Miracle-dz/UniFAR-FTUs huggingface dataset:
Integrating UAV-SfM and Airborne Lidar Point Cloud Data to Plantation Forest Feature Extraction

A low-cost but accurate remote-sensing-based forest-monitoring tool is necessary for regularly inventorying tree-level parameters and stand-level attributes to achieve sustainable management of timber production forests. Lidar technology is precise for multi-temporal data collection but expensive. A low-cost UAV-based optical sensing method is an economical and flexible alternative for collecting high-resolution images for generating point cloud data and orthophotos for mapping but lacks height accuracy. This study proposes a protocol of integrating a UAV equipped without an RTK instrument and airborne lidar sensors (ALS) for characterizing tree parameters and stand attributes for use in plantation forest management. The proposed method primarily relies on the ALS-based digital elevation model data (ALS-DEM), UAV-based structure-from-motion technique generated digital surface model data (UAV-SfM-DSM), and their derivative canopy height model data (UAV-SfM-CHM). Following traditional forest inventory approaches, a few middle-aged and mature stands of Hinoki cypress (Chamaecyparis obtusa) plantation forests were used to investigate the performance of characterizing forest parameters via the canopy height model. Results show that the proposed method can improve UAV-SfM point cloud referencing transformation accuracy. With the derived CHM data, this method can estimate tree height with an RMSE ranging from 0.43 m to 1.65 m, equivalent to a PRMSE of 2.40–7.84%. The tree height estimates between UAV-based and ALS-based approaches are highly correlated (R2 = 0.98, p < 0.0001), similarly, the height annual growth rate (HAGR) is also significantly correlated (R2 = 0.78, p < 0.0001). The percentage HAGR of Hinoki trees behaves as an exponential decay function of the tree height over an 8-year management period. The stand-level parameters stand density, stand volume stocks, stand basal area, and relative spacing are with an error rate of less than 20% for both UAV-based and ALS-based approaches. Intensive management with regular thinning helps the plantation forests retain a clear crown shape feature, therefore, benefitting tree segmentation for deriving tree parameters and stand attributes.
A Investigation and Evaluation of Data Center Network architectures

Large-scale data centers shape the center infrastructure support for the ever expanding cloud based totally services. Thus the overall performance and dependability characteristics of data facilities will have great impact at the scalability of those offerings. In specific, the data center networks wishes to be agile and reconfigurable that allows you to reply fast to ever changing software needs and service necessities. Significant studies portraits has been achieved on designing the data center network topologies that allows us to enhance the performance of data centers. In this paper we provide an explanation for a regular data center network architecture within the industry, the challenges current data center networks stumble upon today and introduce proposed solutions by means of a recent research.
Second MAFA Variant Causing a Phosphorylation Defect in the Transactivation Domain and Familial Insulinomatosis

Simple Summary Adult-onset familial insulinomatosis is a rare disorder with recurrent, severe hypoglycemia caused by multiple insulin-secreting pancreatic tumors. The etiology was unclear until the genetic variant p.Ser64Phe in the MAFA protein, a key coordinator of insulin secretion in pancreatic cells, was defined as the cause in two families. Based on the cases of two sisters with insulinomatosis, we aimed to identify further disease causes. The sequencing of the complete coding regions of the patients’ genomes revealed a second genetic MAFA variant, p.Thr57Arg, as the cause of familial insulinomatosis, linking genetic, clinical, and biochemical analyses from the patients’ family to the pre-described cell culture data. Thus, we confirm a defect in a crucial regulatory region of the MAFA protein as an important cause of a specific hereditary syndrome, which is characterized by insulinomatosis and/or mild hyperglycemia. This study extends the pathophysiological and diagnostic disease concept and verifies the inheritance pattern of familial insulinomatosis. Abstract Adult-onset familial insulinomatosis is a rare disorder with recurrent, severe hypoglycemia caused by multiple insulin-secreting pancreatic tumors. The etiology was unclear until the variant p.Ser64Phe in the transcription factor MAFA, a key coordinator of β-cell insulin secretion, was defined as the cause in two families. We here describe detailed genetic, clinical, and family analyses of two sisters with insulinomatosis, aiming to identify further disease causes. Using exome sequencing, we detected a novel, heterozygous missense variant, p.Thr57Arg, in MAFA’s highly conserved transactivation domain. The impact of the affected region is so crucial that in vitro expression studies replacing Thr57 have already been performed, demonstrating a phosphorylation defect with the impairment of transactivation activity and degradation. However, prior to our study, the link to human disease was missing. Furthermore, mild hyperglycemia was observed in six additional, heterozygote family members, indicating that not only insulinomatosis but also MODY-like symptoms co-segregate with p.Thr57Arg. The pre-described MAFA variant, p.Ser64Phe, is located in the same domain, impairs the same phosphorylation cascade, and results in the same symptoms. We confirm MAFA phosphorylation defects are important causes of a characteristic syndrome, thus complementing the pathophysiological and diagnostic disease concept. Additionally, we verify the high penetrance and autosomal dominant inheritance pattern.
Developing Students’ Computational Thinking and Subject Knowledge through Computational Modeling in Secondary Education

Computational thinking has become a core element in many countries' attempts to integrate computing into curricula at various levels of education. Computational modeling should be conceived of as part of computational thinking in K-12 education. Computational modeling has been shown to improve students’ content knowledge in existing subjects in K-12 education. However, there is a research knowledge gap in terms of how computational modeling and coding is improving students’ content knowledge of existing subjects in high school. the findings from an open-ended questionnaire with all participating students (n=210) and semi-structured interviews with all teachers (n=15). Thematic analysis was applied to categorize the qualitative data. The framework was successfully tested in high schools in Denmark and the paper concludes that students gained knowledge in CT and content knowledge in the existing subject through working with computer models of phenomena in different subjects. The study also showed that by letting students tinker with computer models they were able to integrate both coding, modeling and content knowledge.
Fixed-Text vs. Free-Text Keystroke Dynamics for User Authentication

: There are many physical biometrics such as iris patterns and fingerprints. There are also interactive gestures like how a person types on a keyboard, moves a mouse, holds a phone, or even taps a touch screen. Keystroke dynamics or typing dynamics is an automatic method that confirms the identity of an individual based on the manner and the way of the user typing on a keyboard. There are two types of keystroke systems, Fixed-text system, and free-text system and each of them has it is own importance. In this research paper, we are investigating the possibility of classifying individuals using features extracted from their keystroke dynamics with two different datasets: (1) fixed-text dataset with different difficulty levels and (2) free-text dataset with no restrictions what a user types on the keyboard. Investigation was done using several classification techniques: RandomForest (RF), Support Vector Machines (SVM), BayesNet (BN), and K-Nearest Neighbors (KNN). The highest accuracy achieved with the fixed-text dataset was 98.8% using RF for classification while the highest achieved accuracy with the free-text dataset was 87.58 % using RF classifier .
"Let’s Meet and Work it Out": Understanding and Mitigating Encountered-Type of Haptic Devices Failure Modes in VR

Encountered-type of Haptic devices (ETHD) are robotic interfaces physically overlaying virtual counterparts prior to a user interaction in Virtual Reality. They theoretically reliably provide haptics in Virtual environments, yet they raise several intrinsic design challenges to properly display rich haptic feedback and interactions in VR applications. In this paper, we use a Failure Mode and Effects Analysis (FMEA) approach to identify, organise and analyse the failure modes and their causes in the different stages of an ETHD scenario and highlight appropriate solutions from the literature to mitigate them. We help justify these interfaces’ lack of deployment, to ultimately identify guidelines for future ETHD designers.
Dynamic User Activity Prediction using Contextual Service Matching Mechanism

The significance of context-based services is significantly increasing with the advancement of integrated technologies of sensors and ubiquitous technologies. The existing approaches are reviewed to find out that identification of user's activity has more scope of improvement. After reviewing the current literature towards context-based methodologies, it is found that existing methods are devoid of considering dynamic context; while the modelling perspective is mainly towards considering predefined and static contextual information. Further, existing models doesn't have any inclusion of potential belief system nor any incorporation of service matching. Further, practical world case-studies is characterized by complex activity of user while it is quite challenging to extract the accurate contextual information associated with complex user activity. From the practical deployment scenario, the existing system offers less supportability toward collaborative network, which is highly essential to be considered for constraint modelling for user activity detection. Therefore, the proposed manuscript contributes a solution towards existing research problems by introducing a Dynamic User Activity Prediction using Contextual Service Matching Mechanism. A mixed research methodology is used to prove how service matching mechanism is important in contextual service discovery using multimodal activity data. The first contributory solution towards addressing the research problem is by introducing a novel and simplified belief system that considers both static contextual parameters as well as dynamic activity-based contextual parameter. The second contributory solution towards existing problem is to develop a novel service matching module that takes the input from service reposit, user calendar events, and collaborative units for assisting in similarity-based recommendation system. The model considers Hidden Markov Model for activity determination considering states of activity. With a combined usage of user activity context, feature management, and collaborative model, the proposed system offers better granularity in investigating user activity. The experimental and simulation analysis of the proposed outcome shows the enhanced accuracy performance of proposed system under different test environment. The study also investigates the impact of the service matching mechanism as well as relevance feedback on the accuracy to find that the proposed system excels better accuracy. Keywords—Contextual information; service discovery; prediction; ubiquitous computing; user activity
Implementation of Runway Detection Systems using Machine Learning

This internship proposal lies within the currently very active research field aiming at implementing Machine Learning techniques to increase autonomy level and reliability of autonomous aerial vehicles, in accordance with the EASA roadmaps1 [1]. Recent advances in vision sensors and hardware computing performances, together with improved learning algorithms, make vision-based solutions a real asset to improve guidance, navigation and control architectures for autonomous flights. In particular, during landing, considered by far the most critical phase, vision-based solutions are proven to be extremely useful for enriching data fusion algorithms and consequently drone absolute/relative positioning calculations [2]. However, image processing techniques employed until now for runway detection are mostly based on low level feature extraction and/or pre-knowledge of the landing site geographic
Feasibility of nominations in stationary gas networks with random load

The paper considers the computation of the probability of feasible load constellations in a stationary gas network with uncertain demand. More precisely, a network with a single entry and several exits with uncertain loads is studied. Feasibility of a load constellation is understood in the sense of an existing flow meeting these loads along with given pressure bounds in the pipes. In a first step, feasibility of deterministic exit loads is characterized algebraically and these general conditions are specified to networks involving at most one cycle. This prerequisite is essential for determining probabilities in a stochastic setting when exit loads are assumed to follow some (joint) Gaussian distribution when modeling uncertain customer demand. The key of our approach is the application of the spheric-radial decomposition of Gaussian random vectors coupled with Quasi Monte-Carlo sampling. This approach requires an efficient algorithmic treatment of the mentioned algebraic relations moreover depending on a scalar parameter. Numerical results are illustrated for different network examples and demonstrate a clear superiority in terms of precision over simple generic Monte-Carlo sampling. They lead to fairly accurate probability values even for moderate sample size.
Beware of the Interactions of Variability Layers When Reasoning about Evolution of MongoDB

With commits and releases, hundreds of tests are run on varying conditions (e.g., over different hardware and workload) that can help to understand evolution and ensure non-regression of software performance. We hypothesize that performance is not only sensitive to evolution of software, but also to different variability layers of its execution environment, spanning the hardware, the operating system, the build, or the workload processed by the software. Leveraging the MongoDB dataset, our results show that changes in hardware and workload can drastically impact performance evolution and thus should be taken into account when reasoning about performance. An open problem resulting from this study is how to manage the variability layers in order to efficiently test the performance evolution of a software.
Calibration and sensitivity analysis of rut prediction model for semi-rigid pavement using AASHTOWare ME design

Rutting is one of the most serious distresses of semi-rigid asphalt pavements in Jiangsu. In this research, AASHTOWare pavement ME Design software was used for rutting prediction of semi-rigid pavements. A semi-rigid pavement is composed of a flexible layer and a rigid layer. Local calibration was done first by taking into account local materials, traffic information, and environmental conditions. A total of eight sites representing typical mixture types of Jiangsu province were selected to complete the calibration and another four highways were chosen to make the validation. Then the calibrated software was used to identify sensitive factors for rutting prediction. Critical inputs needed for the calibration and sensitive analysis were tested or collected from local project-level Pavement Management System (PMS) database. The modulus of semi-rigid base was back-calculated from deflection basin data. Calibration coefficients for each typical structure and the optimal coefficients for this region were identified, respectively. Results also demonstrate that traffic inputs and thickness of Hot Mixed Asphalt (HMA) layers are more sensitive to the predicted rutting depth than void contents of HMA mixtures.
D ETECTION OF WESTERN BLOT PROTEIN BANDS USING TRANSFER LEARNING IN THE ASSESSMENT OF RISK OF GASTRIC AND OESOPHAGEAL CANCER

We present a transfer learning technique to encode visual features from an immunoblot assay for the detection of immuno-reactive protein bands to understand the association between H. pylori infection and gastric and oesophageal cancer. A CE-marked immunoblot method (HelicoBlot 2.0) was used to analyse 1500 human serum samples, test strips were scanned and the images segmented to enable a machine-learning algorithm to be developed which could identify protein bands on the strips. A model has been developed to detect protein bands with a performance of 95% AUROC. This novel approach to protein band detection reduces time spend by laboratory experts to interpret results, reduces ambiguity in band classiﬁcation, and can be applied to large-scale epidemiological studies investigating the relationship between infectious disease and risk of cancer.
Behaviors of bearing capacity of ultra-long bored piles

Based on the comparison and analysis of the static load tests on the experimental ultra-long bored piles of CCTV Tower and World Trade Center Tower(third stage) in Beijing,the behaviors of the pile tip resistance and the shaft resistance are studied.The results show that the deformation of pile top is mainly from its compression under loads.The tip resistance makes minor contribution to the bearing capacity relative to the shaft resistance.The pile shaft resistance exhibits a softening phenomenon in clay layer and a hardening phenomenon in sandy soil layer,and its play exhibits depth effect.The bearing capacity distribution of piles estimated according to the specifications is greatly different from the test results.Both the effective cleaning residue process and the post grouting means are proposed to be necessary measures for the ultra-long bored piles.
KloakDB: Distributed, Scalable, Private Data Analytics with 𝐾 -anonymous Query Processing

Private data federations enable multiple data owners to query the union of their secret data such that no party has access to the private inputs of another and the only information revealed is that which can be gleaned from the query output. Here, the data owners compute the query result amongst themselves using cryptographic techniques. Oblivious computation guarantees that a program’s observable transcript – its instruction traces, I/Os, and network utilization – does not change as a function of a query’s private inputs. Although this protects against side channel leakage, it has query runtimes that are multiple orders of magnitude higher than running the same query with no privacy guarantees. This performance overhead makes it impractical for large workloads. We propose a framework that offers a private data federation users a decision space between these two extremes of full oblivi-ousness and no privacy during query evaluation. KloakDB has a novel semi-oblivious query processing model, and it provides users with a fine-grained privacy-performance trade off. It generalizes 𝑘 -anonymity to model the information revealed by a federation during query evaluation. 𝑘 -anonymous query processing, is based on the widely deployed privacy model 𝑘 -anonymity. It is the de-facto standard for data releases in many domains including healthcare and education research. 𝐾 -anonymous query processing ensures that side channel information revealed during query execution for each record is indistinguishable from those of 𝑘 or more records. With modest values for 𝑘 , KloakDB demonstrates speedups of 15 × – 1060 × over oblivious query processing.
Estimating Egocentric 3D Human Pose in the Wild with Weak External Supervision

In Table 2 of our main paper, we show that our method outperforms the state-of-the-art methods: MoCap and xR-egopose. In order to further compare the performance on different types of motions, we show the quantitative comparisons on Wang et al. [7]’s test dataset in Table 1 and on MoCap dataset [9] in Table 2. We show that our method outperforms all of the baselines on most types of motion in these results. Note that our method is trained on the EgoPW dataset while the focal length and distortion of the fisheye camera in the EgoPW dataset is different from the fisheye camera used in MoCap, which affects the performance of our method on the MoCap test dataset.
Global Innovation Spillovers and Productivity: Evidence from 100 Years of World Patent Data

We use a panel of historical patent data covering a large range of countries over the past century to study the evolution of innovation across time and space and its eﬀect on productivity. We document a substantial rise of international knowledge spillovers as measured by patent citations since the 1990s. This rise is mostly accounted for by an increase in citations to US and Japanese patents in ﬁelds of knowledge related to computation, information processing, and medicine. We estimate the causal eﬀect of innovation induced by international spillovers on sectoral output per worker and total factor productivity (TFP) growth in a panel of country-sectors from 2000 to 2014, as well as on aggregate income per capita since 1960. To assess causality, we develop a shift-share instrument that leverages pre-existing citation linkages across countries and ﬁelds of knowledge, as well as heterogeneous countries’ exposure to technology waves. On average, an increase of one standard deviation in log-patenting activity increases sectoral output per worker growth by 1.1 percentage points. We ﬁnd results of similar magnitude for sectoral TFP growth and long-run aggregate income per capita growth.
THE EFFECT OF ALTERNATIVE ASSESSMENT METHODS ON YOUNG LEARNERS’ AUTONOMY AND SELF-REGULATION: A QUASI-EXPERIMENTAL STUDY

The aim of the study is to examine the implementation of Alternative Assessment in English as a Foreign Language (EFL) in a state primary school as proposed by the Institute of Educational Policy (IEP). Mixed research methods are employed. The quantitative data were analyzed, compared and contrasted along with qualitative data to prove that different types of Descriptive Assessment (DA) and learner portfolios have a favorable impact on Young Learners' (YL) autonomy and selfregulation. After implementing a two-month period intervention, it was found that both experimental group pupils’ Relative Autonomy Index (RAI) for self-regulation and performance in the language test significantly improved in comparison to their peers in the control group. The results are discussed in relation to the pedagogical implications they have for EFL in YL.
The role of imaging for identifying unstable carotid plaque

Stroke is one of the major causes of morbidity and mortality worldwide and approximately 15 million strokes occur every year. The risk of stroke depends not only on the grade of carotid artery stenosis but also on plaque characteristics that make them unstable. The features of unstable plaques include thin fibrous cap, large lipid core, inflammation, neovascularization and intraplaque hemorrhage. The present review summarizes existing data on the role of imaging in the identification of vulnerable carotid plaque.