Reviewerly/exHarmony huggingface dataset:
Applications of Artificial Intelligence in Live Action Role-Playing Games (LARP)

Live Action Role-Playing (LARP) games and similar experiences are becoming a popular game genre. Here, we discuss how artificial intelligence techniques, particularly those commonly used in AI for Games, could be applied to LARP. We discuss the specific properties of LARP that make it a surprisingly suitable application field, and provide a brief overview of some existing approaches. We then outline several directions where utilizing AI seems beneficial, by both making LARPs easier to organize, and by enhancing the player experience with elements not possible without AI.
Vision: I don’t want to use my Phone! A Cognitive Walkthrough for YubiKeys

Security keys can be used as a second factor when users aim at securing their online accounts or devices in addition to passwords. In this vision paper, we made the first step to identify common issues when registering a YubiKey as second factor on Google, Facebook, Windows 10 and Ubuntu 18.04.4 accounts by a cognitive walkthrough. We found the websites to be too phone-centered and the instructions for operating systems too complex and incomplete at the same time. We therefore plan to analyze further websites to identify the most usable implementation for each step of the registration process, in order to combine those into an improved overall solution.
Leaming Lorenz attractor differential equations using neural network

This article describes usage of artificial neural network (ANN) to predict a chaotic dynamical system. Properly designed and implemented neural network is able to learn the properties of such a system. Using artificial intelligence, it is possible to predict the following values based on current system values with certain accuracy. The ANN is trained to learn the output differentials of Lorenz attractor. The experimental part of this paper compares four different neural network structures that have been applied to investigate this issue.
Efficien Decoding of Lexicographical Rank in Binary Combinatorial Coding

In this paper, we present a method that reduces the decoding complexity of an entropy encoding technique, namely, combinatorial coding, in order to increase compression efficiency without suffering from intolerable decoding latency. Performance of the proposed method is empirically evaluated on randomly generated bit strings for variable lengths and Hamming weights. As a result, it is made possible that combinatorial coding may practically be applied in larger sized blocks, producing higher throughput than the naive implementation.
Implementation of composite DIMSE services

Digital Imaging and Communications in Medicine (DICOM) is the basis for the collaboration and processing of information as well as associated data involving medical imaging. All clinical images that are produced from medical equipment are stored in DICOM format. The DICOM norm defines the different image-related resources that prove to be an advantage for healthcare providers. This specification defines and outlines the services at the protocol level, i.e. the sort of information will be shared with each service at the semantic level, i.e. what each service has been configured with. DICOM service operates with Service Class Provider (SCP) that acts as server and client referred as Service Class User (SCU). In this paper implementation of composite DICOM Message Service Element (DIMSE) services are described. Verification service, storage service and query/retrieve services are implemented using object oriented language c# and corresponding logs are collected to verify the implementation.
Multi-label learning embedding approach based on multi-temporal spectral signature for hyperspectral images classification

Currently, Hyperspectral signal processing is a crucial area of research. Respectively, various techniques have been investigated to apprehend features combination and multi-label classification issues. Indeed, significant consideration has been given to approaches supporting the use of a single type of features. Moreover, few efforts have been dedicated to model the multi-label aspect of hyperspectral pixels and to integrate simultaneously divergent kinds of interdependent features. In this paper, we propose a novel embedding multi-label learning approach integrating complementary weighted features. The proposed framework combines the singular statistical characteristics of each feature to accomplish a physically meaningful cooperative low-dimensional representation of extracted features. This will grant, in one hand, the refinement of classification process and the propagation of narrow class information to unlabeled sample, in the other hand, when only partial labeling knowledge is available. This paper makes the following contributions: (i) the extraction of multi-view features based on the 3D model of the spectral signature and (ii) an embedding multi-label based approach by better tackling unbalanced and dimensionality issues. A set of complementary spatial/spectral features is extracted in the experimental section from a series of hyperspectral images. The obtained results reflect the efficiency of the proposed classification schema while maintaining a reasonable computational complexity.
P2P Energy Trading based on Blockchain and Customer Risk Preference

In this paper, we study a local P2P energy trading with help of Blockchain technologies and consideration for customers’ risk preference. The local energy transactions are based on double-side auction mechanism, meanwhile enabling immediate seller-buyer pairing process via iterative price adjustment. Additionally, the quantitative description of risk preference guarantees the optimal decision-making according to customers’ subjective gain reference. The Blockchain platform is also used to support the proposed P2P energy trading mechanism, strengthening the decentralized implementation and smart contract deployment. The demonstration is provided using Ethereum and Remix development environment.
Mandarin Singing Synthesis Based on Generative Adversarial Network

This paper proposed a method for statistical parametric singing synthesis incorporating GAN (Generative Adversarial Network) that trained acoustic model. In GAN, the acoustic model was trained to minimize the weighted sum of the conventional minimum generation loss and adversarial loss, which was minimizing the distance between the natural and generated samples parameter, thus effectively solved the problem of over-smoothing. In the experimental part, we established a singing voice corpus with 60 songs and divided them that have been recorded and labeled into about 1000 sentences, of which 950 sentences were for training model. Comparing the generated songs of the method proposed in this paper and HMM, through 10 people MOS scores, the score of the former was 3.12 that was better than the latter of 2.81.
Fuzzy Control of Exploration and Exploitation Trade-Off with On-Line Convergence Rate Estimation in Evolutionary Algorithms

In this paper, the fuzzy control of exploration and exploitation trade-off with on-line convergence rate estimation in evolutionary algorithms is presented. We introduce to the proposed algorithm three fuzzy systems (Mamdani type-1). These fuzzy systems are responsible for controlling the parameters of an evolutionary algorithm, such as selection pressure, crossover probability and mutation probability. While creating the fuzzy rules in the proposed fuzzy systems, we assumed that, at the start, the algorithm should possess maximal exploration property (low selection pressure, high mutation probability, and high crossover probability), while at the end, the algorithm should possesses maximal exploitation property (high selection pressure, low mutation probability, and low crossover probability). Also, in the paper we propose a method for estimating the algorithm convergence rate value. The proposed approach is verified using test functions chosen from literature. The results obtained using the proposed method are compared with the results obtained using evolutionary algorithms with a different selection operator, and standard values for crossover and mutation probability.
Modelling of Flexible Manipulator System Using Flower Pollination Algorithm

The study of the flexible manipulator system (FMS) has attracted many researchers due to its superiority of light weight and faster system response. Flexible manipulator system is an improvement from its rigid structure, however it can be easily vibrated when it subjected to disturbance. If the advantages of FMS are not to be sacrificed, an accurate model and efficient control system must be developed. Thus, this study presents an approach of evolutionary swarm algorithm via flower pollination algorithm (FPA) to model the dynamic system of flexible manipulator structure. An experimental rig of flexible manipulator system was developed for input-output acquisition. Then, this input-output data was fed to system identification method to obtain a dynamic model of flexible manipulator system utilizing evolutionary algorithm with linear auto regressive with exogenous (ARX) model structure. The result obtained through flower pollination algorithm was then compared with conventional method known as least square (LS) algorithm in terms of mean square error (MSE), correlation test and pole-zero diagram. The best MSE achieved by LS modeling for endpoint acceleration and hub angle positioning are 0.0075 and 0.0028, respectively. While, the best MSE produced by flower pollination algorithm for endpoint acceleration and hub angle positioning are 0.0063 and 0.0020, respectively. It is reveals that the performance of intelligence algorithm is superior than conventional algorithm.
Analysis of Social Design Projects Based on Krippendorff’s Four Pillars of HCD

Design is recognized as a discipline capable of transmitting information through different types of artifacts (material or immaterial), in order to influence the emotions, behaviors and attitudes of the recipients and users. Aiming at efficient communication, the Human-Centered Design (HCD) approach incorporates methods of anthropology and participatory design, concerning itself with the way that the recipients see, interpret and live with the created artifacts. In view of this, this article seeks to describe and analyze two projects developed with design interventions, which sought to communicate and transmit knowledge about relevant social issues. The first is from the Passport to Employability program, which consists of a training course for inmates to assist them in their social reintegration through various activities as well as a toolkit. The second case is the Late Adoption project, which has as its main objective sensitizing society about adoptive affiliation, especially of groups considered difficult to place, such as children over five years old and adolescents, those who have a disability or groups of siblings. When analyzing the two projects from the perspective of HCD, similarities were perceived, such as the need to work in an interdisciplinary way and the importance of making complex information accessible to all stakeholders. In this sense, design can play an extremely significant role as an information mediator between different areas of knowledge and the users or audience of the developed artifact.
Risk Monitoring Services of Discharged SARS-CoV-2 Patients

In the latest months, the outbreak of SARS-CoV-2 has forced worldwide healthcare systems to rethink their organisation. In this landscape, particular attention has been devoted to discharged patients. Remote monitoring on patients' health status is used, through dedicated web platforms and apps, to check home rehabilitation progress and, at the same time, promptly notify the arise of anomalies. Nevertheless, the variety of patients and the large volume of collected data call for models, tools and methods for data representation and exploration, in order to focus on relevant groups of patients only. Given our previous research efforts in the Big Data exploration field, we designed a Risk Monitoring Services ecosystem, devoted to support doctors (e.g., medical researchers, clinicians, analysts) in the analysis of data collected through app by: (i) identifying groups of SARS-CoV-2 discharged patients, built according to features such as sex, age, co-morbidities, prior therapies; (ii) monitoring the health status of patients, by extracting snapshots of patients' health parameters measurements, evolving over time, and comparing them with baseline or reference values within the same patients group; (iii) promptly notifying doctors when some measurements diverge from reference values for a group of patients.
Learning from Multiple Graphs of Student and Book Interactions for Campus Book Recommendation

The development of university digitalization has generated a large amount of student data, such as academic performance records, book loaning records, etc. This provides a data foundation for many student-related tasks, such as book recommendations, academic performance predictions. Campus book recommendation is very important, which can not only help students to find books they might be interested in but also improve the utilization of book resources in campus libraries. Existing work proved the effectiveness of incorporating pairwise relationships of users and items, i.e., social network and item dependency graph. However, most of them model item dependency based on co-occurrence and ignore occurrence order between items which is crucial in the campus book recommendation. In this paper, we propose to construct multiple graphs including a student-student graph, a student-book interaction graph, and a book-book graph to simultaneously capture student correlation, student-book interaction, and book dependency. We design a graph-based convolution network that contains three modules with different convolution operations to learn representations for students and books from the three graphs, and a prediction module to make recommendations. Experiments on the real-world dataset demonstrate the effectiveness of the proposed method.
Correlation Filter-based Object Tracking Algorithms

Object tracking is one of the most important tasks in computer vision. It is widely used in traffic monitoring, robotics, automatic vehicle tracking and the like. Discriminant tracking method based on correlation filtering theory has made a series of new progress due to its high efficiency and robustness. Basic algorithms, improved algorithms and algorithms combined deep learning on correlation filter-based object tracking are studied in this paper. Color-based, scale-based, part-based, and bound effect-based are included in these algorithms. Despite the broad application prospects of correlation filter in the field of object tracking, it is still a very challenging for research direction due to complex scenes and the object factors. 32 representative algorithms are compared on the OTB2013 and OTB100 datasets, experiment results show that the algorithm adopted by multiple features combination has better accuracy and higher success rate in the face of occlusion or position error.
The influence of positive or negative impacts on information behavior

Abstract In recent progress in information behavior research, people have been at the center of investigations. Thomas Wilson's popular general model of information behavior has evolved from the growing realization that information behavior occurs across many contexts. The current proposal suggests adding the influence of impacts to Wilson's general model of information processing and use. By accounting for past experiences with information sources, information scientists are better able to tackle society's grand challenges.
MiD: Enabling Integration between Web Systems

Technological solutions are increasingly present in everyday corporate environments. These solutions assist in a variety of activities that are of paramount importance to your employees, customers and partners. However, as demands grow, the complexity of business needs increases, and as a result, technology systems need to evolve to maintain the standard of service quality. In this sense, the integration between systems is an alternative to help in this evolutionary scenario, enabling the sharing of resources between different systems, contemplating interoperability, reducing unwanted redundancies and eliminating bottlenecks and incompatibilities. However, despite the advantages and benefits, there are some inherent difficulties to achieve integration, which are: the effort spent on development, the integration process with Legacy Systems, the risks associated with software modifications and the integration with software developed by third parties. Because of this, we propose an approach and a tool, called MiD, to overcome such difficulties inherent in building Web Services. MiD simplifies the development of Web Services for integration between Web systems, enabling integration without the need for access to target system source code and low cost development and maintenance. An evaluation was performed to verify the advantages of using MiD in a real integration project. Preliminary results show that for the context used in the assessment environment, MiD has the potential to reduce effort in developing Web Services for integration.
Exploiting Interference-aware GPU Container Concurrency Learning from Resource Usage of Application Execution

The advent of GPGPU (General-Purpose Graphic Processing Unit) containers enlarges opportunities of acceleration and easy-to-use in clouds. However, there is still lack of research on utilizing efficiently GPU resource and managing multiple applications at the same time. Co-execution of applications without understanding applications' execution characteristics may result in low performance caused by their interference problems. To solve the problem, this paper defines resource metrics that causes performance degradation when sharing resource. We calculate the degree of interference during concurrent execution of multi applications using a ML (Machine Learning) method with the metrics. The experiments show that the execution of interference aware groups improves 7% in execution time compared to non-interference aware group in overall. For a workload consisting of several applications, the overall performance was improved by 18% and 25%, respectively, when compared to SJF and random.
Existing Methods of Digital Watermarking

This chapter depicts the overall information about the various algorithms and techniques available for digital watermarking and it also covers the emerging issues in the existing algorithms. This chapter contains the working strategy of different spatial domain and frequency domain techniques like DWT (discrete wavelength transform), DCT (discrete cosine transform), SVD (singular vector decomposition), and LSB (least significant bit). In this chapter, aside from giving more attention just to those papers, which are identified with this proposal work, the mind has been taken to cover increasingly up and coming ideas. After that, the deficiencies and the open doors for the exploration work are recognized and in view of those, inquiries about issues are produced by giving appropriate supports.
Multi-Dimensional Randomized Response

In our data world, a host of not necessarily trusted controllers gather data on individual subjects. To preserve her privacy and, more generally, her informational self-determination, the individual has to be empowered by giving her agency on her own data. Maximum agency is afforded by local anonymization, that allows each individual to anonymize her own data before handing them to the data controller. Randomized response (RR) is a local anonymization approach able to yield multi-dimensional full sets of anonymized microdata that are valid for exploratory analysis and machine learning. This is so because an unbiased estimate of the distribution of the true data of individuals can be obtained from their pooled randomized data. Furthermore, RR offers rigorous privacy guarantees. The main weakness of RR is the curse of dimensionality when applied to several attributes: as the number of attributes grows, the accuracy of the estimated true data distribution quickly degrades. We propose several complementary approaches to mitigate the dimensionality problem. First, we present two basic protocols, separate RR on each attribute and joint RR for all attributes, and discuss their limitations. Then we introduce an algorithm to form clusters of attributes so that attributes in different clusters can be viewed as independent and joint RR can be performed within each cluster. After that, we introduce an adjustment algorithm for the randomized data set that repairs some of the accuracy loss due to assuming independence between attributes when using RR separately on each attribute or due to assuming independence between clusters in cluster-wise RR. We also present empirical work to illustrate the proposed methods.
Machine Learning in Medical Image Processing

Machine Learning is the understanding of some rules or algorithms by the machine. It is the process of providing scientific algorithm and statistical model to computer system which utilizes it to perform specific task without using explicit instructions. Image processing is the method used for quantitative analysis of digital image data by certain algorithm. With advancement in medical science, there are many sophisticated methods for medical imaging and some of them involve the use of machine learning. Machine learning employed on image processing of diseased images can help in earlier detection and diagnosis of developing chronic diseases.
Rule Based Distributed Data-Service Management

In this work, a rule-based data management system is proposed for heterogenous cloud data service architectures. For the management of data, many web services and an overarching control mechanism that communicates with these services quickly and accurately to obtain a reliable system are required. Hence, the proposed rule-based system communicates with the web services regularly and processes the designated rules using a multi-layer queuing model for scheduling. The data service architecture consists of three main parts: user management, rule management, and rule processing. These parts contain data services that require high security, the system safety is provided with a two-level protection mechanism namely authentication and authorization. Scheduling system consists of queues where jobs are inserted to be processed. The proposed system also considers the interdependence of sub-jobs. The queue system is created to use a priority queue. A custom Graduate School Administrative Board Decisions Management System (GSBS) is designed and implemented to test and verify the limits of the proposed system. The proposed multi-layer queuing approach is then compared with the commonly used FIFO approach. Test results demonstrate that the proposed Rule Based Distributed Data-Service (RBDDS) is efficient in terms of scalability, response time and throughput metrics.