Miracle-dz/UniFAR-FTUs huggingface dataset:
Features for Cross Spectral Image Matching: A Survey

In recent years, cross spectral matching has been gaining attention in various biometric systems for identification and verification purposes. Cross spectral matching allows images taken under different electromagnetic spectrums to match each other. In cross spectral matching, one of the keys for successful matching is determined by the features used for representing an image. Therefore, the feature extraction step becomes an essential task. Researchers have improved matching accuracy by developing robust features. This paper presents most commonly selected features used in cross spectral matching. This survey covers basic concepts of cross spectral matching, visual and thermal features extraction, and state of the art descriptors. In the end, this paper provides a description of better feature selection methods in cross spectral matching.
A Visual Analytics Approach for Traffic Flow Prediction Ensembles

Trafﬁc ﬂow prediction plays a signiﬁcant role in Intelligent Transportation Systems (ITS). Due to the variety of prediction models, the prediction results form an intricate structure of ensembles and hence leave a challenge of understanding and evaluating the ensembles from different perspectives. In this paper, we propose a novel visual analytics approach for analyzing the predicted ensembles. Our approach models the uncertainty of different trafﬁc ﬂow prediction results. The variations of space, time, and network structures of those results are presented with the visualization designs. The visual interface provides a suite of interactions to enhance exploration of the ensembles. With the system, analysts can discover some intrinsic patterns in the ensemble. We use real-world urban trafﬁc data to demonstrate the effectiveness of our system.
Papel da dosagem seriada de troponina nos pacientes com suspeita de contusão miocárdica após trauma torácico fechado

BACKGROUND AND OBJECTIVES: Myocardial contusion is often associated with blunt chest trauma. Its diagnosis is challenging to the professionals who work in emergency department due to nonspecific symptoms and the lack of auxiliary exams with enough accuracy to diagnose. Among the available diagnostic tools, the biomarkers of myocardial injury troponin I and troponin T have stood out. Troponins are proteins of the citocellular apparatus, released into the bloodstream only after the disruption of myocytes cellular membrane. Therefore they are highly specific to detect myocardial injuries. CONTENTS: We performed a clinical review using the electronic databases MedLine and LILACS from January 1980 to November 2006 about the importance of a serial measurement of troponin I and T as a diagnostic tool as well as predictor of unfavorable clinical outcome in patients with myocardial contusion after a blunt chest trauma. CONCLUSIONS: Although troponins I and T are more specific than usual biomarkers CKMB and CK, these two first biomarkers show a low sensitivity and positive predictive value to diagnosis myocardial contusion. Patients with ECG abnormalities, troponins elevations or both should remain in an intensive care unit (ICU) for at least 24 hours, period in which they cam develop most of the complications related to myocardial contusion.
Semantic Adversarial Network with Multi-scale Pyramid Attention for Video Classification

Two-stream architecture have shown strong performance in video classification task. The key idea is to learn spatiotemporal features by fusing convolutional networks spatially and temporally. However, there are some problems within such architecture. First, it relies on optical flow to model temporal information, which are often expensive to compute and store. Second, it has limited ability to capture details and local context information for video data. Third, it lacks explicit semantic guidance that greatly decrease the classification performance. In this paper, we proposed a new two-stream based deep framework for video classification to discover spatial and temporal information only from RGB frames, moreover, the multi-scale pyramid attention (MPA) layer and the semantic adversarial learning (SAL) module is introduced and integrated in our framework. The MPA enables the network capturing global and local feature to generate a comprehensive representation for video, and the SAL can make this representation gradually approximate to the real video semantics in an adversarial manner. Experimental results on two public benchmarks demonstrate our proposed methods achieves state-of-the-art results on standard video datasets.
Distributed Approach for Temporal–Spatial Charging Coordination of Plug-in Electric Taxi Fleet

This paper considers a city with a large fleet of plug-in electric taxis (PETs) and studies the charging coordination problem of the fleet. The goal is to reduce charging cost for each PET, defined as the loss of service income caused by charging, by wisely choosing when and where to charge. Considering the fact that the fleet can contain thousands of autonomous PETs, this problem is approached in a distributed way. In detail, a two-stage decision process is designed for each PET in an online fashion upon receiving real-time information. In the first stage, a thresholding method is proposed to assist a PET driver in choosing a proper time slot for charging, with comprehensive consideration of state of charge of PET, time varying income, and queuing status at charging stations (CSs). In the second stage, a game-theoretical approach is devised for PETs to select CSs, so that the traveling and queuing time of each PET can be reduced with fairness. Extensive numerical simulations illustrate the following threefold benefits of the proposed approach: it can effectively reduce the charging cost for PETs, enhance the utilization ratio for CSs, and also flatten the unevenness of charging request for power grid.
New-Onset Purpura in a Patient with Recently Diagnosed Temporal Arteritis

Diagnosis: Disseminated strongyloidiasis. Our patient received treatment with thiabendazole and ivermectin via nasogastric tube. Despite the appropriate antimicrobial and aggressive supportive treatment, she died on day 23 of hospitalization, -2 months after commencing prednisone treatment for biopsy-proven temporal arteritis. Strongyloidiasis is an intestinal infection caused by the parasitic nematode Strongyloides stercoralis. Distinctive characteristics of this parasite include its ability to persist and replicate within the host for decades, producing minimal or no symptoms, and its potential to cause life-threatening infection in a host with a compromised immune system [1-3]. Strongyloidiasis affects 30-100 million people worldwide, and it is endemic in Southeast Asia, Latin America, sub-Saharan Africa, and parts of the southeastern United States [2]. In the United States, the highest prevalence rates (4%) are found in eastern Kentucky and rural Tennessee [3]. Our patient was born and raised in Kentucky and had lived in Dayton, Ohio, for 60 years. We speculate that she acquired the infection in early childhood while walking barefoot in soil contaminated with S. stercoralis larvae.
Research on Multi-source Data Integration in Data Platform of Power Distribution and Utilization

This paper presents a design for integrating the data of multiple systems of power distribution and utilization into a platform system. Two ways of using source data to build power grid model, based on object tree or object classification, are described in detail. With the help of path name, association, and normative name, the data of the source system and the existing data of the platform are matched and mapped, the data from multiple source systems are integrated together, and the code mapping between the systems is constructed. Cleaning and standardizing the data in the process of data collection, data problem will be submitted to the maintenance staff to repair, so the quality of the data into the platform is strictly controlled. Record key data in the process of data collection makes the source and changes traceable. The data collection system implemented in this design can improve data quality, facilitate manual correction and make the data collection process controllable.
Anticipatory Quality Assessment Metric for Measuring Data Hiding Imperceptibility

Steganography is the art of writing concealed information in a way that it cannot arouse suspicion about its existence. The hidden data must not raise the attention of attackers to the existence of secret information in the host image. Therefore, ensuring high imperceptibility of the steganography is very essntial. Imperceptibility evaluation of the stego ﬁle can be determined by subjective or objective evaluation. Subjective evaluation is based on human judgment and opinion determining visual quality. This kind of evaluation, is not eﬃcient as it is expensive, time consuming, and in any way cannot be referred to as an automatic system. On the other hand, objective metrics are based on mathematical concepts and might have poor correlation with subjective evaluation. This paper focuses on predicting the subjective quality using anticipatory objective Image Quality metric (IQM). The primary objective of this paper is to develop a systematic method of using HVS for image quality assessment. Moreover, the new metric method use a mathematical approaches for image quality measures predict the image quality measure basing on human perception.
A reference architecture of healthcare supportive home systems from a systems-of-systems perspective

Population ageing has been taking place all over the world, being estimated that 2.1 billion people will be aged 60 or over in 2050. Healthcare Supportive Home (HSH) Systems have been proposed to overcome the high demand of remote home care for assisting an increasing number of elderly people living alone. Since a heterogeneous team of healthcare professionals need to collaborate to continually monitor health status of chronic patients, a cooperation of pre-existing e-Health systems, both outside and inside home, is required. However, current HSH solutions are proprietary, monolithic, high coupled, and expensive, and most of them do not consider their interoperation neither with distributed and external e-Health systems, nor with systems running inside the home (e.g., companion robots or activity monitors). These systems are sometimes designed based on local legislations, specific health system configurations (e.g., public, private or mixed), care plan protocols, and technological settings available; therefore, their reusability in other contexts is sometimes limited. As a consequence, these systems provide a limited view of patient health status, are difficult to evolve regarding the evolution of patient’s health profile, do not allow continuous patients monitoring, and present limitations to support the self-management of multiple chronic conditions. To contribute to solve the aforementioned challenges, this thesis establishes HomecARe, a reference architecture for supporting the development of quality HSH systems. HomecARe considers HSH systems as Systems-of-Systems (SoS) (i.e., large, complex systems composed of heterogeneous, distributed, and operational and managerial independent systems), which achieve their missions (e.g., improvement of patients’ quality of life) through the behavior that emerges as result of collaborations among their constituents. To establish HomecARe, a systematic process to engineer reference architectures was adopted. As a result, HomecARe presents domain knowledge and architectural solutions (i.e., architectural patterns and tactics) described using conceptual, mission, and quality architectural viewpoints. To assess HomecARe, a case study was performed by instantiating HomecARe to design the software architecture of DiaManT@Home, a HSH system to assist at home patients suffering of diabetes mellitus. Results evidenced HomecARe is a viable reference architecture to guide the development of reusable, interoperable, reliable, secure, and adaptive HSH systems, bringing important contributions for the areas of e-Health, software architecture, and reference architecture for SoS.
Joint Load Balancing and Offloading in Vehicular Edge Computing and Networks

The emergence of computation intensive and delay sensitive on-vehicle applications makes it quite a challenge for vehicles to be able to provide the required level of computation capacity, and thus the performance. Vehicular edge computing (VEC) is a new computing paradigm with a great potential to enhance vehicular performance by offloading applications from the resource-constrained vehicles to lightweight and ubiquitous VEC servers. Nevertheless, offloading schemes, where all vehicles offload their tasks to the same VEC server, can limit the performance gain due to overload. To address this problem, in this paper, we propose integrating load balancing with offloading, and study resource allocation for a multiuser multiserver VEC system. First, we formulate the joint load balancing and offloading problem as a mixed integer nonlinear programming problem to maximize system utility. Particularly, we take IEEE 802.11p protocol into consideration for modeling the system utility. Then, we decouple the problem as two subproblems and develop a low-complexity algorithm to jointly make VEC server selection, and optimize offloading ratio and computation resource. Numerical results illustrate that the proposed algorithm exhibits fast convergence and demonstrates the superior performance of our joint optimal VEC server selection and offloading algorithm compared to the benchmark solutions.
Groupware for collaborative tailoring

In everyday work, teamwork in the presence of the tools, the resources, and the processes that enable work is mostly transparent to the workers. They center their attention on performing work. However, a noticeable change in the work conditions, in the required quality of the product, or in the perceived results of work, may be experienced as a breakdown that brings teamwork to the center of attention. To deal with breakdowns it is currently common practice to include tailoring facilities in groupware systems. The extent to which these facilities are provided, and the way in which they are implemented, determine the power users have to change the groupware system. Determining these facilities has been the focus of most research on tailorability in CSCW. How collaborative tailoring (defined as, collaboration for and in tailoring) can be facilitated remains as yet undetermined. This thesis tackles the problem of the lack of computer support for dis- tributed team members that need to perform tailoring in the context of team- work. The challenge of tailoring in the context of teamwork is to understand and support the needs of the group members, from the moment they encounter a breakdown during work until they have enacted the changes they deem nec- essary. This thesis is based on the premise of participation as a means to achieve acceptance of change. The approach to support collaborative tailoring of team- work presented in this thesis consists of a method for collaborative breakdown handling, a selection of specific groupware tools to be used for the deliberation activities defined by the method, and guidance in the form of scaffoldings for the application of the method. Breakdowns can also occur during tailoring. To deal with breakdowns that occur during tailoring, the method, the tools, and the scaffolding can be tailored. The proposed support for collaborative tailor- ing of teamwork is delivered as a stand-alone groupware system for collaborative tailoring. The system can be deployed along existing groupware systems, thus extending them with support for collaborative tailoring. This thesis exceeds related work by approaching tailoring of teamwork as a social system with a model that explains tailoring as the result of collaborative breakdown handling. The requirements of communication, collaboration, co- operation and coordination, and negotiation observed in the social system are supported by the corresponding technical system. The approach in this thesis is not limited to its application in a particular scenario or groupware system. The only requirement is that the target system/scenario can be tailored. The approach has been conceived to enable and support its own evolution as the result of its tailoring.
Tagging Classical Arabic Text using Available Morphological Analysers and Part of Speech Taggers

Focusing on Classical Arabic, this paper in its first part evaluates morphological analysers and POS taggers that are available freely for research purposes, are designed for Modern Standard Arabic (MSA) or Classical Arabic (CA), are able to analyse all forms of words, and have academic credibility. We list and compare supported features of each tool, and how they differ in the format of the output, segmentation, Part-of-Speech (POS) tags and morphological features. We demonstrate a sample output of each analyser against one CA fully-vowelized sentence. This evaluation serves as a guide in choosing the best tool that suits research needs. In the second part, we report the accuracy and coverage of tagging a set of classical Arabic vocabulary extracted from classical texts. The results show a drop in the accuracy and coverage and suggest an ensemble method might increase accuracy and coverage for classical Arabic.
Scalable Synchronization in Shared-Memory Systems: Extrapolating, Adapting, Tuning

As hardware evolves, so do the needs of applications. To increase the performance of an application, there exist two well-known approaches. These are scaling up an application, using a larger multi-core platform, or scaling out, by distributing work to multiple machines. Both approaches are typically implemented using the shared-memory and message-passing programming models. In both programming models, and for a wide range of workloads, there is contention for access to data, and applications need synchronization. On modern multicore platforms with tens of cores and network interconnects with sub-millisecond latencies, synchronization bottlenecks can significantly limit the scalability of an application. This dissertation studies the scalability of synchronization in shared-memory settings and focuses on the changes brought upon by modern hardware advances in processors and network interconnects. As we show, current approaches to understanding the scalability of applications, as well as prevalent synchronization primitives, are not designed for modern workloads and hardware platforms. We then propose methods and techniques that take advantage of hardware and workload knowledge to better serve application needs. We first look into better understanding the scalability of applications, using low-level performance metrics in both hardware and software. We introduce ESTIMA, an easy-to-use tool for extrapolating the scalability of in-memory applications. The key idea underlying ESTIMA is the use of stalled cycles in both hardware (e.g., cycles spent waiting for cache line fetches or busy locks), as well as software (e.g., cycles spent on synchronization, or on failed Software Transactional Memory transactions). ESTIMA measures stalled cycles on a few cores and extrapolates them to more cores, estimating the amount of waiting in the system. We then focus on locking, a common way of synchronizing data accesses in a shared-memory setting. Typically, contention for data limits the scalability of an application. A poor choice of locking algorithm can further impact scalability. We introduce GLS, a middleware that makes lock-based programming simple and effective. In contrast to classic lock libraries, GLS does not require any effort from the programmer for allocating and initializing locks, nor for selecting the appropriate locking strategy. GLS relies on GLK, a generic lock algorithm that dynamically adapts to the contention level on the lock object, delivering the best performance among simple spinlocks, scalable queue-based locks, and blocking locks. Finally, we study the performance of distributed systems that offer transactions, by exposing shared-memory semantics. Recently, such systems have seen renewed interest due to
Architecting Microservices: Practical Opportunities and Challenges

ABSTRACT Contemporary highly dynamic technology and business environments, coupled with digitally savvy customers, are forcing both private and public organizations to continuously innovate and update their Information and Communication Technology (ICT) service offerings. In order to decrease the development cycles and ensure continuous delivery, many organizations are adopting new practices aimed at unifying software development and operations (i.e., DevOps). However, monolithic architectures of many large systems underpinning ICT services are severely restricting the effectiveness of such efforts. Although the latest architectural trend, microservices, is promising to solve many of the problems associated with monolithic software architectures, there is significant disagreement on when microservice architecture should be adopted, as well as how it may be best implemented. Given the limited empirical research on the topic, this paper identifies and discusses a range of opportunities and challenges associated with the adoption and implementation of microservices. The findings presented in the paper have been derived from in-depth interviews with 19 ICT architects with significant experience in large corporate systems, middleware, service oriented architectures, and, to a somewhat more limited extent, microservices.
Development of KIINCE: A kinetic feedback-based robotic environment for study of neuromuscular coordination and rehabilitation of human standing and walking

Introduction The objective of this article is to introduce the robotic platform KIINCE and its emphasis on the potential of kinetic objectives for studying and training human walking and standing. The device is motivated by the need to characterize and train lower limb muscle coordination to address balance deficits in impaired walking and standing. Methods The device measures the forces between the user and his or her environment, particularly the force of the ground on the feet (F) that reflects lower limb joint torque coordination. In an environment that allows for exploration of the user’s capabilities, various forms of real-time feedback guide neural training to produce F appropriate for remaining upright. Control of the foot plate motion is configurable and may be user driven or prescribed. Design choices are motivated from theory of motor control and learning as well as empirical observations of F during walking and standing. Results Preliminary studies of impaired individuals demonstrate the feasibility and potential utility of patient interaction with kinetic immersive interface for neuromuscular coordination enhancement. Conclusion Applications include study and rehabilitation of standing and walking after injury, amputation, and neurological insult, with an initial focus on stroke discussed here.