Miracle-dz/UniFAR-FTUs huggingface dataset:
Visualising and quantifying the variability of hydrological state in intermittent rivers

: The hydrology of intermittent rivers has been characterised using either flow regimes, with limited spatial resolution, or network contraction, with limited temporal resolution. Exploration of the dynamic behaviour of these rivers, on which highly diverse biological communities depend, requires longitudinal, year-round observations with a more detailed classification of hydrological state than can be provided by gauging stations or wet/dry mapping alone. Observations of dry, ponded, moderate flow and high flow hydrological states spanning 20 years with approximately monthly frequency along ten chalk rivers in the south-east of England were visualised. There was slower transitioning between hydrological states and less spatial fragmentation on rivers with groundwater-dominated regimes than on those more influenced by superficial deposits. Seasonal patterns in both the composition and configuration of states were demonstrated using adapted landscape metrics. Responses to hydrological extremes and anthropogenic influences included drying downstream of the source and an artificially near-perennial reach. A framework is proposed for the categorisation of metrics of hydrological state and demonstrates that the classification and dimensional limitations of traditional approaches cannot fully characterise the hydrological behaviour of intermittent rivers. Such characterisation is an important step towards the tailored assessments required for effective management of these dynamic systems.
MENINGKATKAN KETERAMPILAN FOOTWORK ATLIT BULUTANGKIS USIA 6-12 TAHUN DENGAN PENDEKATAN BERMAIN

Abstrak Penelitian ini bertujuan untuk mengetahui peningkatan keterampilan footwork atlit bulutangkis usia 6-12 tahun melalui pendekatan bermain. Penelitian ini menggunakan metode penelitian tindakan sebanyak dua siklus dengan rancangan kegiatan perencanaan, observasi, tindakan, dan refleksi. Sasaran dalam penelitian ini adalah klub bulutangkis Putra Lebak. Dengan melalui pendekatan bermain dalam latihan footworkdengan pendekatan bermain pada atlit usia 6-12 klub bulutangkis Putra Lebak mempunyai peningkatan keterampilan footwork yang signifikan yaitu persentase pre test 11,0 dan post test 12,64 dengan 1 siklus melalui pendekatan bermain dapat meningkatkan keterampilan footwork maka tindakan yang diberikan memberikan peningkatan hasil tes dengan tingkatan ‘’sedang’’. Simpulan dari penelitian ini adalah metode pembelajaran dengan pendekatan bermain berpengaruh positif dalam meningkatkan keterampilan footwork atlit usia 6-12, yang ditandai dengan peningkatan etrampilan hasil pre test dan post test.Kata Kunci: Pendekatan bermain, Keterampilan, Footwork, Bulutangkis.
Les modalités de mise en œuvre de la cartographie de flux de valeur et la santé des travailleurs : une étude de cas multiples

Le Lean management est un mode d’organisation du travail qu’adoptent de plus en plus d’entreprises. Il rencontre, cependant, de nombreuses critiques quant a ses effets sur la sante des travailleurs. Au cœur de l’implantation de cette organisation du travail, la cartographie de flux de valeur est un outil central. Dans le cadre de cette recherche exploratoire et qualitative, prenant la forme d’une etude de cas multiples, nous avons recueilli des donnees concernant dix projets d’implantation lean en France et au Quebec. Pour ces projets, nous nous sommes demande quelles modalites d’utilisation de la cartographie des flux de valeurs pouvaient favoriser ou bloquer l’emergence des proprietes d’une organisation capacitante. Nous avons pu relever que la maniere dont sont definis les gaspillages, la facon dont sont construits les objectifs, et les modes de recueil des informations sur le travail pour realiser la cartographie de flux de valeur, sont des elements pouvant favoriser ou bloquer l’emergence d’une organisation capacitante.
Education:=Coding+Aesthetics;Aesthetic Understanding, Computer Science Education,and Computational Thinking

The popular press is rife with examples of how students in the United States and around the globe are learning to pro-gram, make, and tinker. The Hour of Code, maker-education, and similar efforts are advocating that more students be ex-posed to principles found within computer science. We propose an expansion beyond simply teaching computational thinking skills, by including an aesthetic framework that highlights the beauty and elegance inherent within the craft of coding. This approach not only introduces students to authentic experiences of computational work, but can result in higher levels of retention and achievement. Delivering science content through an aesthetic lens has been successful in other areas of science education. Such an approach in programming extends the possibility of reaching students that previously may not have been interested in the ﬁeld.
An Improved MFP-Miner Mining Algorithm and Its Application in Book Mining

With purchase of new books every year, collection of books in a library will continuously increase. As circulation data is updated every day, database of a library will also increase while minimal support degree will change due to different required correlation degree. To satisfy requirements of library readers as soon as possible with book allocation, mining result needs to be updated continually. Therefore, this thesis proposes an IM-Miner algorithm to realize synthesized update and mining of maximal frequent item sets with database and minimal support degree varying at the same time. IM-Miner algorithm makes the most of FP-Tree features and doesn’t need to generate maximal frequent candidate item sets during mining. As generation of maximal frequent item sets only occurs in FP-Tree, no scanning of transactional database is required. Experimental results showed that IM-Miner algorithm is more efficient than other algorithms.
Research On Novel Model of Data Mining Based on Improved Association Rules and Clustering Algorithm

Apriori algorithm is one of the most effective algorithms for mining frequent itemsets of Boolean Association rules. Decision tree is a method to analyze and summarize the attributes of a large number of samples. The frequent itemsets are used to generate the association rules, and the strong association rules are generated according to the minimum confidence set by the user. The paper presents research on novel model of data mining based on improved association rules and clustering algorithm. Finally, the effectiveness of the proposed algorithm is verified by experiments.
Fifteen-year results of black spruce uneven-aged silviculture in Ontario, Canada

An uneven-aged silviculture experiment was established in second-growth peatland black spruce (Picea mariana [Mill.] B.S.P.) stands in the boreal forest of northeastern Ontario, Canada in 1994. Three harvest treatments along with an unharvested control were applied in three replications. Fifteen-year results indicate that light- and medium-intensity harvest treatments maintain stand structure suitable for the continued application of uneven-aged silviculture treatments. Based on basal area growth trends and the development of stand structure, a cutting cycle of 20–25 years appears to be feasible for these treatments. Future harvests will likely yield a higher proportion of larger diameter trees with a greater value. The heavy harvest intensity treatment will result in a greater fluctuation of the growing stock and a longer cutting cycle. Without cutting treatments, the control treatment may eventually develop a stand structure that is unsuitable for the implementation of uneven-aged silviculture.
SEMANTIC BUILDING FAÇADE SEGMENTATION FROM AIRBORNE OBLIQUE IMAGES

Abstract. With the introduction of airborne oblique camera systems and the improvement of photogrammetric techniques, high-resolution 2D and 3D data can be acquired in urban areas. This high-resolution data allows us to perform detailed investigations on building roofs and façades which can contribute to LoD3 city modeling. Normally, façade segmentation is achieved from terrestrial views. In this paper, we address the problem from aerial views by using high resolution oblique aerial images as the data source in urban areas. In addition to traditional image features, such as RGB and SIFT, normal vector and planarity are also extracted from dense matching point clouds. Then, these 3D geometrical features are projected back to 2D space to assist façade interpretation. Random forest is trained and applied to label façade pixels. Fully connected conditional random field (CRF), capturing long-range spatial interactions, is used as a post-processing to refine our classification results. Its pairwise potential is defined by a linear combination of Gaussian kernels and the CRF model is efficiently solved by mean field approximation. Experiments show that 3D features can significantly improve classification results. Also, fully connected CRF performs well in correcting noisy pixels.
Development of an augmented haptic control for in-vehicle law enforcement applications

Typical police vehicles are filled with as much technology as the department can afford. Everything from the newest digital camera system to a simple entertainment radio is within the reach of an officer. Currently, most of these systems are installed without any consideration toward the other systems or the interaction required between the driver and the car. The lack of a systems approach in the installation has spawned a project to look at the safety and overall system installation in the New Jersey State Police vehicles. The research presented in this thesis suggests consolidation of the various controls into one intuitive controller that employs haptic technologies. A prototype controller was developed using an Immersion rotary haptic knob and a tactile feedback indicator. The parameters of this interaction design were explored through experimentation with a group of subjects using a testing procedure. From this information a prototype controller was developed and optimized to provide better user/vehicle interaction and a cleaner, safer cockpit.
Visual methods for social and cultural airport research.

This report examines the use of still, moving, and 3D images and visualisations for investigation into the social and cultural role of airports. The potential to use these methods to help people remember, connect to, and visualise these social and cultural spaces of the past, present, and future is explored. It follows on from work conducted as part of the SPARA 2020 project Work Package 7 activities which involved: qualitative research on the social and cultural importance of peripheral and remote airports; production of best practice templates for airport engagement with community stakeholders; and development of engagement strategies for case study airports. This report details the results of a literature search into visual methods and critically reflects upon the use of visual methods (specifically photo elicitation) in remote and rural airport contexts. This literature review: gives an overview of visual methods; details how they are used and applied in various fields; discusses research which uses different types of visuals (still, moving, and 3D); engages in a critical discussion of visual methods including advantages and ethical considerations; examines the potential for visual methods to engage with community stakeholders in social and cultural research; and examines the potential for visual methods to be used in airport and transport research more specifically. The report also reflects on the earlier SPARA 2020 research which employed the photo elicitation visual methodology and discusses the role this visual method played in the project. It also draws upon the findings of an earlier photo elicitation study which examined the social and cultural role of the main street in remote and peripheral areas. Although the report details examples from a wide variety of airports of differing sizes, the implications for socially and culturally significant remote and peripheral airports are explored in more detail, in line with the scope of the SPARA 2020 project. As such, this report will be of interest to: those working at remote and peripheral airports who wish to engage in community stakeholder engagement (especially where they are being asked to evidence non-economic impact to funding bodies); and visual researchers (especially those who study community spaces).
Contextual Autocomplete: A Novel User Interface Using Machine Learning to Improve Ontology Usage and Structured Data Capture for Presenting Problems in the Emergency Department

Objective To determine the effect of contextual autocomplete, a user interface that uses machine learning, on the efficiency and quality of documentation of presenting problems (chief complaints) in the emergency department (ED). Materials and Methods We used contextual autocomplete, a user interface that ranks concepts by their predicted probability, to help nurses enter data about a patient’s reason for visiting the ED. Predicted probabilities were calculated using a previously derived model based on triage vital signs and a brief free text note. We evaluated the percentage and quality of structured data captured using a prospective before-and-after study design. Results A total of 279,231 patient encounters were analyzed. Structured data capture improved from 26.2% to 97.2% (p<0.0001). During the post-implementation period, presenting problems were more complete (3.35 vs 3.66; p=0.0004), as precise (3.59 vs. 3.74; p=0.1), and higher in overall quality (3.38 vs. 3.72; p=0.0002). Our system reduced the mean number of keystrokes required to document a presenting problem from 11.6 to 0.6 (p<0.0001), a 95% improvement. Discussion We have demonstrated a technique that captures structured data on nearly all patients. We estimate that our system reduces the number of man-hours required annually to type presenting problems at our institution from 92.5 hours to 4.8 hours. Conclusion Implementation of a contextual autocomplete system resulted in improved structured data capture, ontology usage compliance, and data quality.
Concept drift adaptation for learning with streaming data

The term concept drift refers to the change of distribution underlying the data. It is an inherent property of evolving data streams. Concept drift detection and adaptation has been considered an important component of learning under evolving data streams and has attracted increasing attention in recent years. According to the existing literature, the most commonly used deﬁnition of concept drift is constrained to discrete feature space. The categorization of concept drift is complicated and has limited contribution to solving concept drift problems. As a result, there is a gap to uniformly describe concept drift for both discrete and continuous feature space, and to be a guideline to addressing the root causes of concept drift. The objective of existing concept drift handling methods mainly focuses on identifying when is the best time to intercept training samples from data streams to construct the cleanest concept. Most only consider concept drift as a time-related distribution change, and are disinterested in the spatial information related to the drift. As a result, if a drift detection or adaptation method does not have spatial information regarding the drift regions, it can only update learning models or their training dataset in terms of time-related information, which may result in an incomplete model update or unnecessary training data reduction. In particular, if a
Språkmedvetenhet i småbarnspedagogiskt arbete – Finländska daghemspedagogers reflektioner

Sammandrag: Under det senaste decenniet har Finland blivit alltmer mångspråkigt. Det syns också i styrdokumenten. I denna artikel beskrivs hur pedagoger vid ett tvåspråkigt så kallat samlokaliserat daghem resonerar kring begreppet språkmedvetenhet och hur deras diskurser förhåller sig till det som skrivs om språkmedvetenhet i Grunderna för planen för småbarnspedagogik 2016 (det nationella styrdokumentet för småbarnspedagogik). Enligt resultaten är pedagogerna medvetna om deras roll som språkmedvetna vuxna och hur de kan stödja barns språkliga medvetenhet i svenska/finska medan diskurser om annan flerspråkighet ej förekommer spontant. Studien visar att det är viktigt att diskutera språkmedvetenhet redan i lärarutbildningen och i annan fortbildning.Nyckelord: daghemspedagoger, diskursanalys, småbarnspedagogik, språkmedvetenhet
Outlook of Coordinated Transmission Control in 5G Networks for IoTs

The sum of wireless nodes are forecasted to ascent sharply from about ten billion in year 2018 to twenty-five billion by 2020. Consequently, the data packet capacity response is anticipated to increase in future. The projected 5th generation (5G) of cellular grids is anticipated to be a blend of grid mechanisms with diverse magnitudes, transfer energy, backhaul networks and wireless access equipment. Although there are several fascinating complications comprised by the 5G context, we highlighted the problems of communication mechanism with the potential of Internet of Things (IoT). We offer a charter to discover required method constraints strategy that deal with the preeminent advances in terms of possible output for device-todevice inspired mobile grids. We explore the working and breakdown of immediate data and energy handover in IoT and assess individually about communication outage likelihood and energy outage possibility.
Classification of Web Log Data to Identify Interested Users

With the increasing demand of internet more number of website are used for getting required information and thus more usage of web-based data. Whereas the data that is stored in different type of format in form of web log file. This log file should be maintained as these data are in unsorted manner and it is done through preprocessing. Web usage mining focuses on discovering useful knowledge or information. Web log file is automatically generated by web server whenever user accesses the resource like webpage of website. Web Usage Mining consists of three steps, Data Preprocessing, Pattern Discovery and Pattern Analysis. Data Preprocessing extracts text format data form log file and store clean data into database. Pattern Discovery finds pattern, Classify data by applying mining techniques. Pattern analysis finds knowledge from the discovered pattern.The main objective of this thesis is instead of spending high amount of time in tracking the behaviour of overall users to redesign the web site, spend less amount of time in focusing interested group of users only.The existing model used Naive Bayesian Classification to identify interested group of users from web log data. In this we propose Classification based on Predictive Association Rules Mining (CPAR) algorithm to identify interested group of users and also we present a comparative study of Naive Bayesian with CPAR.
The Survey of Vision-based 3D Modeling Techniques

This paper reviews the vision-based localization and map construction methods from the perspectives of VSLAM, SFM, 3DMax and Unity3D. It focuses on the key technologies and the latest research progress on each aspect, analyzes the advantages and disadvantages of each method, illustrates their implementation process and system framework, and further discusses the way to promote the combination for their complementary strength. Finally, the future opportunity of the combination of the four techniques is expected.
State machine of place-labelled petri net controlled grammars

A place-labelled Petri net controlled grammar is, in general, a context-free grammar equipped with a Petri net and a function which maps places of the net to productions of the grammar. The languages of place-labelled Petri net controlled grammar consist of all terminal strings that can be obtained by parallel application of the rules of multisets which are the images of the sets of input places in a successful occurrence sequence of the Petri net. In this paper, we investigate the structural subclass of place-labelled Petri net controlled grammar which focus on the state machine. We also establish the generative capacity of state machine of place-labelled Petri net controlled grammars.
Robust Recommendation Method Based on Shilling Attack Detection and Matrix Factorization Model

The existing robust collaborative recommendation algorithms have low robustness against PIA and AoP attacks. Aiming at the problem, we propose a robust recommendation method based on shilling attack detection and matrix factorization model. Firstly, the type of shilling attack is identified based on statistical characteristics of attack profiles. Secondly, we devise corresponding unsupervised detection algorithms for standard attack, AoP and PIA, and the suspicious users and items are flagged. Finally, we devise a robust recommendation algorithm by combining the proposed shilling attack detection algorithm with matrix factorization model, and conduct experiments on the MovieLens dataset to demonstrate its effectiveness. Experimental results show that the proposed method exhibits good recommendation precision and excellent robustness for shilling attacks of multiple types.
Application of deep learning algorithms for Lithographic mask characterization

The appearance of defects on the photomask is a key challenge in lithographic printing. Printable defects affect the lithographic process by causing errors in both the phase and magnitude of the light and of the sizes and location of the printed features. Presently 193 nm optical inspection tools are still the main ones for detecting pattern defects on EUV masks.1 However, pattern sizes on EUV masks could not be detected due to the resolution limit of 193 nm inspection tools. We propose and investigate the application of Convolutional Neural Networks (CNNs) to characterize and classify defects on lithographic masks. This paper details the training and evaluation of the CNNs to classify defects in simulated aerial images of an EUV setting. The simulation software Dr.LiTHO is used to simulate aerial images of defect-free masks and of masks with different types and locations of defects. Specifically we compute images of regular arrays of squares to be imaged with typical settings of EUV lithography (λ = 13.5 nm, NA= 0.33). We consider five types of absorber defects (extrusion, intrusion, oversize, undersize and center spot). The architecture of the CNN contains 4 convolutional layers (conv. layers) with a mixed size of filter,(3x3) and (5x5). The convolution stride and the spatial padding is 1 pixel for all conv. layers. Spatial pooling is carried out by 4 max-pooling layers. Two separate networks are trained for detection of the defect types and location, whereas a third algorithm combines the results. When an image is presented to the implemented algorithm and trained networks, it will return the defect type with its location. An accuracy of 99.9% on the training set and 99.3% on the test set is achieved for detection of the defect type. The network trained for location detection results in 98.7% training accuracy and 98.0% for the test set. Having a sufficient amount of training images the trained CNNs classify the types of defects and their location in the aerial image with high accuracy. The proposed method can be also applied to other defect types and simulation settings.