Reviewerly/exHarmony huggingface dataset:
Developing an AI-assisted clinical decision support system to enhance in-patient holistic health care

Holistic health care (HHC) is a synonym for complete patient care, and as such an efficient clinical decision support system (CDSS) for HHC is critical to support the judgement of physician’s decision in response of patient’s physical, emotional, social, economic, and spiritual needs. The field of artificial intelligence (AI) has evolved considerably in the past decades and many AI applications have been deployed in various contexts. Therefore, this study aims to propose an AI-assisted CDSS model that predicts patients in need of HHC and applies an improved recurrent neural network (RNN) model, long short-term memory (LSTM) for the prediction. The data sources include in-patient’s comorbidity status and daily vital sign attributes such as blood pressure, heart rate, oxygen prescription, etc. A two-year dataset consisting of 121 thousand anonymized patient cases with 890 thousand physiological medical records was obtained from a medical center in Taiwan for system evaluation. Comparing with the rule-based expert system, the proposed AI-assisted CDSS improves sensitivity from 26.44% to 80.84% and specificity from 99.23% to 99.95%. The experimental results demonstrate that an AI-assisted CDSS could efficiently predict HHC patients.
WiCAM: Imperceptible Adversarial Attack on Deep Learning based WiFi Sensing

With the popularization of deep learning models in wireless sensing, researchers have made considerable efforts to construct sophisticated models to improve the accuracy of related applications. But very few studies have addressed the potential vulnerabilities of deep models, and existing works evaluate wireless adversarial performance only in communication or sensing. None of them has a comprehensive definition of attack imperceptibility. In this paper, we come up with a definition of the wireless attack imperceptibility for both communication and sensing. Our goal is to craft an adversarial perturbation, which can degrade the performance of WiFi sensing without compromising WiFi communication. To achieve this goal, we propose WiCAM to reveal the temporal and spatial attention of a DNN, capturing the crucial portions of its input. Then we design a mask to limit adversarial perturbation in the attended parts only, and thus the impact of the attack on WiFi communication is minimized. WiCAM is a general adversarial framework that can integrate existing adversarial methods such as FGSM and PGD to generate perturbations. We carry out experiments on three popular WiFi sensing applications, including human activity recognition, gesture recognition, and user identification. Extensive experiments are conducted on both public datasets and self-collected datasets. The results show that when declining the accuracy of a target model below 50%, WiCAM can reduce the impact on communication in terms of BER by up to 77.78% in QAM-64, compared to the common adversarial methods.
Adaptive Artificial Personalities

chapter Share on Adaptive Artificial Personalities Authors: Kathrin Janowski View Profile , Hannes Ritschel View Profile , Elisabeth André View Profile Authors Info & Claims The Handbook on Socially Interactive Agents: 20 years of Research on Embodied Conversational Agents, Intelligent Virtual Agents, and Social Robotics Volume 2: Interactivity, Platforms, ApplicationOctober 2022 Pages 155–194https://doi.org/10.1145/3563659.3563666Published:03 November 2022Publication History 6citation3DownloadsMetricsTotal Citations6Total Downloads3Last 12 Months3Last 6 weeks1 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 AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
STRIDE‐based threat modeling and DREAD evaluation for the distributed control system in the oil refinery

Industrial control systems (ICSs) used to be operated in closed networks, that is, separated physically from the Internet and corporate networks, and independent protocols were used for each manufacturer. Thus, their operation was relatively safe from cyberattacks. However, with advances in recent technologies, such as big data and internet of things, companies have been trying to use data generated from the ICS environment to improve production yield and minimize process downtime. Thus, ICSs are being connected to the internet or corporate networks. These changes have increased the frequency of attacks on ICSs. Despite this increased cybersecurity risk, research on ICS security remains insufficient. In this paper, we analyze threats in detail using STRIDE threat analysis modeling and DREAD evaluation for distributed control systems, a type of ICSs, based on our work experience as cybersecurity specialists at a refinery. Furthermore, we verify the validity of threats identified using STRIDE through case studies of major ICS cybersecurity incidents: Stuxnet, BlackEnergy 3, and Triton. Finally, we present countermeasures and strategies to improve risk assessment of identified threats.
The German COVID-19 Digital Contact Tracing App: A Socioeconomic Evaluation

The COVID-19 pandemic posed challenges to governments in terms of contact tracing. Like many other countries, Germany introduced a mobile-phone-based digital contact tracing solution ("Corona Warn App"; CWA) in June 2020. At the time of its release, however, it was hard to assess how effective such a solution would be, and a political and societal debate arose regarding its efficiency, also in light of its high costs. This study aimed to analyze the effectiveness of the CWA, considering prevented infections, hospitalizations, intensive care treatments, and deaths. In addition, its efficiency was to be assessed from a monetary point of view, and factors with a significant influence on the effectiveness and efficiency of the CWA were to be determined. Mathematical and statistical modeling was used to calculate infection cases prevented by the CWA, along with the numbers of prevented complications (hospitalizations, intensive care treatments, deaths) using publicly available CWA download numbers and incidences over time. The monetized benefits of these prevented cases were quantified and offset against the costs incurred. Sensitivity analysis was used to identify factors critically influencing these parameters. Between June 2020 and April 2022, the CWA prevented 1.41 million infections, 17,200 hospitalizations, 4600 intensive care treatments, and 7200 deaths. After offsetting costs and benefits, the CWA had a net present value of EUR 765 m in April 2022. Both the effectiveness and efficiency of the CWA are decisively and disproportionately positively influenced by the highest possible adoption rate among the population and a high rate of positive infection test results shared via the CWA.
The optimal asset trading settlement based on Proof-of-Stake blockchains

The Proof-of-Stake (PoS) protocol is booming in blockchain networks because of excessive energy consumption and slow block generation associated with the Proof-of-Work (PoW) protocol. In terms of transaction settlement, does the PoS protocol perform better than PoW? We build a transaction settlement model based on the PoS blockchain, describing how the staking income and costs affect settlement performance, and put forward a Successful Settlement Factor (SSF) to determine whether the settlement will be successful given the block size and block time. In addition, the system developers can adjust block size and block time according to SSF to achieve the best settlement performance. Finally, using historical data for a real-world blockchain network, our model demonstrates that the optimal block time, average settlement time, average settlement fees, and total settlement lag of the PoS blockchain are 23.41%, 23.29%, 23.23%, 23.31%, respectively, less than that of the PoW blockchain.
Galvatron

Transformer models have achieved state-of-the-art performance on various domains of applications and gradually becomes the foundations of the advanced large deep learning (DL) models. However, how to train these models over multiple GPUs efficiently is still challenging due to a large number of parallelism choices. Existing DL systems either rely on manual efforts to make distributed training plans or apply parallelism combinations within a very limited search space. In this approach, we propose Galvatron, a new system framework that incorporates multiple popular parallelism dimensions and automatically finds the most efficient hybrid parallelism strategy. To better explore such a rarely huge search space, we 1) involve a decision tree to make decomposition and pruning based on some reasonable intuitions, and then 2) design a dynamic programming search algorithm to generate the optimal plan. Evaluations on four representative Transformer workloads show that Galvatron could perform automatically distributed training with different GPU memory budgets. Among all evaluated scenarios, Galvatron always achieves superior system throughput compared to previous work with limited parallelism.
Understanding the impact of digital contact tracing during the COVID-19 pandemic

Digital contact tracing (DCT) applications have been introduced in many countries to aid the containment of COVID-19 outbreaks. Initially, enthusiasm was high regarding their implementation as a non-pharmaceutical intervention (NPI). However, no country was able to prevent larger outbreaks without falling back to harsher NPIs. Here, we discuss results of a stochastic infectious-disease model that provide insights in how the progression of an outbreak and key parameters such as detection probability, app participation and its distribution, as well as engagement of users impact DCT efficacy informed by results of empirical studies. We further show how contact heterogeneity and local contact clustering impact the intervention’s efficacy. We conclude that DCT apps might have prevented cases on the order of single-digit percentages during single outbreaks for empirically plausible ranges of parameters, ignoring that a substantial part of these contacts would have been identified by manual contact tracing. This result is generally robust against changes in network topology with exceptions for homogeneous-degree, locally-clustered contact networks, on which the intervention prevents more infections. An improvement of efficacy is similarly observed when app participation is highly clustered. We find that DCT typically averts more cases during the super-critical phase of an epidemic when case counts are rising and the measured efficacy therefore depends on the time of evaluation.
Golden Jackal Optimization With Joint Opposite Selection: An Enhanced Nature-Inspired Optimization Algorithm for Solving Optimization Problems

This paper presents the logical relationships of Aristotle’s <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">square of opposition</i> on four basic categorial prepositions (i.e., contrary, contradictory, subcontrary, and subaltern) of Joint Opposite Selection (JOS). JOS brings a mutual reinforcement by joining two opposition strategies of Dynamic Opposite (DO) and Selective Leading Opposition (SLO). The DO and SLO improve the balance of exploration and exploitation, respectively, in a given search space. We also propose an enhancement of Golden Jackal Optimization (GJO) with Joint Opposite Selection named GJO-JOS. In the optimization process, JOS assists GJO in assaulting the prey swiftly using SLO. DO assists GJO in finding better chances to locate the fittest prey. With JOS, the GJO succeeds in elevating its performance. We evaluate the performance of GJO-JOS in a competition of CEC 2017 on a set of 29 benchmark functions. The benchmark functions include unimodal, multimodal, hybrid, and composition. Based on these benchmark functions, the evaluation results of GJO-JOS are competitive to GJO with seven single opposition-based learning strategies (OBLs). We also compare GJO-JOS to eight nature-inspired algorithms including the original version of GJO. GJO-JOS produces promising results among seven single OBLs, eight natured-inspired algorithms, and GJO. The experimental results confirm that GJO-JOS generates equilibrium in the balance mechanism effectively.
A FAIR Model Catalog for Ontology-Driven Conceptual Modeling Research

Conceptual models are artifacts representing conceptualizations of particular domains. Hence, multi-domain model catalogs serve as empirical sources of knowledge and insights about specific domains, about the use of a modeling language's constructs, as well as about the patterns and anti-patterns recurrent in the models of that language crosscutting different domains. However, to support domain and language learning, model reuse, knowledge discovery for humans, and reliable automated processing and analysis by machines, these catalogs must be built following generally accepted quality requirements for scientific data management. Especially, all scientific (meta)data—including models—should be created using the FAIR principles (Findability, Accessibility, Interoperability, and Reusability). In this paper, we report on the construction of a FAIR model catalog for Ontology-Driven Conceptual Modeling research, a trending paradigm lying at the intersection of conceptual modeling and ontology engineering in which the Unified Foundational Ontology (UFO) and OntoUML emerged among the most adopted technologies. In this initial release, the catalog includes over a hundred models, developed in a variety of contexts and domains. The paper also discusses the research implications for (ontology-driven) conceptual modeling of such a resource.
Forecasting from LiDAR via Future Object Detection

Object detection and forecasting are fundamental components of embodied perception. These two problems, how-ever, are largely studied in isolation by the community. In this paper, we propose an end-to-end approachfor detection and motion forecasting based on raw sensor measurement as opposed to ground truth tracks. Instead of predicting the current frame locations and forecasting forward in time, we directly predict future object locations and backcast to determine where each trajectory began. Our approach not only improves overall accuracy compared to other modular or end-to-end baselines, it also prompts us to rethink the role of explicit tracking for embodied perception. Additionally, by linking future and current locations in a many-to-one manner, our approach is able to reason about multiple futures, a capability that was previously considered difficult for end-to-end approaches. We conduct extensive experi-ments on the popular nuScenes dataset and demonstrate the empirical effectiveness of our approach. In addition, we investigate the appropriateness of reusing standard forecasting metrics for an end-to-end setup, and find a number of limitations which allow us to build simple baselines to game these metrics. We address this issue with a novel set of joint forecasting and detection metrics that extend the commonly used AP metrics from the detection community to measuring forecasting accuracy. Our code is available on GitHub.
Pixel screening based intermediate correction for blind deblurring

Blind deblurring has attracted much interest with its wide applications in reality. The blind deblurring problem is usually solved by estimating the intermediate kernel and the intermediate image alternatively, which will finally converge to the blurring kernel of the observed image. Numerous works have been proposed to obtain intermediate images with fewer undesirable artifacts by designing delicate regularization on the latent solution. However, these methods still fail while dealing with images containing saturations and large blurs. To address this problem, we propose an intermediate image correction method which utilizes Bayes posterior estimation to screen through the intermediate image and exclude those unfavorable pixels to reduce their influence for kernel estimation. Extensive experiments have proved that the proposed method can effectively improve the accuracy of the final derived kernel against the state-of-the-art methods on benchmark datasets by both quantitative and qualitative comparisons.
Optimization of Max-Min and Min-Min Task Scheduling Algorithms Using G.A in Cloud Computing

Task and resource scheduling in cloud computing remains an important topic that requires enhancement. In this paper, a hybrid task scheduling consisting of three algorithms Min-Min scheduling, Max-Min scheduling, and genetic algorithm is proposed to reduce Makespan and optimize the load balances between resources. In this work genetics algorithm play a vital role by determining which tasks should schedule using the Max-Min algorithm and which ones should be scheduled using the Min-Min algorithm. The Makespan and resources utilization are two measures used to test the efficiency of the proposed work. The results obtained from the proposed work are compared with Min-Min and Max-Min scheduling algorithms, the experimental results show the best outcomes in reducing the Makespan and utilization of all available resources effectively compared with the other two algorithms.
Global Spectral Filter Memory Network for Video Object Segmentation

This paper studies semi-supervised video object segmentation through boosting intra-frame interaction. Recent memory network-based methods focus on exploiting inter-frame temporal reference while paying little attention to intra-frame spatial dependency. Specifically, these segmentation model tends to be susceptible to interference from unrelated nontarget objects in a certain frame. To this end, we propose Global Spectral Filter Memory network (GSFM), which improves intra-frame interaction through learning long-term spatial dependencies in the spectral domain. The key components of GSFM is 2D (inverse) discrete Fourier transform for spatial information mixing. Besides, we empirically find low frequency feature should be enhanced in encoder (backbone) while high frequency for decoder (segmentation head). We attribute this to semantic information extracting role for encoder and fine-grained details highlighting role for decoder. Thus, Low (High) Frequency Module is proposed to fit this circumstance. Extensive experiments on the popular DAVIS and YouTube-VOS benchmarks demonstrate that GSFM noticeably outperforms the baseline method and achieves state-of-the-art performance. Besides, extensive analysis shows that the proposed modules are reasonable and of great generalization ability. Our source code is available at https://github.com/workforai/GSFM .
Intelligent Optimization of Adaptive Dynamic Window Approach for Mobile Robot Motion Control Using Fuzzy Logic

The paper presents an adaptive dynamic window approach (DWA) for mobile robot dynamic obstacles avoidance optimized utilizing a fuzzy logic controller. Most of the present work on autonomous navigation in dynamic environments does not take into account the dynamics of the obstacles. One of the methods used in research today for dynamic obstacles avoidance is the dynamic window approach. The (DWA) is a well-known navigation scheme. One problem facing the DWA is how to optimize the weights of its objective function to allow the robot to move towards the goal while avoiding collisions in all environments. The main contribution of this paper is to build an intelligent system that will be able to optimize the objective function weights of the dynamic window to make it more resilient to changes and moves as fast as possible towards the goal using fuzzy logic system. The proposed new adaptive controller was able to reduce the failure rate of the DWA from 20% to only five per cent in static environments, and maintain more than 60% success rate in dynamic environments with up to 25 obstacles / 100 m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , on the other hand the basic algorithm was failing to less than 50% with 15 obstacles / 100 m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> .
Uav Path Planning Based on Improved Artificial Potential Field Method

At present, the research on UAV mission planning mainly focuses on traditional $\text{A}^{\star}$ algorithm, artificial potential field method, $\text{D}^{\star}$ algorithm and some early popular intelligent algorithms, such as ant colony algorithm, particle swarm algorithm, genetic algorithm and so on. To solve the artificial potential field method in unmanned aerial vehicle (UAV) route planning for obstacles and target distance too close without gravitational repulsion and only lead to target unreachable, UAV repulsive force and force of the net force is zero, not to the target point close to the problems, put forward an improved artificial potential field algorithm, we first by improved artificial potential field method in the repulsive force function, In the repulsive function, the attraction of UAV to the target point is introduced by obstacles to solve the problems of local optimization and unreachable target point. Then, the repulsive potential field of UAV boundary is established to limit the driving area of UAV, and the influence of UAV speed and repulsive potential field is properly considered. Finally, the effectiveness of the improved method is verified by simulation.
Feature selection based on absolute deviation factor for text classification

In text classification, it is necessary to perform feature selection to alleviate the curse of dimensionality caused by high-dimensional text data. In this paper, we utilize class term frequency (CTF) and class document frequency (CDF) to characterize the relevance between terms and categories in the level of term frequency (TF) and document frequency (DF). On the basis of relevance measurement above, three feature selection methods (ADF based on CTF (ADF-CTF), ADF based on CDF (ADF-CDF), and ADF based on both CTF and CDF (ADF-CTDF)) are proposed to identify relevant and discriminant terms by introducing absolute deviation factors (ADFs). Absolute deviation, a statistic concept, is first adopted to measure the relevance divergence characterized by CTF and CDF. In addition, ADF-CTF and ADF-CDF can be combined with existing DF-based and TF-based methods, respectively, which results in new ADF-based methods. Experimental results on six high-dimensional textual datasets using three classifiers indicate that ADF-based methods outperform original DF-based and TF-based ones in 89% cases in terms of Micro-F1 and Macro-F1, which demonstrates the role of ADF integrated in existing methods to boost the classification performance. In addition, findings also show that ADF-CTDF ranks first averagely among multiple datasets and significantly outperforms other methods in 99% cases.