Miracle-dz/UniFAR-FTUs huggingface dataset:
A static and fast calibration method for line scan camera based on cross-ratio invariance

This paper presents a new calibration method of line scan camera model based on cross-ratio invariance using a calibration pattern on the top of the gauge block. Instead of using the 3D pattern, planner pattern with precise motion platform, or pre-calibrated matrix camera, only a planner pattern with gauge blocks is used in this new method. A group of data on the line scan camera projection plane is obtained by placing the planner pattern at a different height. Line scan camera parameters were calibrated through a derived mathematical model. This method is simple, inexpensive and easy to use. The experimental results demonstrated that the average reprojection error of this method is 0.0431, which is 62.2% lower than the traditional method, which proves the feasibility and effectiveness of this method.
Review of Cochrane Interactive Learning: Conducting an Intervention Review

Our team of six academic librarians, one a Cochrane author, with a combined 100+ years of experience in the athenaeumic arts, are seeking to determine if the eleven Cochrane Interactive Learning (CIL) modules (https://training.cochrane.org/interactivelearning), with an assumed fifteen contact hours, are sufficient to lead a novice through the systematic review labyrinth to achieve the goal of publication or presentation.
Pre-Evaluation method of the experiential architecture based on multidimensional physiological perception

ABSTRACT With a people-oriented principle, contemporary architectural design regards the users’ spiritual and emotional satisfaction as fundamental needs. However, it was tough for designers to collect and quantify the users’ accurate perception of a design scheme in the past. This research aims to propose a method incorporating virtual reality (VR) and sensors, and wearable technology to pre-evaluate the impact of spatial schemes on user experience. Specifically, it collected users’ physiological signals to detect emotions and eye-tracking data to understand visual attention during their roaming in a VR environment. An empirical design scheme for the International School of Design’s outdoor spaces in Harbin Institute of Technology was adopted as a study case. For this purpose, multiple groups of contrast schemes were established in VR for users to experience. Participants’ physiological signals were collected and analyzed using the paired-samples T-test method. Subjective questionnaire data were also collected to assist in the verification of objective physiological data. The results showed that designers could evaluate the experience of the designs by comparing user reactions through analysis of physiological signals. Simultaneously, this evaluation method explores a feasible way for designers to promote architecture designs to be more humanized and scientific. GRAPHICAL ABSTRACT
Attitudes, Barriers, Motivations to Sun Protection in Reunion Island’s Schools: Qualitative Study

Abstract The incidence of melanoma in Reunion Island is on the rise and is now one of the highest worldwide. Although the main risk factor of melanoma is sun exposure during childhood, sun protection measures remain insufficient in Reunionese schools. From November 2019 to November 2020, we conducted a qualitative study to explore the attitudes, barriers, and motivations to sun protection among the main actors of children’s protection in Reunion Island. Individual semi-directive interviews were performed with 14 children aged 6 to 10 years, 13 parents, and 13 teachers. The interviews were recorded and transcribed. Relevant data were coded, triangulated, analyzed, and then modeled following the methodology of grounded theory. Sufficiency of the data was sought. All 40 participants described their sun protection habits. Protection was lower during school activities than during leisure activities. Parents identified several practical and financial barriers to sun protection. Teachers pointed out the lack of adequate infrastructure and sun protection training. Responsibility for children’s protection was a point of disagreement between parents and teachers. Children limited their use of protection, mainly for reasons of comfort. Children’s sun protection in schools is the responsibility of educational staff, parents, and society at large. Improving communication between these various actors is necessary. Sun safety campaigns and reorganization of the school environment would allow for better protection of the child population.
The Role of Future Orientation and Self-Mastery After Bullying Victimization: Associations With Depression

The high prevalence of bullying is accompanied by research suggesting that bullying victimization predicts depressive symptoms during late adolescence. However, the processes through which bullying may lead to depression are not fully understood. This study used structural equation modeling to examine future orientation and self-mastery in the relationship between bullying and depression. Researchers used three waves of data (N = 448; Mage = 14.83; 52% female) in the Youth Development Study. The indirect pathway from bullying victimization to depressive symptoms through self-mastery was significant, suggesting that self-mastery may mediate the relationship. Results demonstrate that bullying victimization has long-term effects on adolescents’ mental health, and self-mastery can serve as a potential target for intervention and prevention.
Fairtrade certification in the banana hired labour sector

Evidence is needed about the difference that certification makes to workers on banana plantations. The Fairtrade system is therefore investing in monitoring to understand the difference certification makes to banana workers’ employment, living and working conditions, and empowerment. This study meets this need by gathering data on a range of indicators. This study 1) gathers baseline data on indicators and themes that monitor the progress of implementation of Fairtrade’s revised hired labour standards on certified plantations in key banana origins; 2) based on this data it researches and analyses the difference that Fairtrade makes across key themes in comparison to non-certified contexts; it prioritises workers’ voices and perspectives in achieving the objectives of the study. It particularly focuses on understanding the role of Fairtrade in supporting worker empowerment and empowerment-related goals. Focus countries are Ghana, Colombia and the Dominican Republic.
Effect of slenderness ratio on the reliability-based serviceability limit state design of augered cast-in-place piles

This study investigated factors that control the reliability of Augered Cast-In-Place (ACIP) piles in predominately cohesionless soils under axial compression at the Serviceability Limit State (SLS). A simple probabilistic hyperbolic model was used to account the uncertainty in the load-displacement relationship using correlated bivariate curve-fitting parameters. Contrary to previous studies, the curvefitting parameters were found to be dependent on pile slenderness ratio (D/B) and the effect of D/B and other pertinent variables (e.g., uncertainty in capacity, displacement) on SLS reliability was investigated using a First-Order Reliability Method (FORM). The D/B ratio had a considerable effect on foundation reliability, illustrating the importance of the dependence between the load-displacement behavior (i.e. curve-fitting parameters) and pile geometry and stiffness. In general, the uncertainty in the capacity model had a larger effect on reliability than that of the allowable displacement; the reliability index was found to approach an upper bound limit regardless of the level of uncertainty in allowable displacement and the pile capacity model. Design [LRFD]). RBD procedures for Augered Cast-In-Place (ACIP) piles (e.g., Stuedlein et al. 2012) are not yet accepted in codes. Owing to the lack of model statistics for pile displacement, foundation reliability at the SLS is not as well understood compared to the ULS (Phoon et al. 2006). In order to assess foundation reliability at the SLS, Phoon (2006) proposed a simple probabilistic hyperbolic model that accounts for the uncertainty in the entire loaddisplacement relationship using a bivariate random vector consisting of hyperbolic curve-fitting parameters, which were found to be correlated and non-normally distributed. Phoon & Kulhawy (2008) describe a translational model to incorporate the correlated random variables into reliability calculations using a database of 40 loading tests on ACIP piles. This study used an expanded database to investigate factors affecting the reliability of ACIP piles at the SLS. Contrary to Phoon & Kulhawy (2008), the hyperbolic model parameters were determined to be dependent on the pile slenderness ratio. The dependence was removed by transforming the model parameters, which were then used to assess foundation reliability for different pile geometries. In order to determine the variables which govern reliability, a parametric study was conducted by varying the mean and uncertainty of allowable displacement, uncertainty of predicted resistance, and the slenderness ratio.
Improving Pain Assessment Using Vital Signs and Pain Medication for Patients With Sickle Cell Disease: Retrospective Study

Background Sickle cell disease (SCD) is the most common inherited blood disorder affecting millions of people worldwide. Most patients with SCD experience repeated, unpredictable episodes of severe pain. These pain episodes are the leading cause of emergency department visits among patients with SCD and may last for several weeks. Arguably, the most challenging aspect of treating pain episodes in SCD is assessing and interpreting a patient’s pain intensity level. Objective This study aims to learn deep feature representations of subjective pain trajectories using objective physiological signals collected from electronic health records. Methods This study used electronic health record data collected from 496 Duke University Medical Center participants over 5 consecutive years. Each record contained measures for 6 vital signs and the patient’s self-reported pain score, with an ordinal range from 0 (no pain) to 10 (severe and unbearable pain). We also extracted 3 features related to medication: medication type, medication status (given or applied, or missed or removed or due), and total medication dosage (mg/mL). We used variational autoencoders for representation learning and designed machine learning classification algorithms to build pain prediction models. We evaluated our results using an accuracy and confusion matrix and visualized the qualitative data representations. Results We designed a classification model using raw data and deep representational learning to predict subjective pain scores with average accuracies of 82.8%, 70.6%, 49.3%, and 47.4% for 2-point, 4-point, 6-point, and 11-point pain ratings, respectively. We observed that random forest classification models trained on deep represented features outperformed models trained on unrepresented data for all pain rating scales. We observed that at varying Likert scales, our models performed better when provided with medication data along with vital signs data. We visualized the data representations to understand the underlying latent representations, indicating neighboring representations for similar pain scores with a higher resolution of pain ratings. Conclusions Our results demonstrate that medication information (the type of medication, total medication dosage, and whether the medication was given or missed) can significantly improve subjective pain prediction modeling compared with modeling with only vital signs. This study shows promise in data-driven estimated pain scores that will help clinicians with additional information about the patient’s condition, in addition to the patient’s self-reported pain scores.
A Resource-Efficient Predictive Resource Provisioning System in Cloud Systems

In cloud systems, demand-prediction based resource provisioning schemes help assure the SLOs (service level objectives) of cloud tenants. We notice that if a provisioning scheme does not exclude bursts from historical resource demands in normal demand prediction or always uses a large padding to correct under-prediction, it will lead to resource over-provisioning and low resource utilization. To improve the previous schemes, in this paper, we present a Resource-efficient Predictive Resource Provisioning system in clouds (RPRP) that excludes bursts in demand prediction and has algorithms to specifically handle bursts to avoid resource over-provisioning. Rather than setting padding to a possibly high value, RPRP has a load-dependent padding algorithm that adaptively determines padding based on predicted demands. To handle bursts, RPRP has a burst-resilient shared padding algorithm that reserves resource shared by multiple co-located VMs rather than for individual VMs. It also embodies a responsive padding algorithm that adaptively adjusts padding to recover from both under-provisioning and over-provisioning. We implemented RPRP on top of Xen and conducted both trace-driven simulation and real-world testbed experiments. The experimental results show that RPRP achieves higher resource utilization, more accurate demand predictions, and fewer SLO violations than previous schemes.
Design and implementation of a virtual networking framework for the mobilityfirst future internet architecture

OF THE THESIS DESIGN AND IMPLEMENTATION OF A VIRTUAL NETWORKING FRAMEWORK FOR THE MOBILITYFIRST FUTURE INTERNET ARCHITECTURE by Aishwarya Babu Thesis Director: Dipankar Raychaudhuri This thesis presents the design and implementation of a virtual networking framework for the MobilityFirst (MF) future Internet architecture (FIA). The proposed design leverages the key feature of MobilityFirst by which all network-attached object are given a globally unique identifiers (GUID). Virtual networks (VNs) are supported in this architecture by assigning a virtual network GUID to each VN, and then using the global name resolution service (GNRS) to dynamically maintain the topology information. Virtual networks in MobilityFirst also use an additional routing update message between nodes in the VN, enabling new capabilities such as application specific routing (ASR). The proposed virtual networking processing components are integrated into the prototype MobilityFirst software router which is based on the open source Click routing platform. Routing decisions are made using virtual routing tables maintained at each virtual router and these virtual tables are populated by VN control messages.
Achieving Privacy-Preserving Discrete Fréchet Distance Range Queries

The advances in Internet of Things, Big Data, and machine learning technologies have greatly transformed our daily lives into much more intelligent ones by offering various promising services. Among those services, the discrete Fréchet distance (DFD) range query, which aims to obtain a set of trajectories whose distances to a given query trajectory do not exceed a given threshold, has been widely applied to support applications such as vehicle trajectory clustering and other data processing tasks. Meanwhile, due to the huge data volume issue in the Big Data era, there is a trend towards outsourcing various query services to the cloud for achieving a better performance. However, since the cloud is not fully trustable, designing privacy-preserving query services becomes a research focus. Over the past years, many schemes focusing on privacy-preserving trajectory analysis have been proposed, but none of them can well support privacy-preserving DFD range queries. Aiming at addressing this challenge, this paper proposes a novel privacy-preserving DFD range query scheme, in which queries are conducted in a filtration-and-verification manner and the privacy of the dataset and queries can be preserved. Specifically, by indexing the dataset with two R-trees, a query can be conducted by i) querying the two R-trees to obtain a candidate set and ii) verifying each trajectory in the set, which involve two basic operations, namely, rectangle intersection detection and proximity detection. To preserve the privacy of the dataset and queries, we build the two basic operations upon a novel Inner-Product Preserving Encryption (IPPE) scheme, which is proved to be selectively secure with trivial leakages. Besides, extensive experiments are conducted, and the results demonstrate that our proposed scheme can significantly reduce the computational cost by effectively reducing the candidate set’s size.
Optimal Placement of Micro-services Chains in a Fog Infrastructure

: Fog computing emerged as a novel approach to deliver micro-services that support innovative applications. This paradigm is consistent with the modern approach to application development, that leverages the composition of small micro-services that can be combined to create value-added applications. These applications typically require the access from distributed data sources, such as sensors located in multiple geographic locations or mobile users. In such scenarios, the traditional cloud approach is not suitable because latency constraints may not be compatible with having time-critical computations occurring on a far away data-center; furthermore, the amount of data to exchange may cause high costs imposed by the cloud pricing model. A layer of fog nodes close to application consumers can host pre-processing and data aggregation tasks that can reduce the response time of latency-sensitive elaboration as well as the trafﬁc to the cloud data-centers. However, the problem of smartly placing micro-services over fog nodes that can fulﬁll Service Level Agreements is far more complex than in the more controlled scenario of cloud computing, due to the heterogeneity of fog infrastructures in terms of performance of both the computing nodes and inter-node connectivity. In this paper, we tackle such problem proposing a mathematical model for the performance of complex applications deployed on a fog infrastructure. We adapt the proposed model to be used in a genetic algorithm to achieve optimized deployment decisions about the placement of micro-services chains. Our experiments prove the viability of our proposal with respect to meeting the SLA requirements in a wide set of operating conditions.
Asbestos Cement Pipe Condition Assessment and Remaining Service Life Prediction

This research details a condition assessment of asbestos cement (AC) pipe within in the Bella Vista Village Property Owners Association water distribution system. Bella Vista Village contains appropriately 300 miles of AC pipe with nominal diameters from 6-14 inch installed in the 70s and early 80s. The goal of the research is to determine the current condition of AC pipe within the system and predict future performance of the pipes. AC pipe samples were obtained from 23 locations spread out across the distribution system with nominal pipe diameters of 6-8 inch. Estimates of current level of degradation were obtained using phenolphthalein staining and hardness measurements of each sample. The residual strength of the pipes was measured using ASTM standard tests for pipe strength, such as crushing and hydrostatic tests. Remaining life of the AC pipe was estimated based on a linear degradation rate determined appropriate from a number of accelerated acid degradation experiments. These accelerated acid experiments conclusively determined the shape of the degradation vs time plot was linear. The AC pipe samples tested in the research were determined to be minimally to moderately degraded, with degradation levels measured between 1-31%. However, over 60% of the samples were determined to be below 10% degradation. All samples tested above AWWA strength requirements for crushing and hydrostatic capacity for AC pressure pipes. The estimated remaining service life for the AC pipe samples varied from 1 to >100 years with the majority (54%) of the samples having an estimated remaining service life greater than 100 years.
Y@C82 metallofullerene: calculated isomeric composition

Abstract Relative populations of the four energy-lowest isolated-pentagon-rule (IPR) isomers of Y@C82 under the high-temperature synthetic conditions are computed using the Gibbs energy based on characteristics from the density functional theory calculations (B3LYP/6–31G SDD entropy, B3LYP/6–31G SDD and B2PLYPD/6–31 + G SDD energetics). Two major species are predicted, Y@ -C82 and Y@ -C82, their calculated equilibrium relative populations agreeing with observations.
Enabling Trust and Privacy-Preserving e-KYC System Using Blockchain

The electronic know your customer (e-KYC) is a system for the banking or identity provider to establish a customer identity data verification process between relying parties. Due to the efficient resource consumption and the high degree of accessibility and availability of cloud computing, most banks implement their e-KYC system on the cloud. Essentially, the security and privacy of e-KYC related documents stored in the cloud becomes the crucial issue. Existing e-KYC platforms generally rely on strong authentication and apply traditional encryption to support their security and privacy requirement. In this model, the KYC system owner encrypts the file with their host’s key and uploads it to the cloud. This method induces encryption dependency and communication and key management overheads. In this paper, we introduce a novel blockchain-based e-KYC scheme called e-KYC TrustBlock based on the public key encryption method binding with the client consent enforcement to deliver trust, security and privacy compliance. In addition, we introduce attribute-based encryption to enable the privacy preserving and fine-grained access of sensitive transactions stored in the blockchain. Finally, we conduct experiments to show that our system is efficient and scalable in practice.
Detecting Sinkhole Attacks in IoT-Based Wireless Sensor Networks Using Distance From Base Station

Wireless sensor networks (WSNs) are infrastructure-less in nature, which contains numbers of autonomous sensor nodes. These sensor nodes are dedicated to monitoring the physical conditions of the environment and are organizing the collected data at a central location. Application area of WSN, like - heath care, military surveillance, are sensitive with respective to information sensed, that’s why security of WSN needs to be very effective. Providing security to WSN plays a major role as it consists of limited resources. The security system should lie within the boundary of the resource potential as well as should be competent enough to handle attacks. Intrusion detection system (IDS) is one such type of defense system, which can fulfill the measure of limitation of resources. In this paper, a detection technique is proposed against sinkhole attack using the Euclidean distance of each node from base station. The main advantage of the proposed technique is that it doesn't require any extra hardware setup as well as doesn't require extra communication cost.
Users' Privacy Concerns and Attitudes towards Usage-based Insurance: An Empirical Approach

: Usage-based Insurance (UBI) is a car insurance model in which the insurance payment calculations are based on driving data such as speed, acceleration, braking, location, etc. Driving data are collected and analysed by the insurer to provide feedback on driving performance, help drivers improve their skills, and possibly apply a discount on their next renewal. So far, UBI research has been focused more on its architecture, beneﬁts, or acceptance, while the users’ perception of such forms of insurance and their privacy concerns received less attention. To ﬁll this gap, we conducted an online survey with 281 participants and analysed their responses using qualitative and quantitative methods. We found that data collection and sharing are the main privacy concerns. Furthermore, we identiﬁed potential discounts as the most important feature in favor of adopting UBI, while data collection and unfair ratings are the main reasons to avoid or quit UBI.