Miracle-dz/UniFAR-FTUs huggingface dataset:
Multidetector Computed Tomography Pulmonary Angiogram in the Assessment of Myocardial Infarction

Purpose: The objective of this study is to evaluate the effectiveness of nonelectrocardiographic gated multidetector computed tomography (MDCT) performed for the purpose of assessment of pulmonary embolus in the evaluation of myocardial infarction (MI). Methods: We retrospectively evaluated 147 consecutive patients who had a computed tomography angiogram for pulmonary embolus on a 16-detector MDCT. Of this cohort, we determined 11 patients to have acute MIs based on electrocardiogram, positive troponin levels, and clinical findings and history. Four additional patients were found to have chronic MI based on history. The MDCT data were evaluated by an attending Radiologist, blinded to the clinical data, for quantitative and qualitative attenuations. Each MI was assigned a region of the heart based on its anatomical sections. Results: We were able to detect 10 of 15 MIs, resulting in an overall sensitivity and specificity of 66.6% and 91.4%, respectively. The overall accuracy was 76.7%. If non-Q wave MIs are excluded from the data set, the sensitivity increased to 76.9%. Quantitative analysis of each region of the heart showed that there was a statistically significant difference (P < 0.01) between the infracting regions of the heart (69 ± 3.3 SEM) and those with normal perfusion (93.3 ± 1.0 SEM). Conclusions: While this method did prove to be effective at determining patients who had an MI, the low sensitivity and inability to differentiate chronic from acute myocardial infarction suggest that this technique is very limited in clinical practice.
The Seamlessness of Outdoor and Indoor Localization Approaches based on a Ubiquitous Computing Environment: A Survey

In the world today, various ways of positioning alongside location awareness have been developed. They play an important role towards improving the performance of localization system based technologies for indoor and outdoor conditions, which enables the user to clarify a problem, to develop a system that supports various users' needs and assist in their requirements. The outdoor positioning provides an accurate estimation of a location through the use of the global positioning system. However, it cannot be used indoors due to the inherent weakness in the signal. Therefore, the indoor positioning systems require an alternative solution to improve its performance. The integration of indoor and outdoor positioning systems seamlessly is significant towards bridging different systems without obstacle, and helps to support the user with ease. This paper examines previous literature works with regards to different locations and positioning that have enabled integration of indoor and outdoor environments seamlessly. The paper will seek to analyze the historical works in terms of their respective research methods, results and proposals. The associated challenges for the system are included in this paper as well, as a means to enable continuous improvement of the system in a real environment. Through the use of sensor technologies, the system can be utilized to improve daily activities by improving the complexity of the positioning technology.
Application of Spatial Data Mining in Accident Analysis System

Traffic accidents cause enormous losses for our country and plenty of national assets drain away every year, and therefore the task of traffic management is weightier than Mount Tai, but the complexity of traffic accident analysis has brought many difficulties to traffic management and decision-making. A three-layer analysis system based on spatial data mining of GIS is proposed in this paper, this three-layer architecture displays the whole process of accident data extracting, preprocessing and mining and it applies spatial data mining to GIS, which will certainly further expand the width and depth of GIS application field. Finally, this paper introduces the method of developing traffic accident analysis system by using ArcGIS Engine and C#. NET and gives the class realization of system main functions.
Harnessing Energy Consumption in a Smarthome IoT Framework

Internet of Things has advanced significantly in different sectors and industries. It has produced significant home and business applications that contribute in the quality of life and growth of the economy. The integration of technology services through home networking is described as Smart Home – a technology that have been widely used for the past decades to make quality of living better. All of these were made possible with the propagation of Internet of Things using communicating and actuating networks. The study was conducted to determine the relationship of energy consumption in a Smarthome IoT framework. Based on the findings, a decrease of 30kWH of the 210kWH usage for one (1) month was recorded against the average 240kWH consumption of a household for three (3) month period after deploying Multiple Appliances Remote Controller for Home (MARCHome) in a 2-story house to monitor the impact of Internet of Things to the energy management and unseen incidents that causes escalation in energy consumption and fire. Result showed that 240kWH usage can reduce 10% to 30% per kilowatt usage depending on the availability of Internet connection and active use of smart phone. Correspondingly, analysis revealed a computed value of 39.8 where relationship between number of appliances without remote controller and energy consumption is direct wherein value of y at $\mathbf{x}=0$ is 125.86.
A Practical Screening Method for Blockchain-Based Systems

Since Bitcoin and Blockchain became popular, various industries have been interested in applying Blockchain technology to their business. Companies have been working on many Proof-of-Concepts to find good use cases. To find use cases quickly in the fast-changing environment around Blockchain, companies repeat short-term Proof-of-Concepts many times. There is some work on evaluation methods for Blockchain-based systems like the evaluation form validating the feasibility of Blockchain-based systems with comprehensive evaluation items and prototypes. However, the previous evaluation methods do not satisfy the tight time constraints of Proof-of-Concepts conducted by companies. It takes a long time to define evaluation indices, implement measurement tools and run a series of tests for all evaluation items. It also takes more time to repeat the evaluation to compare multiple Blockchain-based systems using different Blockchain software products. In this paper, we propose a practical screening method for Blockchain-based systems. To reduce time spent on Proof-of-Concepts, the screening phase with a simple check form is introduced before the prototyping phase. Throughout the screening phase, a screening form selects a few Blockchain software products which demonstrate good coverage of requirements for a use case in terms of network topology, settlement finality, governance, privacy/confidentiality and application architecture. After choosing a few promising Blockchain software products, the prototyping phase follows. Prototypes using selected Blockchain software products are evaluated by filling comprehensive evaluation items like the previous methods. The time required for Proof-of-Concepts decreases due to eliminating unpromising Blockchain software products in the screening phase. For the evaluation of the proposed method, we had a case study. The result showed that the estimated workload of a Proof-of-Concept decreased by 59% compared to the previous method.
A New Approach to Client Onboarding Using Self-Sovereign Identity and Distributed Ledger

Existing client onboarding and Know Your Customer (KYC) processes are typically slow, expensive and often accomplished in-person. Moreover, the current identity management models in practice deprive users from having complete control over their digital identity data. Users' identity attributes are stored on multiple centralized repositories, which often follow inadequate security policies. In this paper, we take advantage of Hyperledger Indy, a public and permissioned distributed ledger technology (DLT), to develop a digital onboarding framework based on the Self-Sovereign Identity (SSI) principles. With this framework we take a step towards tackling a number of weaknesses in current KYC processes and identity management models, while addressing the requirements associated with SSI, Privacy by Design and European Union's General Data Protection Regulation (GDPR).
Hierarchical Variational Network for User-Diversified & Query-Focused Video Summarization

This paper focuses on the query-focused video summarization, which is an extended task of video summarization and aims to automatically generate user-oriented summary by highlighting frames/shots relevant to the query. This task is different from traditional video summarization in paying attention to users' subjectivity through queries. Diversity is a recognized important property in video summarization. However, existing methods only consider diversity as the dissimilarity between frames/shots which is far from user-oriented summarization. Users' different understandings of video should be an important source of diversity, reflected in the process of eliminating query-unrelated redundancy. To this end, this paper explores user-diversified & query-focused video summarization via a well-devised hierarchical variational network called HVN. HVN has three distinctive characteristics: (i) it has a hierarchical structure to model query-related long-range temporal dependency; (ii) it employs diverse attention mechanisms to encode query-related and context-important information and makes them balanced; (iii) it employs a multilevel self-attention module and a variational autoencoder module to add user-oriented diversity and stochastic factors. Experimental results demonstrate that HVN not only outperforms the state-of-the-arts but also improves the user-oriented diversity to some extent.
Higher curvature wormholes

In general relativity, traversable wormholes are possible provided they do not represent shortcuts in the spacetime. Einstein equations, together with the achronal averaged null energy condition, demand to take longer for an observer to go through the wormhole than through the ambient space. This forbids wormholes connecting two distant regions in the space. The situation is different when higher-curvature corrections are considered. Here, we construct a traversable wormhole solution connecting two asymptotically flat regions, otherwise disconnected. This geometry is an electro-vacuum solution to Lovelock theory of gravity coupled to an Abelian gauge field. The electric flux suffices to support the wormhole throat and to stabilize the solution. In fact, we show that, in contrast to other wormhole solutions previously found in this theory, the one constructed here turns out to be stable under scalar perturbations. We also consider wormholes in AdS. We present a protection argument showing that, while stable traversable wormholes connecting two asymptotically locally AdS$_5$ spaces do exist in the higher-curvature theory, the region of the parameter space where such solutions are admitted lies outside the causality bounds coming from AdS/CFT.
Georeferencing of Road Infrastructure from Photographs using Computer Vision and Deep Learning for Road Safety Applications

Georeferenced information of road infrastructure is crucial for road safety analysis. Unfortunately, for essential structures, such as fences and crash barriers, exact location information and extent is often not available hindering any kind of spatial analysis. For a GIS-based study on wildlife-vehicle collisions (WVCs) and, therein, the impact of these structures, we developed a method to derive this data from video-based road inspections. A deep learning approach was applied to identify fences and barriers in photos and to estimate the extent and location, based on the photos’ metadata and perspective. We used GIS-based analysis and geometric functions to convert this data into georeferenced line segments. For a road network of 113 km, we were able to identify over 88% of all barrier lines. The main problems for the application of this method are infrastructure invisible from the road or hidden behind vegetation, and the small sections along the streets covered by photos not depicting the tops of higher dams or slopes.
A Data Driven Method of Feedforward Compensator Optimization for Autonomous Vehicle Control

A reliable controller is critical for execution of safe and smooth maneuvers of an autonomous vehicle. The controller must be robust to external disturbances, such as road surface, weather, wind conditions, and so on. It also needs to deal with internal variations of vehicle sub-systems, including powertrain inefficiency, measurement errors, time delay, etc. These factors introduce issues in controller performance. In this paper, a feedforward compensator is designed via a data-driven method to model and optimize the controller's performance. Principal Component Analysis (PCA) is applied for extracting influential features, after which a Time Delay Neural Network is adopted to predict control errors over a future time horizon. Based on the predicted error, a feedforward compensator is then designed to improve control performance. Simulation results in different scenarios show that, with the help of with the proposed feedforward compensator, the maximum path tracking error and the steering wheel angle oscillation are improved by 44.4% and 26.7%, respectively.
Using Enterprise Modeling in Development of New Business Models

In the dynamic world of today, enterprises need to be innovative not only in their current line of products and services, but also in their business models. One of the challenges in Business Model Innovation (BMI) is to introduce radical changes in the current business model when entering new markets. Ideas for new models can come from various sources, however each such idea needs to be analysed from the sustainability and implementation perspectives. This paper evaluates whether enterprise modelling can help in analysis of hypotheses for radical changes of BMI. The evaluation is carried on a particular practice of an organization. Analysis of a new idea has been done using a so-called Fractal Enterprise Model (FEM). FEM ties various enterprise business processes together and connects them to enterprise assets (resources) that are used and/or are managed by the processes. FEM has been used to understand which new assets and processes should be acquired, and which existing ones can be reused when planning the implementation of a new business model.
Glycogen storage in multiple muscles of old GSD‐II mice can be rapidly cleared after a single intravenous injection with a modified adenoviral vector expressing hGAA

Glycogen storage disease II (GSD‐II) is an autosomal recessive lysosomal storage disease, due to acid‐alpha‐glucosidase (GAA) deficiency. The disease is characterized by massive glycogen accumulation in the cardiac and skeletal muscles. There is early onset (infantile, also known as Pompe disease) as well as late onset (juvenile and adult) forms of GSD‐II. Few studies have been published to date that have explored the consequences of delivering a potential therapy to either late onset GSD‐II subjects, and/or early onset patients with long‐established muscle pathology. One recent report utilizing GAA‐KO mice transgenically expressing human GAA (hGAA) suggested that long‐established disease in both cardiac and skeletal muscle is likely to prove resistant to therapies. To investigate the potential for disease reversibility in old GSD‐II mice, we studied their responsiveness to exogenous hGAA exposure via a gene therapy approach that we have previously shown to be efficacious in young GAA‐KO mice.
Design and Control of an Intelligent Wheeled Robot

Mobile robotics is an emerging interdisciplinary, which lacks a standardized architecture. With the development of embedded system, Raspberry Pi has become a new control scheme for mobile robots with powerful processing ability, convenient network connectivity, real-time display of images and videos. In this paper, a wheeled mobile robot is designed. The kinematic model of the robot is developed. A series of experiments of motion control are conducted. A control strategy is proposed based on an attitude detection sensor and ultrasonic sensor to enable the robot with the capability of state perception and autonomous obstacle avoidance. The experimental and simulation results in different environments demonstrate the effectiveness of the developed wheeled mobile robot.
StreamBox-TZ: Secure Stream Analytics at the Edge with TrustZone

While it is compelling to process large streams of IoT data on the cloud edge, doing so exposes the data to a sophisticated, vulnerable software stack on the edge and hence security threats. To this end, we advocate isolating the data and its computations in a trusted execution environment (TEE) on the edge, shielding them from the remaining edge software stack which we deem untrusted. This approach faces two major challenges: (1) executing high-throughput, low-delay stream analytics in a single TEE, which is constrained by a low trusted computing base (TCB) and limited physical memory; (2) verifying execution of stream analytics as the execution involves untrusted software components on the edge. In response, we present StreamBox-TZ (SBT), a stream analytics engine for an edge platform that offers strong data security, verifiable results, and good performance. SBT contributes a data plane designed and optimized for a TEE based on ARM TrustZone. It supports continuous remote attestation for analytics correctness and result freshness while incurring low overhead. SBT only adds 42.5 KB executable to the TCB (16% of the entire TCB). On an octa core ARMv8 platform, it delivers the state-of-the-art performance by processing input events up to 140 MB/sec (12M events/sec) with sub-second delay. The overhead incurred by SBT's security mechanism is less than 25%.
Measuring urban vulnerability to climate change using an integrated approach, assessing climate risks in Beijing

This study is responding to the recommendation made by IPCC’s fifth Assessment Report on establishing a standard for measuring and reporting climate risk and vulnerability. It exemplifies the assessment of urban vulnerability to climate change by an integrated approach. The results indicate that Beijing is highly exposed to multiple climate threats in the context of global climate change, specifically urban heat waves, urban drainage floods and drought. Vulnerabilities to the climatic threats of heat waves, drainage floods and droughts have increased by 5%–15% during the period of 2008–2016 in Beijing. High vulnerabilities to both heat waves and drainage floods have been observed in the urban downtown area and high vulnerability to droughts have been observed in the outskirts. This vulnerability assessment, which addressed climatic threats, provides a holistic understanding of the susceptibility to climate change that could facilitate adaptation to climate change in the future. The developments of threats like flooding, heat waves and droughts are analyzed separately for 16 districts and an integrated vulnerability index for all of Beijing is provided as well.
BRST-BFV and BRST-BV Lagrangians for Bosonic Fields with Continuous Spin on $R^{1,d-1}$

Gauge-invariant Lagrangian descriptions for a free bosonic scalar field of continuous spin in a d-dimensional Minkowski space-time using a metric-like formulation are constructed on the basis of a constrained BRST–BFV approach we propose. The resulting formulations contain different sets of auxiliary fields, depending on the manner of a partial gauge-fixing and a resolution of some of the equations of motion for a BRSTunfolded first-stage reducible gauge theory. To reach an equivalence of the resulting BRST-unfolded constrained Lagrangian equations of motion with the initial irreducible Poincare group conditions of a Bargmann–Wigner type, it is demonstrated that one should replace the field in these conditions by a class of gauge-equivalent configurations. Triplet-like, doublet-like and non-gauge constrained Lagrangian descriptions, as well as a quartet-like unconstrained Lagrangian formulation, are derived using both Fronsdal and new tensor fields, in particular, BRST–BV actions, in the minimal sector of the respective field and antifield configurations, are constructed in an explicit way.
A Comprehensive Evaluation of Health-Related Life Quality Assessment Through Head and Neck, Prostate, Breast, Lung, and Skin Cancer in Adults

Health assessment data assists the well-being and patient care teams' process in drawing up a care and assistance plan and comprehending the requirements of the patient. Comprehensive and precise data about the Quality of Life of cancer patients play a significant part in the development and organization of cancer patient care. Quality of Life has been used to mean a variety of various things, such as health situation, physical function, symptoms, psychosocial modification, well-being, enjoyment of life, and happiness. Chronic diseases such as cancer are among the disorders that severely affect people's health and consequently their Quality of Life. Cancer patients experience a range of symptoms, including pain and various physical and mental conditions that negatively affect their Quality of Life. In this article, we examined cancer and the impact that this disease can have on the Quality of Life of cancer patients. The cancers examined in this article include head and neck, prostate, breast, lung, and skin cancers. We also discussed health assessment and the importance and purpose of studying patients' Quality of Life, especially cancer patients. The various signs and symptoms of the disease that affect the Quality of Life of patients were also reviewed.
Onboarding with a Chat - the effects of Message Interactivity and Platform Self-Disclosure on User Disclosure propensity

Activating users on online platforms is a critical endeavor that requires the employment of adequate user onboarding strategies, which focus on converting visitors into revenue-generating users. Despite a robust understanding of the antecedents of user onboarding behavior, researchers have devoted only little attention towards how platforms can actively influence desired user onboarding outcomes. Drawing on social response as well as social exchange theory, this study examines how disembodied interfaces like chatbots can facilitate the user onboarding process. In cooperation with a German startup company, we empirically tested in a randomized field experiment with 2095 visitors how low vs. high message interactivity (i.e., static vs. conversational presentation of requests) and platform selfdisclosure (i.e., a platform providing information about itself) affect user disclosure propensity (i.e., likelihood that a user discloses information). Our results demonstrate that users in high message interaction conditions were significantly more likely to self-disclose in contrast to low message interaction conditions, while platform self-disclosure had a significant positive effect as well. Furthermore, high message interactivity significantly amplified the effect of platform self-disclosure on user disclosure propensity in contrast to low message interactivity. Consequently, our study provides novel findings on the effectiveness of disembodied interfaces to improve user onboarding behavior.