Miracle-dz/UniFAR-FTUs huggingface dataset:
Cascading Threats in Critical Infrastructures with Control Systems

Critical infrastructures (CIs) increase in complexity due to numerous dependencies on other CIs but also due to the ongoing digitalization in the industry sector. This yields an increased risk of failure of a single CI as the overall systems gets very fragile and sensitive to errors Failure of a single component may affect large parts of an infrastructure due to cascading effects. One way to support functionality of a CI is the use of Industrial Control Systems (ICS) that allow monitoring remote sites and controlling processes. However, this is an additional source for threats as recent cyber-attacks have shown. Further, the additional information for such cyber systems is often not efficiently combined with existing information on the physical infrastructure. We here propose a method to combine these two sources of information in order to estimate the impact of a security incident on CIs, taking into account cascading effects of threats. An implementation of the model allows simulation of the dynamics inside a CI and yields a record of the status of each asset of the CI. The way the assets change their states illustrates the consequences of an incident on the entire CI. Visualization of the results provides an overview on the situation of the entire CI at a certain point of time and a sequence of such visualization over an entire period of time illustrates the changes over time. The results from this analysis may be used to support security officers in analyzing the current (hybrid) state of their CI in case of an incident and thus increase the hybrid situational awareness.
Task Offloading with Power Control for Mobile Edge Computing Using Reinforcement Learning-Based Markov Decision Process

This paper proposes an efficient computation task offloading mechanism for mobile edge computing (MEC) systems. The studied MEC system consists of multiple user equipment (UEs) and multiple radio interfaces. In order to maximize the number of UEs benefitting from the MEC, the task offloading and power control strategy for a UE is optimized in a joint manner. However, the problem of finding the optimal solution is NP-hard. We then reformulate the problem as a Markov decision process (MDP) and develop a reinforcement learning- (RL-) based algorithm to solve the MDP. Simulation results show that the proposed RL-based algorithm achieves a near-optimal performance compared to the exhaustive search algorithm, and it also outperforms the received signal strength- (RSS-) based method no matter from the standpoint of the system (as it leads to a larger number of beneficial UEs) or an individual (as it generates a lower computation overhead for a UE).
Understanding patterns and mood changes through tweets about disasters

We analyzed a sample of large tweet collections gathered since 2011, to expand understanding about tweeting patterns and emotional responses of diﬀerent types of tweeters regarding disasters. We selected three examples for each of four disaster types: school shooting , bombing , earthquake , and hurricane . For each collection, we deployed our novel model TwiRole for user classiﬁcation, and an existing deep learning model for mood detection. We found diﬀerences in the daily tweet count patterns, between the diﬀerent types of events. Likewise, there were diﬀerent average scores and patterns of moods (fear, sadness, surprise), both between types of events, and between events of the same type. Further, regarding surprise and fear, there were diﬀerences among roles of tweeters. These results suggest the value of further exploration as well as hypothesis testing with our hundreds of event and trend related tweet collections, considering indications in those that reﬂect emotional responses to disasters.
Erratum: usDSM: a novel method for deleterious synonymous mutation prediction using undersampling scheme

Corresponding authors: Tiejun Zhang, GMU-GIBH Joint School of Life Sciences, Guangzhou Medical University, Guangzhou, Guangdong 511436, China. Tel. : 86-20-37103208. E-mail: zhang_tiejun@gzhmu.edu. cn; Junfeng Xia, Institutes of Physical Science and Information Technology and School of Computer Science and Technology, Anhui University, Hefei, Anhui 230601, China. Tel. : 86-551-63861990. E-mail: jfxia@ahu.edu. cn
Sympathy Detection in Disaster Twitter Data

Nowadays, micro-blogging sites such as Twitter have become powerful tools for communicating with others in various situations. Especially in disaster events, these sites can be the best platforms for seeking or providing social support, of which informational support and emotional support are the most important types. Sympathy, a sub-type of emotional support, is an expression of one’s compassion or sorrow for a difficult situation that another person is facing. Providing sympathy to people affected by a disaster can help change people’s emotional states from negative to positive emotions, and hence, help them feel better. Moreover, detecting sympathy contents in Twitter can potentially be used for finding candidate donors since the emotion “sympathy” is closely related to people who may be willing to donate. Thus, in this paper, as a starting point, we focus on detecting sympathy-related tweets. We address this task using Convolutional Neural Networks (CNNs) with refined word embeddings. Specifically, we propose a refined word embedding technique in terms of various pre-trained word vector models and show great performance of CNNs that use these refined embeddings in the sympathy tweet classification task. We also report experimental results showing that the CNNs with the refined word embeddings outperform not only traditional machine learning techniques, such as Naïve Bayes, Support Vector Machines and AdaBoost with conventional feature sets as bags of words, but also Long Short-Term Memory Networks.
Understanding Aggregate Human Behaviour Changes in Response to a Natural Disaster in Vanuatu via Mobile Network Data Analysis

This paper presents a use case of mobile network data as a new source of insight for humanitarian action. Based on analysis covering the Republic of Vanuatu, we identify aggregate behavioral patterns indicating short term and medium term behavior changes as a result of a tropical cyclone, which could contribute to our understanding of the resilience of communities to natural hazards. We also find interesting behavioral insights on how the human movement network is impacted by a cyclone. Due to the detail and tractability of the data set, insights on preparation, displacement, damage and resilience could enable more agile and adaptive responses by public institutions and other actors to humanitarian emergencies. Considering the array of natural hazards that the South Pacific region faces on an annual basis, this use case contributes more evidence in favor of using anonymized mobile network data to inform humanitarian action.
A New Type of Industrial Robot Trajectory Generation Component Based on Motion Modularity Technology

Motion modularity is the main method of motion control for higher animals. That means the complex movements of the muscles are made up of basic motion primitives, and the brain or central nervous system does not care about the specific details of the movement. However, the industrial robot control system does not adopt the technical roadmap of motion modularity, it generates complex trajectories by providing a large number of sampling points. This approach is equivalent to using the brain to directly guide the specific movement of the muscle and has to rely on a faster Fieldbus system to obtain complex motion trajectories. This work constructs a modularized industrial robot trajectory generation component based on Dynamic Movement Primitives (DMP) theory. With this component, the robot controller can generate complex trajectories without increasing the sampling points and can obtain good trajectory accuracy. Finally, the rationality of this system is proved by simulations and experiments.
Data Acquisition for ad-hoc Evacuation Simulations of Public Buildings

Crowd simulation is suitable to evaluate evacuation strategies but its validity strongly depends on the quality of input data. The acquisition of adequate input data is particularly challenging when simulating the evacuation of public buildings such as universities. As they are publicly accessible, the exact number of persons on site is unknown. Yet, to investigate speciﬁc emergency situations by means of simulation, e.g. amok or ﬁre, information is required about distribution and amount of people within the building at a speciﬁc point of time. Due to data privacy, public buildings do not implement access control. However, data artifacts are available in various information systems, e.g., wiﬁ data, room administration. Our hypothesis is, that the acquisition and fusion of such data artifacts is suﬃcient to enable data-based ad-hoc simulation of evacuation scenarios as decision support for the operations management. To this end, we introduce a procedure for the situation-dependent collection fusion of simulation input data. Furthermore, a case study is provided to demonstrate the feasibility of the approach.
Predicting Hurricane Damage Using Social Media Posts Coupled with Physical and Socio-Economic Variables

During a natural disaster or emergency event, individual social media posts or hot spots may not necessarily correlate to the most devastated areas. To better understand the correlation between social media and physical damage, we compare Tweets, data about the physical environment, and socio-economic factors with insurance claim information (as a proxy for physical damage) from 2017 Hurricane Irma in the state of Florida. We use machine learning to identify relevant Tweets, sensitivity analyses to identify socio-economic factors, and statistical regression to determine the predictive capability of insurance claims as a proxy for damage. We ﬁnd that Tweets alone result in a poorly ﬁtted regression model of insurance claims, but the inclusion of physical features (e.g., power outages, wind level) and socio-economic factors (e.g., population density, education, Internet access) improves the model’s ﬁt. Such models contribute to the knowledge base that may allow social media to predict damage in real-time.
Simulating real-time Twitter data from historical datasets

In this paper, we will discuss a system design for simulating social media data based on historical datasets. While many datasets containing data collected from social media during crisis have become publicly available, there is a lack of tools or systems can present this data on the same timeline as it was originally posted. Through the design and use of the tool discussed in this paper, we show how historical datasets can be used for algorithm testing, such as those used in machine learning, to improve the quality of the data. In addition, the use of simulated data also has its benefits in training scenarios, which would allow participants to see real, non-fabricated social media messages in the same temporal manner as found on a social media platform. Lastly, we will discuss the positive reception and future improvements suggested by 911 Public Service Answering Point (PSAP) professionals.
Semantic Understanding of Human Mobility Lifestyle to support Crisis Management

In this paper, we propose a method for understanding the semantics of mobility (mainly related to lifestyle) patterns based on stay point detection from tracking data. The method identifies the context (trip purpose and visited point of interest) of tracking data by using large-scale data collection infrastructure. We evaluate our method with a tracking dataset in Birmingham (European project SETA) generated by 534 users from September 2017 to September 2018. To this end, we compare insights from the tracking data with check-in mobility in social media. The results show that both data capture rich human lifestyle features related to the visited point of interest. Our study provides solid evidence that lifestyle patterns from tracking and social media data can indeed be useful for understanding and gauging the level of disruption after a crisis, as it is possible to check the deviation of habits from normal conditions and post-crisis.
Sharing Gut Feelings to Support Collaborative Decision Making

Expertise-based intuition plays a key role in decision making, especially in complex environments as those involved with crisis and emergency domains where decisions often need to be made on the basis of dynamic, incomplete, and/ or contradictory information. In such environments, a deliberative analysis is often impossible or inefficient. Using teams to make collaborative decisions in complex environments can bring benefits to organizations, but the complexity of supporting this scenario also increases. The present work proposes a solution based on graphs to support the sharing of the intuition rationale in teams aiming at an accelerated expertise. The development of the proposal is part of a methodological context of design science research. In this paper we report the execution of one of the expected cycles that explores the use of generated artifacts in practice that then produced insights for the proposed computational support.
Serum testosterone level correlates with left ventricular hypertrophy in older women

Introduction Sex hormones may play an important role in age-related cardiac remodeling. However, their impact on cardiac structure and function in females of advanced age still remains unclear. The aim of this study is to evaluate the relationship between sex hormones level and echocardiographic parameters in older women with concomitant cardiovascular diseases. Materials and Methods The study group included 52 community-dwelling women with mean age 79.5 ± 2.8 years, consecutive patients of an outpatient geriatric clinic. In all the subjects, a transthoracic echocardiogram was performed and serum testosterone, estradiol, follicle-stimulating hormone, luteinising hormone, dehydroepiandrosterone sulphate, and cortisol levels were determined. Results Testosterone level correlated positively with interventricular septum diastolic dimension (IVSd) (rS=0.293, p<0.05), left ventricular mass index (rS=0.285, p<0.05), E/E’ ratio (rS=0.301, p<0.05), and negatively with E’ (rS=-0.301, p<0.05). Estradiol level showed a positive correlation with the posterior wall dimension (rS=0.28, p<0.05). Besides, no significant correlations between clinical or echocardiographic parameters and other hormones were observed. Female subjects with diagnosed left ventricular hypertrophy (LVH) (n=34) were characterized by a significantly higher rate of hypertension (p=0.011), higher waist-to-height ratio (p=0.009), higher testosterone level (0.82 vs. 0.48 nmol/L, p=0.024), higher testosterone/estradiol ratio (16.4 vs. 9.9, p=0.021), and received more anti-hypertensive drugs (p=0.030). In a multiple stepwise logistic regression, the best determinants of LVH were the presence of hypertension (OR=6.51; 95% CI 1.62-26.1), and testosterone level (OR= 6.6; 95% CI 1.19-36.6). Conclusions Higher serum testosterone levels may contribute to pathological cardiac remodeling, especially in hypertensive women. Estradiol, gonadotropins, DHEAS, and cortisol were not related to echocardiographic parameters.
Robust filtering of crisis-related tweets

Social media enables fast information exchange and status reporting during crises. Filtering is usually required to identify the small fraction of social media stream data related to events. Since deep learning has recently shown to be a reliable approach for ﬁltering and analyzing Twitter messages, a Convolutional Neural Network is examined for ﬁltering crisis-related tweets in this work. The goal is to understand how to obtain accurate and robust ﬁltering models and how model accuracies tend to behave in case of new events. In contrast to other works, the application to real data streams is also investigated. Motivated by the observation that machine learning model accuracies highly depend on the used data, a new comprehensive and balanced compilation of existing data sets is proposed. Experimental results with this data set provide valuable insights. Preliminary results from ﬁltering a data stream recorded during hurricane Florence in September 2018 conﬁrm our results.
Exploring multiplexing tools for co-visualization in crisis units

Natural hazards can generate damages in large inhabited areas in a very short time period. Crisis managers must plan interventions very quickly to facilitate the arrival of the first emergency. In a crisis unit, experts visualize heterogeneous visual representations of spatio-temporal information, in order to facilitate decision-making, based on various types of screens, i
Feedback Control of a Soft Swinging Appendage

This paper presents a state-space description using planar discrete elastic rod theory of a soft robotic appendage with torque input at one end. We design a linear output feedback controller to balance the appendage in an unstable vertical configuration with an angle sensor and torque input co-located at the base. Gains are tuned through simulations of the nonlinear system and hardware experiments are performed to verify performance. Simulation results suggest that the resulting control design balances some appendages that would otherwise buckle under their own weight.
Production of novel beneficial alleles of a rice yield‐related QTL by CRISPR/Cas9

In crop plants, many beneficial alleles of yield-related quantitative trait loci involve changes in transcription, rather than changes in protein coding sequence. However, these alleles have multiple mutations and discerning the causal mutation and then recapitulating this mutation in elite crop cultivars has remained challenging. Now, CRISPR-Cas9 approaches allow researchers to address both issues. Our study of a locus involved in resistance to lodging demonstrated that saturation editing of the qSCSA3-1/OsTB1/SCM3 region led to loss-of-function, normal-like, and gain-of-function plant types, based on OsTB1 expression and stem cross-section area. One gain-of-function allele recapitulated a previously described beneficial allele of OsTB1 carrying a TGTG insertion in the 5' region of OsTB1 and enhanced OsTB1 expression at the inflorescence formation stage. These studies indicate that genome editing technology, combined with information on agronomically important genes, can accelerate genetic improvement of crops.
Towards Inclusive Education: The Impact of Disability on School Attendance in Developing Countries

The paper aims to reduce the global knowledge gap pertaining to the impact of disability on school attendance, using cross-nationally comparable and nationally representative data from 18 surveys in 15 countries that are selected among 2,500 surveys and censuses. These selected surveys administered the Washington Group Short Set (WGSS) of disability-screening questions, covering five functional domains of seeing, hearing, mobility, self-care, and remembering, and collected information on educational status. Using both descriptive and econometric approaches, the paper finds that (i) the average disability gap in school attendance stands at 30% in primary and secondary schools in 15 countries; (ii) more than 85% of disabled primary-age children who are out of school have never attended school; (iii) the average marginal effect of disability on primary and secondary school attendance is negative and significant (-30%), and (iv) countries that have reached close to universal primary education report high ratios of disabled to non-disabled out-of-school children indicating that general education policies to improve access do not effectively mainstream disabled children in education, and (v) disabled children confront the same difficulties in participating in education, regardless of their individual and socio-economic characteristics.
Using genomics to understand inter- and intra- outbreak diversity of Pasteurella multocida isolates associated with fowl cholera in meat chickens

Fowl cholera, caused by Pasteurella multocida, continues to be a challenge in meat-chicken-breeder operations and has emerged as a problem for free-range meat chickens. Here, using whole-genome sequencing (WGS) and phylogenomic analysis, we investigate isolate relatedness during outbreaks of fowl cholera on a free-range meat chicken farm over a 5-year period. Our genomic analysis revealed that while all outbreak isolates were sequence type (ST) 20, they could be separated into two distinct clades (clade 1 and clade 2) consistent with difference in their lipopolysaccharide (LPS) type. The isolates from the earlier outbreaks (clade 1) were carrying LPS type L3 while those from the more recent outbreaks (clade 2) were LPS type L1. Additionally, WGS data indicated high inter- and intra-chicken genetic diversity during a single outbreak. Furthermore, we demonstrate that while a killed autogenous vaccine carrying LPS type L3 had been successful in protecting against challenge from L3 isolates it might have driven the emergence of the closely related clade 2, against which the vaccine was ineffective. The genomic results also revealed a 14 bp deletion in the galactosyltransferase gene gatG in LPS type L3 isolates, which would result in producing a semi-truncated LPS in those isolates. In conclusion, our study clearly demonstrates the advantages of genomic analysis over the conventional PCR-based approaches in providing clear insights in terms of linkage of isolate within and between outbreaks. More importantly, it provides more detailed information than the multiplex PCR on the possible structure of outer LPS, which is very important in the case of strain selection for killed autogenous vaccines.
Detection of extremity chronic traumatic osteomyelitis by machine learning based on computed-tomography images

Abstract Despite the availability of a series of tests, detection of chronic traumatic osteomyelitis is still exhausting in clinical practice. We hypothesized that machine learning based on computed-tomography (CT) images would provide better diagnostic performance for extremity traumatic chronic osteomyelitis than the serological biomarker alone. A retrospective study was carried out to collect medical data from patients with extremity traumatic osteomyelitis according to the criteria of musculoskeletal infection society. In each patient, serum levels of C-reactive protein (CRP), erythrocyte sedimentation rate (ESR), and D-dimer were measured and CT scan of the extremity was conducted 7 days after admission preoperatively. A deep residual network (ResNet) machine learning model was established for recognition of bone lesion on the CT image. A total of 28,718 CT images from 163 adult patients were included. Then, we randomly extracted 80% of all CT images from each patient for training, 10% for validation, and 10% for testing. Our results showed that machine learning (83.4%) outperformed CRP (53.2%), ESR (68.8%), and D-dimer (68.1%) separately in accuracy. Meanwhile, machine learning (88.0%) demonstrated highest sensitivity when compared with CRP (50.6%), ESR (73.0%), and D-dimer (51.7%). Considering the specificity, machine learning (77.0%) is better than CRP (59.4%) and ESR (62.2%), but not D-dimer (83.8%). Our findings indicated that machine learning based on CT images is an effective and promising avenue for detection of chronic traumatic osteomyelitis in the extremity.