Reviewerly/exHarmony huggingface dataset:
Interactive Narrative Generation Using Location and Genre Specific Context

In this paper, we present a novel method of creating interactive narratives. Location-based narrative sentences are mapped to their real-world location using GPS. A corpus of genre-based sentences is aggregated to train a neural network. New narratives are created by selecting a sentence from the network’s “Sentence Space” that corresponds to contextual information from the user’s physical location. Each narrative is explored using GPS location tracking and WebGL based augmented reality in mobile web browsers.
Do, or Do Not, Make Them Think?: A Usability Study of an Academic Library Search Box

This usability study explores whether patrons prefer and are well-served by specialized, format-based searches or simpler, single-box searches, and the implications of these practices and preferences for information literacy. Qualitative data suggest that format-specific searches can confuse and slow down users and single-box searches are often faster and more successful. These findings highlight the potential conflict between user experience (UX) design and traditional conceptualizations of research and library-based search.
A Hybrid Bat Algorithm Based on Combined Semantic Measures for Word Sense Disambiguation

The task of assigning an appropriate sense for an ambiguous word depending on its context is referred to as Words Sense Disambiguation (WSD). The objective of WSD is to attain an improved accuracy for real world applications, such as in information extraction, automatic summarisation, or machine translation. The WSD is solved through the implementation of a computational intelligence approach known as the Bat Algorithm (BA). The BA has the potential to explore an expansive area of the search space as it is a population-based algorithm, which makes it considerably efficient in the diversification process. To further improve the search, a local search algorithm referred to as Hill Climbing (HC) is applied that balances the exploration as well as the exploitation aspects. The suggested algorithm has the ability to optimise the semantic value of the words from the inputted text. In this study, the semantic measure depends on the Leacock and Chodorow (LCH) algorithms and the extended Lesk (eLesk). The recommended algorithm is tested based on certain benchmark datasets. According to the experimental results, it is found that our algorithm can derive better quality performance than that of other relevant algorithms. It is, therefore, concluded that the method suggested in this study provides an effectual solution for the WSD problem.
Learning soft domain constraints in a factor graph model for template-based information extraction

The ability to accurately extract key information from textual documents is necessary in several downstream applications e.g., automatic knowledge base population from text, semantic information retrieval, question answering, or text summarization. However, information extraction (IE) systems are far from being errorless and in some cases commit errors that seem obvious to a human expert as they violate common sense or domain knowledge. Towards improving the performance of IE systems, we focus on the question of how domain knowledge can be incorporated into IE models to reduce the number of spurious extractions. Starting from the assumption that such domain knowledge cannot be incorporated explicitly and manually by domain experts due to the amount of effort and technical complexities involved, we propose a machine learning approach in which domain constraints are acquired as a byproduct of learning a model that learns to extract key information in a supervised setting. We frame the task as a template-based information extraction problem in which several dependent slots need to be automatically filled and propose a factor graph based approach to model the joint distribution of slot assignments given a text. Beyond using standard textual features in factors that score the compatibility of slot fillers in relation to the text, we use additional features that are text-independent and capture soft domain constraints. During the training process, these constraints receive a weight as part of the parameter learning process indicating how strongly a constraint should be enforced. These domain constraints are thus 'soft' in the sense that they can be violated, but the system learns to penalize solutions that violate them. The soft constraints we introduce come in two flavors: on the one hand we incorporate information about the mean of numerical attributes and use features that indicate how far a certain value is from the mean. We call these features single slot soft constraints. On the other hand, we model the pairwise compatibility between slot filler assignments independent of the textual context, thus modeling the (domain) compatibility of the slot assignments. We call the latter ones pairwise slot soft constraints. As main result of our work, we show that learning pairwise slot soft constraints improves the performance of our extraction model compared to single slot soft constraints by up to 6 points in F1, leading to an F1 score of 0.91 for individual template types. Further, the human readable output format of our model enables the extraction and interpretation of the learned soft constraints. Based on this, we show in an evaluation by domain experts that more than 68% of the learned soft constraints are regarded as plausible.
What blockhain developers and users expect from virtual currency regulations: A survey study

Blockchain and distributed ledger technologies (BC/DLT) are one of the hottest hype information technologies. Within BC/DLT regulatory development, virtual currency regulations have dominated both research and regulative work. At the same time, the impacts on virtual currency regulation on their developers and users have been investigated limitedly, especially empirically. This article fills this research gap. The present article reviews research on regulatory frameworks, approaches and regulations primarily within the European Union (EU) as the empirical data is collected within one EU member state. The virtual currency regulation use case of the country is also described. Survey data was collected from 40 virtual currency service developers and users with the help of three BC/DLT and fintech associations. The detected 27 regulatory expectations were rank-ordered with 164 pairwise comparisons by using the analytic hierarchy process (AHP) based Wiki Survey method. The respondents indicated that increased clarity of taxation is their most important regulatory expectation followed by regulators’ understanding about the nature of distributed ledgers. This article contributes to research by consolidating findings on regulations, by empirically investigating developers’ and users’ regulatory expectations and by extending the use of the Wiki Survey to fintech and BC/DLT studies.
MATLANG

We investigate the expressive power of MATLANG, a formal language for matrix manipulation based on common matrix operations and linear algebra. The language can be extended with the operation inv for inverting a matrix. In MATLANG + inv we can compute the transitive closure of directed graphs, whereas we show that this is not possible without inversion. Indeed we show that the basic language can be simulated in the relational algebra with arithmetic operations, grouping, and summation. We also consider an operation eigen for diagonalizing a matrix. It is defined such that for each eigenvalue a set of orthogonal eigenvectors is returned that span the eigenspace of that eigenvalue. We show that inv can be expressed in MATLANG + eigen. We put forward the open question whether there are boolean queries about matrices, or generic queries about graphs, expressible in MATLANG+eigen but not in MATLANG+inv. Finally, the evaluation problem for MATLANG + eigen is shown to be complete for the complexity class 9R.
Thermal-benchmarking for cloud hosting green data centers

Thermal efficient usage of cloud hosting data center servers saves cooling energy and helps establish green cloud data centers. To achieve this goal, the data centers must be stress-tested to avail thermal data related to server utilization. The inherent limitations of cloud computing limit the control of cloud data center owner over the workload execution of cloud services except for the infrastructure and therefore thermal-aware workload modeling or thermal benchmarking of cloud infrastructure can fill this gap. Thermal-benchmarking techniques, through manipulation of server utilization, reveal the thermal profiles and thermal statistics of the servers that can be useful for thermal efficient data center management. This paper presents a generic approach to thermal-benchmarking and profiling of cloud hosting data center servers. We propose workload models to generate customizable thermal benchmarks for stress testing of data center servers. Additionally, we use workload traces from Alibaba cloud to generate thermal statistics to show that the proposed thermal benchmarking approach is applicable to any data center workload trace for any data center server.
Bitcoin Jumps and Speculations: Empirical Evidence from High-Frequency Data

The aim of this chapter is to investigate the relationship between the jump dynamics of Bitcoin prices and the speculations by using high-frequency data. I measure the significance of the jumps using Huang and Tauchen (Journal of Financial Econometrics, 3, 456–499) nonparametric test and Google’s Trends statistics for the measurements of the speculation. Since the futures contracts on Bitcoin transactions plays significant effect on its volatility, therefore, this paper additionally tests the effect of the futures contracts on this relationship. The results show that there is a discrete jump in the Bitcoin price around speculations and the futures contracts do not have any significant effect on this relationship, but notably after the launch of futures contract, the speculations have much higher significant effect on the Bitcoin jumps.
Imposing assertions in Maude via program transformation

Program transformation is widely used for producing correct mutations of a given program so as to satisfy the user's intent that can be expressed by means of some sort of specification (e.g. logical assertions, functional specifications, reference implementations, summaries, examples). This paper describes an automated correction methodology for Maude programs that is based on program transformation and can be used to enforce a safety policy, given by a set A of system assertions, in a Maude program R that might disprove some of the assertions. The outcome of the technique is a safe program refinement R' of R in which every computation is a good run, i.e., it satisfies the assertions in A. Furthermore, the transformation ensures that no good run of R is removed from R'. Advantages of this correction methodology can be summarized as follows. •A fully automatic program transformation featuring both program diagnosis and repair that preserves all executability requirements.•A simple logical notation to declaratively express invariant properties and other safety constraints through assertions.•No dynamic information is required to infer program fixes: the methodology is static and does not need to collect any error symptom at runtime.
A Novel Fair and Verifiable Data Trading Scheme

With the widespread use of smart devices, a huge volume of data is generated every day, which is helpful for device user and device enterprises. However, the data generated by the smart device contains the user’s privacy, and the data is easy to be modified, forged, which requires a suitable scheme to protect the privacy of the data seller, the authenticity of the data, the fairness during the data trading process. In order to solve the problems, we design a novel fair and verifiable data trading scheme by combining hash function, signature, oblivious transfer, smart contract and private blockchain. The hash function is used for data integrity, the signature is used for the source of the data, the oblivious transfer is used for data verification, the smart contract is used for the encryption key trading, and the private blockchain is used as a ledger for the verification record, trading record and user reputation. The performance analysis shows that our scheme has enough features to help users complete data trading, and our scheme provides an extra function, the reputation record of users to reduce the possibility of user being deceived. The security analysis shows that our scheme provides IND-CCA security, anonymity, and has the capability of resisting collusion attack and data seller fraud. The fairness and practicability of the scheme are verified by simulation.
GSD-Net: Compact Network for Pixel-Level Graphical Symbol Detection

In document images, graphical elements are commonly used in many application domains, such as industries, architectures and offices. In graphics recognition domain, symbol spotting, recognition and retrieval are three different interested areas, where locating them is important in real-world applications, since not all graphical elements are isolated and lineal. While deep learning based solutions provide very easily trainable end-to-end system, they are usually computationally expensive. In this work, we propose a compact deep learning based model for pixel-level graphical element segmentation that can perform at a similar level as state-of-the-art architectures. The proposed network is trained on multiple datasets from the Systems Evaluation SYnthetic Documents (SESYD) data repository. Further, we demonstrated the robustness of the network against noise and affine transformations.
Light Me Up: An Augmented-Reality Projection System

Real-time facial projection mapping is a challenging problem due to the low system latency and the high spatial augmentation accuracy requirements. We propose a new compact and inexpensive projector-camera system (ProCam) composed of off-the-self devices that achieves dynamic facial projection mapping. A mini projector and a depth sensor camera are coupled together to project content on a user's face. In one application, the camera tracks the facial landmarks of a person and simulated makeup is mapped on the person's face. The latter is created by defining different zones of interest on the face. Instead of using sophisticated hardware, we propose an affordable system that can be easily installed anywhere while it assures an immerse experience. No initialization phase is needed and the system can handle different face topologies. In addition, the users can keep their eyes open and enjoy the projetion in a mirror.
Fluctuating Time Quantum Round Robin (FTQRR) CPU Scheduling Algorithm

In multitasking world of today, CPU scheduling is the only process to determine how different requests can be serviced which are in the ready queue waiting for execution. CPU scheduling algorithms make sure to increase the CPU utilization by decreasing “average turnaround time (TAT),” “average waiting time (WT),” and “context switches” among different processes. Round robin scheduling algorithm is the most efficient algorithm which is used to increase the efficiency of CPU. This paper is proposing a new fluctuating time quantum round robin algorithm (FTQRR) using attributes of “Dynamic Time Quantum based Round Robin” (DTQRR) (Berhanu et al. in Int J Comput Appl 167(13):48–55, 2017 []) and “Improved Round Robin with Varying Time Quantum” (IRRVQ) (Kumar Mishra and Rashid in Int J Comput Sci Eng Appl 4(4):1–8, 2014 []) such that “average turnaround time, average waiting time, and context switches” among various process will be minimized.
Investigating the Learning Process in Job Search

We investigated the learning process in search by conducting a log-based study involving registered job seekers of a commercial job search engine. The analysis shows that job search is a complex task: seekers usually submit multiple queries over sessions that can last days or even weeks. We find that querying, clicking, and job application rates change over time: job seekers tend to use more filters and a less diverse set of query terms. In terms of click and application behavior, we observed a significant decrease in click rate and query term diversity, as well as an increase in application rates. These trends are found to largely match information seeking models of learning in a complex search task. However, common behaviors are observed in the logs that suggest the existing models may not be sufficient to describe all of the users' learning and seeking processes.
AHMED: Toolset for Ad-Hoc Mixed-reality Exhibition Design

We present “AHMED”, a mixed-reality toolset that allows visitors to experience mixed-reality museum or art exhibitions created ad-hoc at locations such as event venues, private parties,or a living room. The system democratizes access to exhibitions for populations that cannot visit these exhibitions in person for reasons of disability, time-constraints, travel restrictions, or socio-economic status.
Tree Structure Network: A Learning-Based Deep Network for Classification of CPU Instruction through EM Signal

In this paper, we proposed a learning-based method to find the instruction which CPU is executing. Reverse engineering is an important issue on military, product security analysis, and intellectual property. Our work is a kind of reverse engineering. We can get the information of instruction without direct accessing CPU. While a CPU is running, it would emit EM signal. We collect the EM signal and combine Deep learning model and novel speech processing method to classify it. In our work, firstly, we use Deep learning network based on convolution neural network (CNN). Then, we propose a Tree Structure Network (TSN) and combine label to solve the problems of both data imbalance and Hard Separate Pairs(HSP). Finally, we can classify the EM signal with 61% Top-1 (the strictest) accuracy. To the best of our knowledge, this is the first work that use learning-based method to classify the EM signal.
Proving that Programs Are Differentially Private

We extend recent work in Quantitative Information Flow (QIF) to provide tools for the analysis of programs that aim to implement differentially private mechanisms. We demonstrate how differential privacy can be expressed using loss functions, and how to use this idea in conjunction with a QIF-enabled program semantics to verify differentially private guarantees. Finally we describe how to use this approach experimentally using Kuifje, a recently developed tool for analysing information-flow properties of programs.
Implementation and Evaluation of a Multi-Factor Web Authentication System with Individual Number Card and WebUSB

As the number of Internet users increases, their usage also diversifies, and it is important to prevent Identity on the Internet (Digital Identity) from being violated. Unauthorized authentication is one of the methods to infringe Digital Identity. Multi-factor authentication has been proposed as a method for preventing unauthorized authentication. However, the cryptographic authenticator required for multi-factor authentication is expensive both financially and UX-wise for the user. In this paper, we design, implement and evaluate multi-factor authentication using My Number Card provided by public personal identification service and WebUSB, which is being standardized.
Research on machine learning method and its application technology in intrusion information security detection

With the increasingly serious network security situation, intrusion detection technology has become an important means to ensure network security. Therefore, it has become a consensus to introduce the theory and method of machine learning into intrusion detection, and has made good progress in this research field in recent years. In this paper, a machine learning intrusion detection system is proposed. The system uses the intrusion detection of Elman neural network and the intrusion detection of robust SVM neighbour classification to solve the above problems. Elman neural network intrusion detection uses clustering algorithm to cluster the text of the network packet, which overcomes the defect of missing the text information of the network packet. At the same time, the ability to detect abnormal behaviour between network packet sequences is improved. At the same time, robust SVM neighbour classification intrusion detection can achieve the feature space weighting of the optimal classification face host system log, eliminate the negative impact of noise data, reduce the false alarm rate of intrusion detection, and improve the detection accuracy. Under the requirement of false alarm rate of 0, the intrusion detection based on robust SVM neighbour classification can achieve 87.3% detection rate; when the false alarm rate is 2.8%, the detection rate is 100%.
SynchroMouse

Joint improvised activity and synchrony of movement increases affiliation between people. The mirror game, where two people create joint motion in an improvised pattern, has been used to study different aspects of face-to-face collaboration and synchronization. To explore whether a similar game could be used to study computer-mediated interaction and as an ice-breaker activity to increase affiliation in remote collaboration, we developed a multiplayer online mouse coordination game inspired by the mirror game. The source code to the game is released as free open source software.
Learning and recognition for assistive computer vision

We prove that the 2n-neighbourhood in an n-dimensional digital space is decomposed into the 2(n−1)-neighbourhoods in the mutually orthogonal (n−1)-dimensional digital spaces. This decomposition and construction relation of the neighbourhoods and objects implies that morphological operations in an n-dimensional digital space can be computed as the union of one- and two-dimensional morphological operations on isothetic digital lines and planes intersecting with the digital object in the digital space.
Clustering of longitudinal interval-valued data via mixture distribution under covariance separability

We consider the clustering of repeatedly measured 'min-max' type interval-valued data. We read the data as matrix variate data and assume the covariance matrix is separable for the model-based clustering (M-clustering). The use of a separable covariance matrix introduces several advantages in M-clustering, which include fewer samples required for a valid procedure. In addition, the numerical study shows that this structured matrix allows us to find the correct number of clusters more accurately compared to other commonly assumed covariance matrices. We apply the M-clustering with various covariance structures to clustering the longitudinal blood pressure data from the National Heart, Lung, and Blood Institute Growth and Health Study (NGHS).