Reviewerly/exHarmony huggingface dataset:
Detection of Acute Respiratory Distress Syndrome by Incorporation of Label Uncertainty and Partially Available Privileged Information

Acute respiratory distress syndrome (ARDS) is a fulminant inflammatory lung injury that develops in patients with critical illnesses including sepsis, pneumonia, and trauma. However, many patients with ARDS are not recognized when they develop this syndrome nor given outcome-improving treatments. Because ARDS is a clinical syndrome, physicians may not be certain about a patient’s diagnosis (label uncertainty). In addition, the diagnosis requires a chest x-ray, which may not be always be available in a clinical setting (privileged information). For this paper, we implemented the Learning Using Label Uncertainty and Partially Available Privileged Information (LULUPAPI) paradigm, built on classical SVM, to detect ARDS using Electronic Health Record (EHR) data and chest radiography. In comparison to SVM, this resulted in a 3.55 percent improvement of test AUC.
Fraud prediction in loan default using support vector machine

Abstract The concept of taking loan has been in existence since inception of the human race but it is now taking diverse dimensions. This spans through personal exchange of loans for repayment based on personal track records, enjoying loans as proceeds of daily contribution without collaterals, except for the banking sector that requests collaterals for official loans. The uniform occurrence of being unable to pay the debts and resulting in a default is evident to the level of bank closures and nations’ bankruptcy is experienced across board. With a large volume and variety of data, credit history judgment by man is inefficient; case-based, analogy-based reasoning and statistical approaches have been employed but the 21 st century fraudulent attempts cannot be discovered by these approaches, hence; the machine learning approach using the support vector machine. This work employs a supervised learning approach based on machine learning to predict the possibility of a fraud in a loan application through hidden trends in data instead of giving loans which ordinarily should not be approved; past occurrences discovered through machine learning reveals risky loans and a possible fraud by humans in approvals that can result in a default. Machine learning approaches are able to detect fraudulent financial statements to avert business comatose.
LATCH

We present LATCH (short for Locality-Aware Taint CHecker), a generalizable architecture for optimizing dynamic information flow tracking (DIFT). LATCH exploits the observation that information flows under DIFT exhibit strong temporal locality, with typical applications manipulating sensitive data during limited phases of computation. This property allows LATCH to monitor significant spans of execution using lightweight, coarse-grained checks, invoking precise, computationally intensive tracking logic only during periods of execution that involve sensitive data. LATCH implements this policy without sacrificing the accuracy of DIFT.
Property conveyances as a programming language

Anglo-American law enables property owners to split up rights among multiple entities by breaking their ownership apart into future interests that may evolve over time. The conveyances that owners use to transfer and subdivide property rights follow rigid syntactic conventions and are governed by an intricate body of interlocking doctrines that determine their legal effect. These doctrines have been codified, but only in informal and potentially ambiguous ways.
Talking about digital immortality

Social media, wearable devices and other advanced forms of interaction are enabling digital immortality as a post-mortem possibility where people can manage their digital legacy and recreate digital lives as well. In this paper, we study how users perceive digital immortality. In this discuss the results from our research using a focus group to shed light on the topic in a value-oriented perspective. During analysis of the focus group transcription, we reflected on key human values that our participants manifested during discussion, and how important they are in the design of digital immortality features that understand and respect humans needs, from the very beginning of systems usage to the legacy of data, concerning the emotional moment that death provides. This qualitative and quantitative research analyzes these data and proposes important insights in this area.
Sports Video Motion Target Detection and Tracking Based on Hidden Markov Model

Sports video moving object detection and tracking is a hot topic in computer vision research. The hidden Markov model is applied to the detection and tracking of moving objects in sports video. The acquired student motion is used as input to interact with the virtual scene. Firstly, the virtual and real difference measurement selection algorithm based on statistics is used to select the sports students with strong ability. Then, the classifier confidence method is used to select the students with high confidence level from the sports students who have not been marked, and classify them into the marked sports students to promote the generalization ability of the model. Raise. The experimental results show that this method can effectively assist physical education teaching activities and provide objective and effective data analysis for sports video target detection and tracking.
Image retrieval based on ASIFT features in a Hadoop clustered system

For image matching, the scale invariant feature transform (SIFT) algorithm is a commonly used one. They are invariant to image rotation, scale zooming, and partially invariant to change in illumination and 3D camera viewpoint. Affine SIFT (ASIFT) is an extension of SIFT, which solves the problem when images are captured at different angles. However, ASIFT has higher computational complexity than SIFT, due to a huge amount of features in the images. Therefore, in this study, a Hadoop-based image retrieval system is proposed to solve the ASIFT shortcomings of high computation by the MapReduce technology. The system uses a combination of the Bag-of-Words method and support vector machine. Finally, the experimental results verify that the proposed method is more effective than the other state-of-the-art methods for a variety of datasets.
Geographic-categorical diversification in POI recommendations

Nowadays, Recommender Systems (RSs) have been used to help users to discover relevant Points Of Interest (POI) in Location Based Social Network (LBSN), such as Yelp and FourSquare. Due to the main challenges of data sparsity and the geographic influence in this scenario, most of works about POI recommendations has only focused on improving the system's accuracy. However, there is a consensus that just it is not enough to assess the practical effectiveness. In real scenarios, categorical and geographic diversities have been identified as key dimensions of recommendation utility. The few existing works are concentrated on just one of these concepts, singly. In this work, we propose a novel post-processing strategy to merge these concepts in order to improve the user interest in POIs. Our experimental results in Yelp datasets show that our strategy can improve users' satisfaction, considering different RS and multiple diversification metrics. Our method is able to improve the diversity up to 120% without significant accuracy losses.
TopAwaRe: Topology-Aware Registration

Deformable registration, or nonlinear alignment of images, is a fundamental preprocessing tool in medical imaging. State-of-the-art algorithms restrict to diffeomorphisms to regularize an otherwise ill-posed problem. In particular, such models assume that a one-to-one matching exists between any pair of images. In a range of real-life-applications, however, one image may contain objects that another does not. In such cases, the one-to-one assumption is routinely accepted as unavoidable, leading to inaccurate preprocessing and, thus, inaccuracies in the subsequent analysis. We present a novel, piecewise-diffeomorphic deformation framework which models topological changes as explicitly encoded discontinuities in the deformation fields. We thus preserve the regularization properties of diffeomorphic models while locally avoiding their erroneous one-to-one assumption. The entire model is GPU-implemented, and validated on intersubject 3D registration of T1-weighted brain MRI. Qualitative and quantitative results show our ability to improve performance in pathological cases containing topological inconsistencies.
Intelligent Estimation of Vitrinite Reflectance of Coal from Photomicrographs Based on Machine Learning

The accurate measurement of vitrinite reflectance (especially for mean maximum vitrinite reflectance, MMVR) is an important issue in the fields of coal mining and processing. However, the application of MMVR has been somewhat hampered by the subjective and the time-consuming characteristic of manual measurements. Semi-automated methods that are oversimplified might affect the accuracy in measuring MMVR values. To address these concerns, we propose a novel MMVR measurement strategy based on machine learning (MMVRML). Considering the complex nature of coal, adaptive K-means clustering is firstly employed to automatically detect the number of clusters (i.e., maceral groups) in photomicrographs. Furthermore, comprehensive features along with a support vector machine are utilized to intelligently identify the regions with vitrinite. The largest region with vitrinite in each photomicrograph is gridded for further regression analysis. Evaluations on 78 photomicrographs show that the model based on random forest and 15 simplified grayscale features achieves the state-of-the-art root mean square error of 0.0424. In addition, to facilitate the usage of petrologists without strong expertise in the machine learning domain, we released the first non-commercial standalone software for estimating MMVR.
One-Time Programs Made Practical

A one-time program (OTP) works as follows: Alice provides Bob with the implementation of some function. Bob can have the function evaluated exclusively on a single input of his choosing. Once executed, the program will fail to evaluate on any other input. State-of-the-art one-time programs have remained theoretical, requiring custom hardware that is cost-ineffective/unavailable, or confined to ad-hoc/unrealistic assumptions. To bridge this gap, we explore how the Trusted Execution Environment (TEE) of modern CPUs can realize the OTP functionality. Specifically, we build two flavours of such a system: in the first, the TEE directly enforces the one-timeness of the program; in the second, the program is represented with a garbled circuit and the TEE ensures Bob’s input can only be wired into the circuit once, equivalent to a smaller cryptographic primitive called one-time memory. These have different performance profiles: the first is best when Alice’s input is small and Bob’s is large, and the second for the converse.
Knowledge Transfer in Reinforcement Learning Agent

This manuscript is focused on transfer learning methods for reinforcement learning agents. An preview of contemporary papers in area of transfer Leaning and Knowledge transfer. We provided the background and overview of knowledge transfer methods with an emphasis on the topics of reinforcement learning.
Building a Blockchain Application: A Show Case for Healthcare Providers and Insurance Companies

Blockchain is an evolving technology that provides trusted decentralized records of information through encrypted blocks of linked data. This technology promised to provide immutable and integral records shared among authorized parties. This infrastructure creates a vast range of opportunities and reforms wide range of business practices. However, this emerging technology is lacking application development experience in real life project. In this paper, building blockchain-based application is represented for two purposes. The first one is a show case of how blockchain application can reform current business practice. The case represents a blockchain application replacing common business practice of having a middleman (Third-party) delivering original bills between healthcare providers and insurance companies. A ClaimChain application is built to demonstrate the potential benefits of the blockchain application in comparison to conventional practice. The second purpose is a demonstration of design decisions made to build blockchain application with reference to blockchain application design approaches introduced by software architecture community. The paper ends with a summary of lessons learned and recommendations for development process of blockchain application.
Challenges in data collection in real-world environments for activity recognition

Detecting and recognizing activities of daily living is an important part of ambient assisted living (AAL) systems. This part of the system has the highest impact on the overall system efficiency because it directly provides insights into the user's health state. One of the main challenges that AAL systems are facing are the privacy concerns and the intrusiveness of the sensors that are being deployed. In an ideal scenario, an aged person should be able to continue his or her normal life without noticing that they are being monitored. Another issue for such systems is the data collection. The current approaches usually use data generated in labs and data from end-users users is usually unavailable due to ethical concerns and the inability to deploy them in their living environments. Publications that rely on real-life scenario data are scarce. In this paper, we present the challenges one faces when trying to produce a sound dataset for further analysis and suggest ideas for overcoming them.
Reflected Reality: A Mixed Reality Knowledge Representation for Context-aware Systems

Ambient systems and agents in human-agent shared environments require a great amount of contextual knowledge to successfully handle dynamic situations. The generation, integration, and processing of various forms of knowledge, such as semantic and spatial representations, pose a challenge in intelligent systems. To address this, we integrate real world information into a virtual world using a Reflected Reality metaphor, a combination of smart spaces and intelligent virtual environments. Our system serves as a mixed reality knowledge representation and visualization for context-aware systems. It provides tools to infer knowledge at runtime, including a continuous abstraction process, as well as a semantics-based rule system. This provides a continuum of knowledge, ranging from low-level sensory data to high-level semantic facts. In this paper, we present the concept and data model behind our system and describe its implementation. This can serve as a basis for a variety of applications including serious games.
A model-based approach to generate reactive and customizable user interfaces for the web of things

The Internet of Things(IoT) is an extension of the Internet on physical devices and everyday objects that has been facing an increase of its heterogeneity (e.g. devices, protocols, services). The World Wide Web Consortium(W3C) has been developing the Web of Things standards(WoT) to address this issue and it is proving to be a feasible solution by using web protocols and the Thing Description(TD) formal model to expose Web Things(WT) properties, actions and events. However, users still deal with heterogeneity when trying to interact with WTs as they need to download specific apps or access different websites to interact with each of them. This paper's goal is to propose the use of Models at Runtime to enable the creation of a reactive and customizable User Interface(UI) to interact with any WT according to their TD. Reactive in the sense of reflecting changes that occur to the associated WT in real-time or even changing itself to represent a different WT. Customizable meaning it is designed in a way that the aesthetic aspects are detached from the implementation, allowing developers and designers to personalize them.
Exploring Type-2 Fuzzy Logic with Dynamic Rules in IoT Resources Classification

The increasing offer of Internet-connected resources, providing more than one service, confers complexity to the classification procedures and selection of adequate resources to meet customer demands. In this context, this article aims to present a proposal for the classification of resources in IoT, considering non-functional parameters of QoS. The proposal explores two complementary approaches: (i) use of type-2 fuzzy logic; and (ii) dynamic adequacy of the fuzzy rules employed. The results obtained in the classification of the QWS dataset are presented and are satisfactory, adjusting the selection of the most relevant resources according to the preferences specified by the client.
Ontology-Mediated Querying with Horn Description Logics

An ontology-mediated query (OMQ) consists of a database query paired with an ontology. When evaluated on a database, an OMQ returns not only the answers that are already in the database, but also those answers that can be obtained via logical reasoning using rules from ontology. There are many open questions regarding the complexities of problems related to OMQs. Motivated by the use of ontologies in practice, new reasoning problems which have never been considered in the context of ontologies become relevant, since they can improve the usability of ontology enriched systems. This thesis deals with various reasoning problems that emerge from ontology-mediated querying and it investigates the computational complexity of these problems. We focus on ontologies formulated in Horn description logics, which are a popular choice for ontologies in practice. In particular, the thesis gives results regarding the data complexity of OMQ evaluation by completely classifying complexity and rewritability questions for OMQs based on an EL ontology and a conjunctive query. Furthermore, the query-by-example problem, and the expressibility and verification problem in ontology-based data access are introduced and investigated.
Deterministic Policy Gradient with Advantage Function for Fixed Wing UAV Automatic Landing

This paper addresses the autolanding problem for fixed wing unmanned aerial vehicles (UAV) in the presence of the downburst. The proposed method is developed based on the reinforcement learning methodology. The solution consists of a path-tracking controller for the glideslope maneuver and an attitude controller for the flare maneuver. Both controllers are designed in continuous state and action spaces. In our study, two complementary techniques are proposed within the framework of deterministic policy gradient (DPG). First, the advantage function is introduced in the critic network to improve the performance of the learning process. The proposed representation of the action value function consists of two parts: the low-frequency part and the high-frequency one. Second, a two-stream network is developed to tackle with the issue of partially observable Markov decision process (POMDP). The architecture synthesizes past experiences to perform policy evaluations and policy improvements, which has successfully improved the robustness of the learned policy. The described performance of our approach is illustrated in flight simulations under the influence of the wind field.
Blind image blur metric based on orientation-aware local patterns

We develop an effective blind image blur assessment model based on a novel orientation-aware local pattern operator. The resulting metric first proposes an orientation-aware local pattern operator that fully considers the impact of anisotropy of orientation selectivity mechanism and the gradient orientation effect on visual perception. Our results indicate that the proposed descriptor is sensitive to image distortion and can effectively represent orientation information. We thus use it to extract image structure information. In order to enhance features' representation capability for blur image, we extract edge information by a Toggle operator and use it as weight of local patterns to optimize the computed structural statistical features. Finally, a support vector regression method is used to train a predictive model with optimized features and subjective scores. Experimental results obtained on six public databases show that our proposed model performs better than state-of-the-art image blur assessment models.
A Hybrid Reactive Navigation Strategy for a Non-holonomic Mobile Robot in Cluttered Environments

The reactive collision-free navigation problems are challenging due to the limitation of the environment information. In this paper, we propose a novel hybrid reactive navigation strategy for non-holonomic mobile robots in cluttered environments. Our strategy combines both reactive navigation and Q-learning method. We intend to keep the good characteristics of reactive navigation algorithm and Q-learning and overcome the shortcomings of only relying on one of them. The performance of the proposed strategy is verified by computer simulations, and good results are obtained.
Dual attention based fine-grained leukocyte recognition for imbalanced microscopic images

Traditional clinical diagnostic aid systems for medical images are facing challenges of reliability and interpretability. Artificial intelligence has the potential to bring driving changes to disease diagnosis methods through rapid traversal of medical images and efficient classification. However, the application of artificial intelligence in the field of medical image still faces challenges. Our method combines the multiple modalities of attention which consider the most discriminative part in the images. The proposed classification method is tested on the microscopic image dataset with 40 leukocyte categories, which achieves top-1 accuracy of 84.21% and top-5 accuracy of 99.44% during the testing procedure. And experiments on the dermoscopic image dataset show that our method has good generalization ability across multiple imaging modalities.
3D Facial Expression Recognition Based on Multi-View and Prior Knowledge Fusion

This paper presents a novel multi-view convolutional neural network (CNN) model for 3D facial expression recognition (FER). In contrast to existing deep learning-based 3D FER approaches that mainly learn the expressions from frontal facial attribute images, the proposed model incorporates multi-view and facial prior information of the observed 3D face into the learning process. This information is jointly trained in an end-to-end manner to predict the emotion of the input 3D face model. The experiments on public 3D facial expression datasets show that training the CNN with additional information from different views and facial prior knowledge would result in learning more discriminative features as against from a single view. Our model outperforms the state-of-the-art 3D FER methods in term of recognition accuracy indicating its effectiveness. Moreover, the improvement of the proposed model is displayed more clearly in the discrimination of low-intensity facial expressions.
Campus Guide: A Lidar-based Mobile Robot

There has been a large variety of mobile robots developed to demonstrate navigation capabilities on rural roads or highways. However a small amount of work focuses on autonomous navigation in densely populated areas depending only on a 16-line LiDAR. In this paper, we present an autonomous navigation system for mobile robot which can be used for delivery, cleaning, surveillance and so on in industrial areas and campus where the environment is unstructured and dynamic. The perception of the environment relies on the 16-line LiDAR installed on top of the mobile robot. The proposed system is composed of three components: LiDAR-based mapping and re-localization module, traversable path and obstacle detection module, and path planning and trajectory tracking module. We tested our system on a real mobile robot that can run autonomously on a campus in a complex environment.