Miracle-dz/UniFAR-FTUs huggingface dataset:
From Situational Awareness to Actionability

People are increasingly sharing information on social media during disaster events. This information could be valuable to emergency responders, but there remain challenges for using it to inform response efforts---including filtering relevant information from the large volumes of noise. Previous research has largely focused on identifying information that can contribute to a generalized concept of situational awareness. Our work explores the value of approaching this problem from a different perspective---one of actionablity---with the idea that information relevance may vary across responder role, domain, and other factors. This approach asks how we can get the right information to the right person at the right time? We interviewed and surveyed diverse responders to understand what "actionable" information is, allowing that actionability might differ from one responder to another. Through the findings, we (a) offer a nuanced understanding of actionability and differentiate it from situational awareness; (b) describe responders' perspective of what distinguishes good information when making rapid judgments; and (c) suggest opportunities for augmenting social media use to highlight information that needs immediate attention. We offer researchers an opportunity to frame different models of actionability to suit the requirements of a responding role.
AIS Electronic

This study uses the financial ratio analysis system constructed by ontology engineering, taking the five financial forces aspect as the basis for judging the financial quality of an enterprise and cooperating. Using six steps of the TOVE (Toronto Ontology Virtual Enterprise) design the hierarchical financial ratio knowledge, thereby demonstrating knowledge content and the relevance of accounting subjects and financial ratios. Through this study, we demonstrate a novel financial ratio analysis system that is suitable for non-financial background people. Based on empirical case evidence, the system is easy to understand, implement and maintain. The system can provide users for the investment decisions, credit decisions, critical analysis and management decision-making.
Matrix and Tensor Factorization Based Game Content Recommender Systems: A Bottom-Up Architecture and a Comparative Online Evaluation

Players of digital games face numerous choices as to what kind of games to play and what kind of game content or in-game activities to opt for. Among these, game content plays an important role in keeping players engaged so as to increase revenues for the gaming industry. However, while nowadays a lot of game content is generated using procedural content generation, automatically determining the kind of content that suits players' skills still poses challenges to game developers. Addressing this challenge, we present matrix- and tensor factorization based game content recommender systems for recommending quests in a single player role-playing game. We discuss the theory behind latent factor models for recommender systems and derive an algorithm for tensor factorizations to decompose collections of bipartite matrices. Extensive online bucket type tests reveal that our novel recommender system retained more players and recommended more engaging quests than handcrafted content-based and previous collaborative filtering approaches.
RNAsynth: constraints learning for RNA inverse folding

RNA polymers are an important class of molecules: not only they are involved in a variety of biological functions, from coding to decoding, from regulation to expression of genes, but crucially, they are nowadays easily synthesizable, opening interesting application scenarios in biotechnological and biomedical domains. Here we propose a constructive machine learning framework to aid in the rational design of such polymers. Using a graph kernel approach in a supervised setting we deﬁne an importance notion over molecular parts. We then convert the set of most important parts into speciﬁc sequence and structure constraints. Finally an inverse folding algorithm uses these constraints to compute the desired RNA sequence.
Emotional Stress State Detection Using Genetic Algorithm-Based Feature Selection on EEG Signals

In recent years, stress analysis by using electro-encephalography (EEG) signals incorporating machine learning techniques has emerged as an important area of research. EEG signals are one of the most important means of indirectly measuring the state of the brain. The existing stress algorithms lack efficient feature selection techniques to improve the performance of a subsequent classifier. In this paper, genetic algorithm (GA)-based feature selection and k-nearest neighbor (k-NN) classifier are used to identify stress in human beings by analyzing electro-encephalography (EEG) signals. GA is incorporated in the stress analysis pipeline to effectively select subset of features that are suitable to enhance the performance of the k-NN classifier. The performance of the proposed method is evaluated using the Database for Emotion Analysis using Physiological Signals (DEAP), which is a public EEG dataset. A feature set is extracted in 32 EEG channels, which consists of statistical features, Hjorth parameters, band power, and frontal alpha asymmetry. The selected features through GA are used as input to the k-NN classifier to distinguish whether each EEG datapoint represents a stress state. To further consolidate, the effectiveness of the proposed method is compared with that of a state-of-the-art principle component analysis (PCA) method. Experimental results show that the proposed GA-based method outperforms PCA, with GA demonstrating 71.76% classification accuracy compared with 65.3% for PCA. Thus, it can be concluded that the proposed method can be effectively used for stress analysis with high classification accuracy.
Estimation of Consumer Review Quality

Social media is nowadays an excellent platform for gathering consumer intelligence for supporting various business intelligence applications. Among the various user-generated content on social media websites, consumer review is a critical one. However, as the volume of consumer reviews grows rapidly, it is infeasible to analyze them manually. Design of effective social media analytic techniques becomes essential. In this paper, we concentrate on proposing a review quality estimation (RQE) method. The RQE method adopts three types of indicators (namely content features, author profile features, and social network features) and five well-known supervised learning algorithms for review quality classifier induction. The Ciao public dataset is employed for empirical evaluation of the proposed RQE method. According to our empirical evaluation results, random forest algorithm has best performance. In addition, the combination of three types of features has the best classification performance.
Object Detection in Remote Sensing Images with Center Only

There are a lot of works aiming to reduce the need of human annotations for object detection: self supervised training, interactive verification instead of annotation or weakly supervised training. For example, only pointing object centres is a faster to annotate but weaker ground truth than providing bounding boxes or detailed segmentation mask. Although not usable for large areas such as roads, vegetation and buildings, centers can be used to learn adequate detectors and segmentors. We perform a comparative analysis on four public remote sensing datasets on the task of vehicle detection and show that centre annotations is a competitive baseline compared to other more sophisticated annotations.
An Environmentally Aware Scheme of Wireless Sensor Networks for Forest Fire Monitoring and Detection

Forest fires are a fatal threat to environmental degradation. Wireless sensor networks (WSNs) are regarded as a promising candidate for forest fire monitoring and detection since they enable real-time monitoring and early detection of fire threats in an efficient way. However, compared to conventional surveillance systems, WSNs operate under a set of unique resource constraints, including limitations with respect to transmission range, energy supply and computational capability. Considering that long transmission distance is inevitable in harsh geographical features such as woodland and shrubland, energy-efficient designs of WSNs are crucial for effective forest fire monitoring and detection systems. In this paper, we propose a novel framework that harnesses the benefits of WSNs for forest fire monitoring and detection. The framework employs random deployment, clustered hierarchy network architecture and environmentally aware protocols. The goal is to accurately detect a fire threat as early as possible while maintaining a reasonable energy consumption level. ns-2-based simulation validates that the proposed framework outperforms the conventional schemes in terms of detection delay and energy consumption.
APLenty: annotation tool for creating high-quality datasets using active and proactive learning

In this paper, we present APLenty, an annotation tool for creating high-quality sequence labeling datasets using active and proactive learning. A major innovation of our tool is the integration of automatic annotation with active learning and proactive learning. This makes the task of creating labeled datasets easier, less time-consuming and requiring less human effort. APLenty is highly flexible and can be adapted to various other tasks.
Reframing HRI Design Opportunities for Social Robots: Lessons Learnt from a Service Robotics Case Study Approach Using UX for HRI

Over the last few decades, semi-autonomous machine’s technology started to promote awareness towards the importance of human–robot interaction (HRI) for improving daily activities. More affordable social robots are being commercially released and in order to implement viable applications of HRI, a combination human-computer interaction and user experience methodologies could play a pivotal role in assessing new scenarios and evaluating new investigations. However, literature shows that it is still challenging to reach an optimal user experience with robotic companions. The aim of the study was to determine the chance to enhance the user experience with a semi-autonomous social robot, using user experience and human–computer interaction methodologies. In this study, a social robotic companion has been developed and prototyped in order to be adopted in a specific public environment such as a company workspace. The challenges emerged from this peculiar environment triggered the need for a more productive and comfortable office for the employees, and, at the same time, the usability, acceptance and likeability of the robotic companion have been evaluated. The results emphasize that, since HRI is highly interdisciplinary, the benefits of combining approaches from other fields could positively benefit from a meaningful social interaction with the users.
Exclusive versus Inclusive Trauma System Model in High Volume Trauma Regions

Please cite this paper as: Joshi MK, Choudhary N, Kumar S, Bagaria D, Kumar A, Priyadarshini P. Diaphragmatic Herniation of Ruptured Right Lobe of Liver with Hypertrophied Left Lobe. Bull Emerg Trauma. 2018;6(4):269-270. doi: 10.29252/beat-060401. *Corresponding author: Hamid Reza Abbasi Address: Trauma Research Center, Shahid Rajaee (Emtiaz) Trauma Hospital, Shiraz University of Medical Sciences, Shiraz, Iran. Tel: +98-71-36360697; Fax: +98-71-36254206 e-mail: abbasimezy@yahoo.com Received: August 10, 2018 Accepted: September 9, 2018
An exploration of algorithmic discrimination in data and classification

Algorithmic discrimination is an important aspect when data is used for predictive purposes. This paper analyzes the relationships between discrimination and classification, data set partitioning, and decision models, as well as correlation. The paper uses real world data sets to demonstrate the existence of discrimination and the independence between the discrimination of data sets and the discrimination of classification models.
Recent Findings on the Sentinel-L Geolocation Accuracy Using the Australian Corner Reflector Array

Synthetic Aperture Radar (SAR) satellites observe range and azimuth geometric accuracies at the low centimeter level. The accuracy of geolocation is driven by several aspects, e.g. orbit determination, SAR image processing, or atmospheric error correction. Our paper concentrates on the Sentinel-1 mission and the compensation of the platform motion effects in the geolocation, which were found to limit the best possible geolocation capabilities of Sentinel-1. The key to advance the geolocation results is the rigorous compensation of the bistatic effect in azimuth, and the correction of the Doppler-induced shifts in range. First results for Sentinel-1 at the Australian reflector array consisting of 40 Corner Reflector (CR) show consistent improvement in the geolocation $(1\sigma)$ to 6 cm in range and 28 cm in azimuth for both spacecrafts when using the Interferometric Wide swath (IW) product.
The relationship between phospholipids and insulin resistance: From clinical to experimental studies

Insulin resistance induced by high‐fat diet and impropriate life style is a major contributor to the pathogenesis of metabolic disease. However, the underlying molecular mechanisms remain unclear. Recent studies in metabolic dysfunction have extended this beyond simply elevated cholesterol and triglycerides levels and have identified a key role for lipid metabolism. For example, altered phospholipid metabolism has now become central in the pathogenesis of metabolic disease. In this review, we discuss the association between insulin sensitivity and phospholipid metabolism and highlight the most significant discoveries generated over the last several decades. Finally, we summarize the current knowledge surrounding the molecular mechanisms related to phospholipids and insulin resistance and provide new insight for future research into their relationship.
Cancellous bone and theropod dinosaur locomotion. Part I—an examination of cancellous bone architecture in the hindlimb bones of theropods

This paper is the first of a three-part series that investigates the architecture of cancellous (‘spongy’) bone in the main hindlimb bones of theropod dinosaurs, and uses cancellous bone architectural patterns to infer locomotor biomechanics in extinct non-avian species. Cancellous bone is widely known to be highly sensitive to its mechanical environment, and has previously been used to infer locomotor biomechanics in extinct tetrapod vertebrates, especially primates. Despite great promise, cancellous bone architecture has remained little utilized for investigating locomotion in many other extinct vertebrate groups, such as dinosaurs. Documentation and quantification of architectural patterns across a whole bone, and across multiple bones, can provide much information on cancellous bone architectural patterns and variation across species. Additionally, this also lends itself to analysis of the musculoskeletal biomechanical factors involved in a direct, mechanistic fashion. On this premise, computed tomographic and image analysis techniques were used to describe and analyse the three-dimensional architecture of cancellous bone in the main hindlimb bones of theropod dinosaurs for the first time. A comprehensive survey across many extant and extinct species is produced, identifying several patterns of similarity and contrast between groups. For instance, more stemward non-avian theropods (e.g. ceratosaurs and tyrannosaurids) exhibit cancellous bone architectures more comparable to that present in humans, whereas species more closely related to birds (e.g. paravians) exhibit architectural patterns bearing greater similarity to those of extant birds. Many of the observed patterns may be linked to particular aspects of locomotor biomechanics, such as the degree of hip or knee flexion during stance and gait. A further important observation is the abundance of markedly oblique trabeculae in the diaphyses of the femur and tibia of birds, which in large species produces spiralling patterns along the endosteal surface. Not only do these observations provide new insight into theropod anatomy and behaviour, they also provide the foundation for mechanistic testing of locomotor hypotheses via musculoskeletal biomechanical modelling.
A Deep Learning Approach For Pedestrian Segmentation In Infrared Images

Semantic segmentation in the context of traffic scenes has been vastly explored using different architectures for deep convolutional networks and color images. In the case of infrared images there is place for improvement and scientific contributions mainly due to the lack of data sets that contain baseline segmentations in the infrared domain. This paper proposes a method for real time infrared pedestrian segmentation using ERFNet. Within the context of the proposed method we study the effect of different basic image enhancement techniques on the performance of the segmentation. We enhance an existing dataset of infrared images with ground truth segmentations for pedestrians. Our experiments show that the proposed method is accurate and appropriate for real time applications.
Applications of inductive logic programming

Some applications of Inductive Logic Programming (ILP) are presented. Those applications are chosen that specifically benefit from relational descriptions generated by ILP programs, and from ILP's ability to accommodate background knowledge. Applications included are: drug design, predicting the secondary structure of proteins, and design of finite-element meshes. Some other applications are briefly described. The practical advantages and disadvantages of ILP learning are discussed.
Toward Efficient Breast Cancer Diagnosis and Survival Prediction Using L-Perceptron

Breast cancer is the most frequently reported cancer type among the women around the globe and beyond that it has the second highest female fatality rate among all cancer types. Despite all the progresses made in prevention and early intervention, early prognosis and survival prediction rates are still unsatisfactory. In this paper, we propose a novel type of perceptron called L-Perceptron which outperforms all the previous supervised learning methods by reaching 97.42 \% and 98.73 \% in terms of accuracy and sensitivity, respectively in Wisconsin Breast Cancer dataset. Experimental results on Haberman's Breast Cancer Survival dataset, show the superiority of proposed method by reaching 75.18 \% and 83.86 \% in terms of accuracy and F1 score, respectively. The results are the best reported ones obtained in 10-fold cross validation in absence of any preprocessing or feature selection.
Value-Sensitive Algorithm Design

Most commonly used approaches to developing automated or artificially intelligent algorithmic systems are Big Data-driven and machine learning-based. However, these approaches can fail, for two notable reasons: (1) they may lack critical engagement with users and other stakeholders; (2) they rely largely on historical human judgments, which do not capture and incorporate human insights into how the world can be improved in the future. We propose and describe a novel method for the design of such algorithms, which we call Value Sensitive Algorithm Design. Value Sensitive Algorithm Design incorporates stakeholders' tacit knowledge and explicit feedback in the early stages of algorithm creation. This increases the chance to avoid biases in design choices or to compromise key stakeholder values. Generally, we believe that algorithms should be designed to balance multiple stakeholders' needs, motivations, and interests, and to help achieve important collective goals. We also describe a specific project "Designing Intelligent Socialization Algorithms for WikiProjects in Wikipedia" to illustrate our method. We intend this paper to contribute to the rich ongoing conversation concerning the use of algorithms in supporting critical decision-making in society.
First Results of Experimental Polarimetric SAR-GMTI Modes on RADARSAT-2

Initial results of newly developed dual-channel polarimetric SAR-GMTI modes on RADARSAT-2 are presented, showing combined capabilities to detect ships both in open water and in a river channel surrounded by land and to differentiate land and target types. The potential to use motion information along with backscattering characteristics of HH and VH polarizations to differentiate ships from icebergs are discussed.
Doppler Ambiguities Masking for Altimeter Waveforms: a Model Based Approach

Delay/Doppler processing for radar altimeters allows to improve the along-track resolution, but it can be affected by Doppler ambiguities depending on the design of the instrument. If not accounted for, Doppler ambiguities can cause a distortion of the multilooked waveforms and a loss of accuracy in retrieved geophysical parameters. This paper presents a waveform model based approach to define a Doppler ambiguity mask to filter out the samples affected by the ambiguous returns before multilooking, assuming a uniform rough scattering surface. The effectiveness of the proposed method for Doppler ambiguities masking has been verified on Sentinel-6 simulated data and on in-orbit data from CryoSat SARin acquisitions over ocean.
Collision-avoiding imaging trajectories for linac mounted cone-beam CT.

BACKGROUND Some patients cannot be imaged with cone-beam CT for image-guided radiation therapy because their size, pose, or fixation devices cause collisions with the machine. OBJECTIVE To investigate imaging trajectories that avoid such collisions by using virtual isocenter and variable magnification during acquisition while yielding comparable image quality. METHODS The machine components most likely to collide are the gantry and kV detector. A virtual isocenter trajectory continuously moves the patient during gantry rotation to maintain an increased separation between the two. With dynamic magnification, the kV detector is dynamically moved to increase clearance for an angular range around the potential collision point while acquiring sufficient data to maintain the field-of-view. Both strategies were used independently and jointly with the resultant image quality evaluated against the standard circular acquisition. RESULTS Collision avoiding trajectories show comparable contrast and resolution to standard techniques. For an anthropomorphic phantom, the RMSE is <7×10- 4, multi-scale structural similarity index is >0.97, and visual image fidelity is >0.96 for all trajectories when compared to a standard circular scan. CONCLUSIONS The proposed trajectories avoid machine-patient collisions while providing comparable image quality to the current standard thereby enabling CBCT imaging for patients that could not otherwise be scanned.
Urban Land Use/Land Cover Classification Based on Feature Fusion Fusing Hyperspectral Image and Lidar Data

Hyperspectral images have been widely used in classification because of the abundant spectral information. But it can't distinguish the objective with similar spectral character but different elevation. However, LiDAR data can obtain elevation information. Therefore, it will obtain better classification maps if fusing the two data. In recent years, CNN has attracted much attention due to its powerful ability to excavate the potential representation and features of the raw data. However, it's difficult to distinguish the objects with different spectral information but similar surface character. Unlike CNN features, the traditional manual features, such as the normalized vegetation index (NDVI), have a certain characteristic expression significance. In order to consider both the semantic information of traditional manual features and the advanced features of CNN features, this paper proposes a fusion algorithm of hyperspectral and LiDAR fusion based on feature fusion. The proposed algorithm has achieved a good fusion classification effect on the MUUFL Gulfport Hyperspectral and LiDAR Data set.