predicting solar radiation within this range are extremely challenging, but crucial for solar energy system operation. This paper firstly reviewed the state of art of solar radiation forecasting methods and compared the key features of each method. According to the advantage and costs of each method, this paper then proposed a methodology to generate high accuracy low-cost solar radiation nowcast data for optimising building solar thermal system performance and related control strategies. Note that this paper focused on the scoping study of methodology rather than field experiments, and further research with experiments data will be reported in a journal publication.
Segmentation in Corridor Environments: Combining Floor and Ceiling Detection

Automatic segmentation from indoor images has several applications for mobile platforms. We address the problem of corridor segmentation and propose an approach by combining floor and ceiling detection. However, different difficulties may limit the accuracy of the system. To overcome these difficulties, a strategy is used in this paper to evaluate the degree of consistency of ceiling and floor guidelines. The method is based on computing the disparity between the hypothesized vanishing points by intersecting the boundaries par-wise. The approach is evaluated in a novel dataset. Our experimental validation confirms that the integration of floor and ceiling detection with the consistency model performs effectively and robustly. Because of the simplicity of the method, the image processing is quite fast and robust.
A Drone Localization System Based on Multiple Time Delays Fusion

The recent years have witnessed the prosperity of the amateur drones. In spite of the great convenience brought by drones, the potential issues caused by drones, including public security and individual privacy leakage, cannot be overlooked. To counter the abuse of drones, in this paper, we design a novel drone localization system using two tetrahedral acoustic arrays, which can effectively localize drones within 100-meter range. We propose a novel time difference of arrival (TDOA) estimation algorithm which combines generalized crosscorrelation and probabilistic data association algorithms to accurately estimate TDOA. Different from the existing TDOA estimation algorithms, the proposed algorithm overcomes the multi-path effect and ensures the effectiveness under the low-signal-noise ratio environment. In order to further improve the performance of the proposed algorithm, we develop a novel algorithm to optimize the procedure of TDOA initialization based on dynamic programming. Finally, extensive experiments have been implemented to verify the effectiveness of our system and algorithms.
Context-Aware Computing and Personalization

User modelling plays the main role to support an individual user to improve in his/her working experience, learning methods and information access. In the new generation of pervasive and context-aware systems, contextualized and personalized information and services are the driving entities. This chapter explains how context-aware computing is important for personalization. The relationship between machine learning, ontology learning and personalization is also discussed in this chapter. The next part of this chapter focuses on context-aware profiling and profile translation towards context-aware services. The need for clustering in ubiquitous computing and how context-aware clustering will play an important role in the next-generation Internet is discussed in the last part of this chapter.
Simultaneous Learning of Fuzzy Sets

We extend a procedure based on support vector clustering and devoted to inferringCermenati, Luca the membership function of a fuzzy set to the case of a universeMalchiodi, Dario of discourse over which several fuzzy sets are defined. Zanaboni, Anna Maria The extended approach learns simultaneously these sets without requiring as previous knowledge either their number or labels approximating membership values. This data-driven approach is completed via expert knowledge incorporation in the form of predefined shapes for the membership functions. The procedure is successfully tested on a benchmark.
A Delayed Neural Network for Solving a Class of Constrained Pseudoconvex Optimizations

This paper presents a delayed neural network (DNN) to solve a pseudoconvex optimization problem with equality constraints. Based on differential inclusion theory, the equilibrium point of the proposed DNN is proved to be exponentially stable. Moreover, for any initial value, the state of the DNN reaches equality constraint set in finite time and finally converges to an optimal solution to the pseudoconvex optimization problem. As far as we know, it is the first time that DNN is applied to solve pseudoconvex optimization problems. Compared with the existing neural networks for solving pseudoconvex optimization problems, the neural network here considers the time delays appearing in signal transmission. Furthermore, unlike convergence results based on complicated conditions, the convergence of states to the proposed DNN in this paper only rely on the assumption that the gradient of objective function in the pseudoconvex optimization problem is Lipschitz continuous. Finally, an examples is given to show the effectiveness of the proposed DNN.
Identifying Hidden Risks in Multi-Stakeholder, Dynamic System Development Environments

In this paper the authors explore the causes and effects of material weaknesses in multi-stakeholder, dynamic system development environments. In traditional system development, cost, schedule and performance measures and metrics are often used to baseline activity and quantify risk. Yet, system development efforts often fail to deliver the desired outcome or have catastrophic in-progress effects regardless of achieving the desired milestones and metrics. While cost, schedule and performance challenges clearly identify risks, the authors assert that the alignment of beliefs between stakeholders about expectations across system development activities add a level of insight that improves the ability of stakeholders to discover unknown risks and manage to successful system outcomes. To test this assertion the authors developed a probabilistic Bayesian Belief Network informed by retrospective, historical case studies. The model provides a mechanism to understand the stakeholders' beliefs and how these beliefs directly influence decisions and risks. This model was then tested by an analysis of active system development projects to see if it could identify, in a prospective sense, possible material weaknesses It is envisioned that this approach can be used as an initial analysis tool to baseline system development endeavors and provide continuous assessment in dynamic environments.
Graph-based multivariate conditional autoregressive models

The conditional autoregressive model is a routinely used statistical model for areal data that arise from, for instances, epidemiological, socio-economic or ecological studies. Various multivariate conditional autoregressive models have also been extensively studied in the literature and it has been shown that extending from the univariate case to the multivariate case is not trivial. The difficulties lie in many aspects, including validity, interpretability, flexibility and computational feasibility of the model. In this paper, we approach the multivariate modelling from an element-based perspective instead of the traditional vector-based perspective. We focus on the joint adjacency structure of elements and discuss graphical structures for both the spatial and non-spatial domains. We assume that the graph for the spatial domain is generally known and fixed while the graph for the non-spatial domain can be unknown and random. We propose a very general specification for the multivariate conditional modelling and then focus on three special cases, which are linked to well-known models in the literature. Bayesian inference for parameter learning and graph learning is provided for the focused cases, and finally, an example with public health data is illustrated.
Combining Data Mining Techniques for Evolutionary Analysis of Programming Languages

Programming languages have been evolving gradually in response to changes in the programming industry. Many factors have been driving this evolution: for instance, improving language expressiveness, fixing bugs, and introducing new language features. However, modifying programming languages is a challenging process. One of the main difficulties is to gauge the perception of developers regarding the language over time. Thus, we set out to develop a framework aimed at evaluating the evolution of programming languages based on their technical documentation and the community's feedback from online discussions. Essentially, our framework is comprised of three main components: (1) Topic Modeling, which aims to extract the main semantic topics from the language aspects; (2) Sentiment Analysis, whose objective is to evaluate the perception of developers with respect to each identified topic; and (3) Data Visualization, which presents a visual metaphor that summarizes the information obtained in previous steps. To evaluate our proof-of-concept implementation of the framework, we carried out an evolutionary analysis of the Python programming language. According to our results, our framework was able to identify several changes made to the language as well as the programmers' perceptions regarding those changes: for instance, we found that the use of iterators over traditional repetition structures (i.e., count-based repetition) was initially received negatively by the community, but the outlook of developers on this new feature has matured enough for it to be considered beneficial to the programming language.
The effect of changing training data on a fixed deep learning detection model

Abstract Within the lack of accurate data, for some computer vision applications, researchers usually use other pictures collected from different sources for the training. To know the effect of these added data, we compare the detection results of a customized dataset of objects, using the same detection model, while changing the training data fed into the network. For our work, we run the detection on images captured by the Microsoft Kinect sensor after training the network on different combinations of training data. The first part of the training data is captured by the Kinect itself, and the second is collected from several sources from the internet, referred to as collected images. We then change the distribution of these images between training and validation to feed them into the fixed training model. The results prove that this distribution of data can considerably affect training and detection results under the same model parameters. In addition, mixing the captured images with other collected ones can improve these results.
Fashion Show Films

Due to technological developments, fashion show films provide fashion brands the opportunity to communicate their brand concepts, to attract attention and to gain more brand awareness by publishing them in the Internet. The purpose of this research paper is to investigate how fashion brands communicate their brand concept and personality through fashion show films. For this purpose, ten fashion show films of brands from the categories luxury, premium and high-street activewear are investigated. The results indicate that the investigated brands use different ways to attract attention and to communicate their brand concept and personality. The design of the setting, the presentation of the collection as well as the visualization of the brand concept through the brand name, logo, colors or symbols and camera work play an important role to create an effective and exciting fashion show film in order to communicate the brand concept and to promote their brand image. Mostly luxury and premium brands use fashion show films for branding. For high-street activewear brands the analysis indicates less importance of fashion show films. The limitations of this research are related to the fact that the restricted number of ten fashion show films is analyzed. This gives an overview but cannot provide a comprehensive breakdown of this topic.
Binocular Stereo Matching Based on Convolutional Neural Networks

For the binocular stereo matching of deep learning based on patches, the networks structure is vital for matching cost in stereo matching. The task of using a pair of stereo images to estimate depth information can be achieved by a convolutional neural network after being formatted as a supervised learning task. However, the current stereo matching neural networks have poor stereo matching results in ill-posed-regions. In order to solve this problem, Our proposed a deep learning architecture that constructs a cost volume through improving the relationship between groups. The network consists of a feature extraction module, a cross-form spatial pyramid module and a feature matching fusion module. The improved stereo matching network is trained and verified on the KITTI data. The experimental results show that the improved network has certain advantages in terms of accuracy and speed compared with the previous methods.
Preserving Data Privacy in Electronic Health Records Using Blockchain Technology

Technology is a boon to mankind in this fast-growing era. The advancement in technology is the predominant factor for the sophisticated way of living of the people. In spite of technology, revolution happens across the world, and mankind still suffers due to various health issues. Healthcare industries take immense measures to improve the quality of life. An enormous volume of digital data is being handled every day in the healthcare industry. There arises a need for the intervention of technology in the healthcare industry to be taken to a greater extent. The prime duty of any healthcare industry is to store and maintain those data in the form of electronic health records (EHR) in a secured manner.
Experimental implementation of a continuous-time quantum random walk on a solid-state quantum information processor*

There are some problems that quantum computers seem to be exponentially faster than classical computers, like factoring large numbers, machine learning, and simulation of quantum systems. Constructing an appropriate quantum algorithm becomes more important for solving these specific problems. In principle, any quantum algorithm can recast by a quantum random walk algorithm. Although quantum random walk with a few qubits has been implemented in a variety of systems, the experimental demonstration of solid-state quantum random walk remains elusive. Here we report the experimental implementation of the quantum continuous-time random walk algorithm by a two-qubit quantum processor in a nitrogen–vacancy center in diamond. We found that quantum random walk on a circle does not converge to any stationary distribution and exhibit a reversible property. Our results represent a further investigation of quantum walking dynamics in solid spin platforms, may also lead to other practical applications by the use of quantum continuous-time random walk for quantum algorithm design and quantum coherence transport.
Video Fragmentation and Reverse Search on the Web

This chapter is focused on methods and tools for video fragmentation and reverse search on the web. These technologies can assist journalists when they are dealing with fake news—which nowadays are being rapidly spread via social media platforms—that rely on the reuse of a previously posted video from a past event with the intention to mislead the viewers about a contemporary event. The fragmentation of a video into visually and temporally coherent parts and the extraction of a representative keyframe for each defined fragment enables the provision of a complete and concise keyframe-based summary of the video. Contrary to straightforward approaches that sample video frames with a constant step, the generated summary through video fragmentation and keyframe extraction is considerably more effective for discovering the video content and performing a fragment-level search for the video on the web. This chapter starts by explaining the nature and characteristics of this type of reuse-based fake news in its introductory part, and continues with an overview of existing approaches for temporal fragmentation of single-shot videos into sub-shots (the most appropriate level of temporal granularity when dealing with user-generated videos) and tools for performing reverse search of a video on the web. Subsequently, it describes two state-of-the-art methods for video sub-shot fragmentation—one relying on the assessment of the visual coherence over sequences of frames, and another one that is based on the identification of camera activity during the video recording—and presents the InVID web application that enables the fine-grained (at the fragment-level) reverse search for near-duplicates of a given video on the web. In the sequel, the chapter reports the findings of a series of experimental evaluations regarding the efficiency of the above-mentioned technologies, which indicate their competence to generate a concise and complete keyframe-based summary of the video content, and the use of this fragment-level representation for fine-grained reverse video search on the web. Finally, it draws conclusions about the effectiveness of the presented technologies and outlines our future plans for further advancing them.