Reviewerly/exHarmony huggingface dataset:
Network of Games: An Ecology of Games Informing Integral and Inclusive City Developments

This article analyzes possibilities for connecting individual city games for building a network of games working together. City gaming works along with the understanding that cities are self-organizing systems, influenced by multiple bottom-up and top-down actors with varying interests and powers. Affordable housing, climate adaptation, or area development are examples of urgent urban challenges city games typically focus on. The assumption is that if these specialized games could be linked, then a large game infrastructure built as a modular system, can offer various game combinations responding to urban challenges in an integral and holistic way. To test a working game network, city games, models, and digital apps have been linked through their shared datasets as well as game interfaces. Two city experiments have been conducted in two Dutch cities—Amsterdam and Breda—which enabled the testing to function as “constructive design research.” In Amsterdam (Klimaatspel) two separate city games were connected through their datasets, while in Breda (Play the Koepel) datasets and interfaces merged to create a new game. Used data models are the Energy Transition Model developed by Quintel and the urban plan cost simulator software of Urban Reality. Used game interfaces (digital and analog) include the Typeform, the Network of Games app, the Urban Reality simulator, and the Play the City table-top game format. The testing considered two different approaches for a potential game network. The first option assumes an all-encompassing digital app, reformatting and involving various games and models in a single interface. The second option is an open approach that looks to link custom-made games with existing interfaces. The second option allows both simultaneous and sequential linking. Two experiments utilizing sequential and simultaneous integration of diverse digital tools suggest that a collection of interfaces connecting to each other throughout the entire process from a digital poll to an app, a simulator or a webinar, or analog game sessions is more effective than a single mobile phone app for all potential game interactions. Considering city games as an ecology of city tools that can be linked to one another becomes through this study a concrete goal to reach. Through combining specialized games, addressing complex city challenges becomes possible. This step enables a more effective participation environment for diverse experts and non-experts.
Rule-ranking method based on item utility in adaptive rule model

Decision-making is an important part of most human activities regardless of their daily activities, profession, or political inclination. Some decisions are relatively simple specifically when the consequences are insignificant while others can be very complex and have significant effects. Real-life decision problems generally involve several conflicting points of view (criteria) needed to be considered and this is the reason recent decision-making processes are usually supported by data as indicated by different data mining techniques. Data mining is the process of extracting data to obtain useful information and a promising and widely applied method is association rule mining which has the ability to identify interesting relationships between sets of items in a dataset and predict the associative behavior for new data. However, the number of rules generated in association rules can be very large, thereby making the exploitation process difficult. This means it is necessary to prioritize the selection of more valuable and relevant rules.Therefore, this study proposes a method to rank rules based on the lift ratio value calculated from the frequency and utility of the item. The three main functions in proposed method are mining of association rules from different databases (in terms of sources, characteristics, and attributes), automatic threshold value determination process, and prioritization of the rules produced.Experiments conducted on six datasets showed that the number of rules generated by the adaptive rule model is higher and sorted from the largest lift ratio value compared to the apriori algorithm.
Management and Monitoring Security Events in a Business Organization - SIEM system

Business organizations are increasingly facing advanced threats, which have been particularly affected by new circumstances such as remote work. In such circumstances, members of IT security need to have appropriate systems that are ready to respond to this new security threats. In order to successfully manage and monitor security events and incidents, it is necessary to establish a Security Operations Center (SOC) or Security Information and Event Management (SIEM) system. This paper gives an example of monitoring Security Events by Tier 1 and Tier 2 level support in installed SIEM system in a business organization.
61‐2: <i>Invited Paper:</i> ShyTech Displays High resolution Displays Hidden Behind Decorative Surfaces

ShyTech is the new automotive interior smart home living‐style oriented design direction. It is a consistent alternative approach to giant display consoles. Key intention is to reduce a potential information overload by providing content only when and where necessary (on‐demand principle). This paper introduces design opportunities, use cases, challenges and technical solutions for premium image quality, natural‐looking and genuine‐feeling surface textures, daylight readability and postcard effect avoidance.
Weight pruning-UNet: Weight pruning UNet with depth-wise separable convolutions for semantic segmentation of kidney tumors

Accurate semantic segmentation of kidney tumors in computed tomography (CT) images is difficult because tumors feature varied forms and occasionally, look alike. The KiTs19 challenge sets the groundwork for future advances in kidney tumor segmentation.We present weight pruning (WP)-UNet, a deep network model that is lightweight with a small scale; it involves few parameters with a quick assumption time and a low floating-point computational complexity.We trained and evaluated the model with CT images from 210 patients. The findings implied the dominance of our method on the training Dice score (0.98) for the kidney tumor region. The proposed model only uses 1,297,441 parameters and 7.2e floating-point operations, three times lower than those for other network models.The results confirm that the proposed architecture is smaller than that of UNet, involves less computational complexity, and yields good accuracy, indicating its potential applicability in kidney tumor imaging.
Application of geostatistical techniques to assess groundwater quality in the Lower Anayari catchment in Ghana

In this study, integrated geostatistical techniques were applied to appraise the groundwater quality for various purposes in the Lower Anayari Catchment (LAC), Ghana. Physico-chemical and trace metals were analysed for 41 groundwater samples. Groundwater in LAC was generally found to be moderately hard to fresh and the information entropy-based groundwater quality index (IEGQI) ranked approximately 80.49% of the groundwater as excellent. Partial Least square regression analysis suggests that PO43−, As, Cd and Fe are important parameters for the prediction of IEGQI values. The irrigation water quality index (IWQI) suggest that 2.44% of groundwater in LAC is suitable for moderate permeability soils, 65.85% for moderate to high permeability soils and 31.71% for moderate to high salt tolerance plants. The LSI and RSI indices rate groundwater in LAC to be aggressive water with a high probability of being corrosive to metals, depending on the levels of dissolved oxygen present.
Multiple Object Tracking for Football Game Analysis

We consider the problem of tracking football players in video captured from one side of the football field. The task is challenging due to frequent occlusions of players, varying size of players’ projections, changing illumination and similar appearance of players of the same team. We approach the problem using the tracking-by-detection paradigm, where an object detector is applied to individual video frames and the tracker tries to associate the detector responses and form the trajectories by using some motion model. We provide experiments using a classic object detector based on background modeling as well as a deep learning based detector.
DWT based blind and robust watermarking technique using ABCD map for medical image

The purpose of this paper is to present an imperceptible and robust blind watermarking technique with high embedding capacity for medical images based on 5/3 integer discrete wavelet transform. Watermark embedding locations are determined using a new feature named ABCD map and Fibonacci numbers of the transformed cover medical image. The Arnold transform is used to scramble watermark image in order to provide extra security. The experiment results approve that the proposed medical image watermarking approach not only meet the need of imperceptibility in the medical field, but also has better or similar robustness as compared with other related watermarking approachs
Comparing AWS Streaming Services: A Use Case on ECG Data Streams

The goal of this research paper is to compare Amazon Kinesis Data Streams and SQS via a practical use case for processing electrocardiogram medical data. Both services are tested on a large number of concurrent data streams up to 10.000 concurrent data streams, measuring and evaluating their performance. In both cases, the producer is a programmed data generator that supplies the data to the services. The motivation of the paper is the streaming of textual files with 125-500 samples per second written as integers, representing electrocardiograms from sensors. We set a research hypothesis that Amazon Kinesis will outperform SQS service for streaming medical data collected from sensors. The pros and cons are listed for each of the Amazon services taking into account the price, use cases when each of the services will be more applicable, and the scalability. The concluding results from this experiment are that both of the services provide great performance but they will mainly provide better results if each of them is applied to a specific use case.
SGXGauge: A Comprehensive Benchmark Suite for Intel SGX

Trusted execution environments (TEEs) such as Intel SGX facilitate the secure execution of an application on untrusted machines. A plethora of work focuses on improving the performance of such environments necessitating the need for a standard, widely accepted benchmark suite. We present SGXGauge, a benchmark suite for SGX containing a diverse set of workloads from different domains. We also thoroughly characterize the behavior of the benchmark suite on a native platform and on a platform that uses a library OS-based shim layer (GrapheneSGX).
Reclaiming attention

research-article Open Access Share on Reclaiming attention: Christianity and HCI Authors: Alexis Hiniker University of Washington University of WashingtonSearch about this author , Jacob O. Wobbrock University of Washington University of WashingtonView Profile Authors Info & Claims InteractionsVolume 29Issue 4July - August 2022 pp 40–44https://doi.org/10.1145/3538706Online:29 June 2022Publication History 0citation652DownloadsMetricsTotal Citations0Total Downloads652Last 12 Months652Last 6 weeks77 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteView all FormatsPDF
CVDP k-means clustering algorithm for differential privacy based on coefficient of variation

Data clustering has been applied and developed in all walks of life, which can provide convenience for enterprise service optimization. However, when the original data to be analyzed contains users’ personal privacy information, the clustering analysis process of the data holder may expose users’ privacy. Differential privacy k-means algorithm is a clustering method based on differential privacy protection technology, which can solve the privacy disclosure problem in the process of data clustering. In the differential privacy k-means algorithm, Laplacian noise controlled by privacy parameter ɛ is added to the center point of clustering to protect user sensitive information and clustering results in the original data, but the addition of noise will affect the utility of clustering. In order to balance the availability and privacy of the differential privacy k-means clustering algorithm, the research on the improvement of the algorithm pays more attention to the selection of the initial clustering center or the optimization of the outlier processing, but does not consider the different contribution degree of each dimension data to the clustering. Therefore, this paper proposes a differential privacy CVDP k-means clustering algorithm based on coefficient of variation. The CVDP scheme first eliminates outliers in the original data through data density, and then designs weighted data point similarity calculation method and initial centroid selection method using variation coefficient. Experimental results show that CVDP k-means algorithm has some improvements in availability, performance and privacy.
A Unified Knowledge Extraction Method Based on BERT and Handshaking Tagging Scheme

In the actual knowledge extraction system, different applications have different entity classes and relationship schema, so the generalization and migration ability of knowledge extraction are very important. By training a knowledge extraction model in the source domain and applying the model to an arbitrary target domain directly, open domain knowledge extraction technology becomes crucial to mitigate the generalization and migration ability issues. Traditional knowledge extraction models cannot be directly transferred to new domains and also cannot extract undefined relation types. In order to deal with the above issues, in this paper, we proposed an end-to-end Chinese open-domain knowledge extraction model, TPORE (Extract Open-domain Relations through Token Pair linking), which combined BERT with a handshaking tagging scheme. TPORE can alleviate the nested entities and nested relations issues. Additionally, a new loss function that conducts a pairwise comparison of target category score and non-target category score to automatically balance the weight was adopted, and the experiment results indicate that the loss function can bring speed and performance improvements. The extensive experiments demonstrate that the proposed method can significantly surpass strong baselines. Specifically, our approach can achieve new state-of-the-art Chinese open Relation Extraction (ORE) benchmarks (COER and SAOKE). In the COER dataset, F1 increased from 66.36% to 79.63%, and in the SpanSAOKE dataset, F1 increased from 46.0% to 54.91%. In the medical domain, our method can obtain close performance compared with the SOTA method in the CMeIE and CMeEE datasets.
A new multi-document summarisation approach using saplings growing-up optimisation algorithms: Simultaneously optimised coverage and diversity

Automatic text summarisation is obtaining a subset that accurately represents the main text. A quality summary should contain the maximum amount of information while avoiding redundant information. Redundancy is a severe deficiency that causes unnecessary repetition of information within sentences and should not occur in summarisation studies. Although many optimisation-based text summarisation methods have been proposed in recent years, there exists a lack of research on the simultaneous optimisation of scope and redundancy. In this context, this study presents an approach in which maximum coverage and minimum redundancy, which form the two key features of a rich summary, are modelled as optimisation targets. In optimisation-based text summarisation studies, different conflicting objectives are generally weighted or formulated and transformed into single-objective problems. However, this transformation can directly affect the quality of the solution. In this study, the optimisation goals are met simultaneously without transformation or formulation. In addition, the multi-objective saplings growing-up algorithm (MO-SGuA) is implemented and modified for text summarisation. The presented approach, called Pareto optimal, achieves an optimal solution with simultaneous optimisation. Experimentation with the MO-SGuA method was tested using open-access (document understanding conference; DUC) data sets. Performance success of the MO-SGuA approach was calculated using the recall-oriented understudy for gisting evaluation (ROUGE) metrics and then compared with the competitive practices used in the literature. Testing achieved a 26.6% summarisation result for the ROUGE-2 metric and 65.96% for ROUGE-L, which represents an improvement of 11.17% and 20.54%, respectively. The experimental results showed that good-quality summaries were achieved using the proposed approach.
Outsourcing MPC Precomputation for Location Privacy

Proximity testing is at the core of sev-eral Location-Based Services (LBS) offered by, e.g., Uber, Facebook, and BlaBlaCar, as it determines closeness to a target. Unfortunately, modern LBS demand not only that clients disclose their locations in plain, but also to trust that the services will not abuse this information. These requirements are unfounded as there are ways to perform proximity testing without revealing one's location. We propose POLAR, a protocol that imple-ments privacy-preserving proximity testing for LBS. POLAR is suitable for clients running mo-bile devices, and relies on a careful combination of three well-established multiparty computation protocols and lightweight cryptography. A point of originality is the inclusion of two servers into the proximity testing. The servers may aid multiple pairs of clients and contribute towards enhancing privacy, improving efficiency, and reducing the run-ning time of clients' procedures.
User Story Quality in Practice: A Case Study

(1) Background: User stories are widely used in Agile development as requirements. However, few studies have assessed the quality of user stories in practice. (2) Methods: What is the quality of user stories in practice? To answer the research question, we conducted a case study. We used an analysis report from a real-life project where an organization wanted to improve its existing hotline system or acquire a new one. We invited IT practitioners to write requirements for the new system based on the analysis report, user stories, and whatever else they considered necessary. The practitioners could ask the authors questions as they would ask a customer in a real setting. We evaluated the practitioners’ replies using these IEEE 830 quality criteria: completeness, correctness, verifiability, and traceability. (3) Results: The replies covered only 33% of the needs and wishes in the analysis report. Further, the replies largely missed other requirements needed in most projects, such as learnability and maintainability. Incorrect or restrictive solutions were often proposed by the practitioners. Most replies included user stories that were hard to verify, or would have caused a cumbersome user interface if implemented independently. (4) Conclusion: In this project, relying on the user stories would have been a disaster. Although the user stories could have been improved, they wouldn’t cover the necessary requirements in the project.
A Multi-Strategy Adaptive Comprehensive Learning PSO Algorithm and Its Application

In this paper, a multi-strategy adaptive comprehensive learning particle swarm optimization algorithm is proposed by introducing the comprehensive learning, multi-population parallel, and parameter adaptation. In the proposed algorithm, a multi-population parallel strategy is designed to improve population diversity and accelerate convergence. The population particle exchange and mutation are realized to ensure information sharing among the particles. Then, the global optimal value is added to velocity update to design a new velocity update strategy for improving the local search ability. The comprehensive learning strategy is employed to construct learning samples, so as to effectively promote the information exchange and avoid falling into local extrema. By linearly changing the learning factors, a new factor adjustment strategy is developed to enhance the global search ability, and a new adaptive inertia weight-adjustment strategy based on an S-shaped decreasing function is developed to balance the search ability. Finally, some benchmark functions and the parameter optimization of photovoltaics are selected. The proposed algorithm obtains the best performance on 6 out of 10 functions. The results show that the proposed algorithm has greatly improved diversity, solution accuracy, and search ability compared with some variants of particle swarm optimization and other algorithms. It provides a more effective parameter combination for the complex engineering problem of photovoltaics, so as to improve the energy conversion efficiency.
AMBF-RL: A real-time simulation based Reinforcement Learning toolkit for Medical Robotics

Recently, Reinforcement Learning (RL) techniques have seen significant progress in the robotics domain. This can be attributed to robust simulation frameworks that offer realistic environments to train. However, there is a lack of platforms which offer environments that are conducive to medical robotic tasks. Having earlier designed the Asynchronous Multibody Framework (AMBF) - a real-time dynamics simulator well-suited for medical robotics tasks, we propose an open source AMBF-RL (ARL) toolkit to assist in designing control algorithms for these robots, as well as a module to collect and parse expert demonstration data. We validate ARL by attempting to partially automate the task of debris removal on the da Vinci Research Kit (dVRK) Patient Side Manipulator (PSM) in simulation by calculating the optimal policy using both Deep Deterministic Policy Gradient (DDPG) and Hindsight Experience Replay (HER) with DDPG. The trained policies are successfully transferred onto the physical dVRK PSM and tested. Finally, we draw a conclusion from the results and discuss our observations of the experiments conducted.
High-speed liquid crystal display simulation using parallel reservoir computing approach

Abstract The performance of liquid crystal displays (LCDs) is expected to be improved further with the emergence of their new applications. Numerical simulations such as the finite differential method (FDM) and the finite element method are useful in optimum design. However, they take a long time because dynamical systems in LCDs are nonlinear multiphysics composed of electromagnetism, fluid dynamics, and elastic mechanics. A machine learning method is one of the solutions to reduce computational cost. In this paper, we have extended the parallel reservoir computing framework and applied it to LCD simulation. We have discussed how to implement each natural feature of liquid crystal cells, namely, non-autonomy, multiphysics and long-range orientational order, into the framework of parallel reservoir computing. Sufficient higher accuracy was obtained with several display patterns and driving frequencies at computational speeds more than 100 times higher than FDM.