Miracle-dz/UniFAR-FTUs huggingface dataset:
Exporting Transactional Interface to Applications in Log-Structured File Systems

The write-ahead-logging (WAL) used in SQLite adopts roll-forward mechanism to ensure system consistency, which induces double writes of the updates due to the checkpointing procedure. F2FS is a recent proposed file system that is specifically designed and optimized for flash memory based storage. By leveraging the log-structured update pattern and the atomicity of filesystem-level checkpoint in F2FS, this paper proposes an index remapping mechanism to redirect the DB access to WAL. In this way, the write amplification caused by WAL checkpointing can be mitigated, which further improves the overall performance of the system. The atomicity of the remap operation and the WAL alignment format are discussed in details. In addition, crash recovery under the proposed framework is validated. The experimental results show that the proposed framework can effectively reduce the write traffic and improve system performance.
Deep neural network analysis of go games: which stones motivate a move?

Recently, deep learning was used to construct deep convolution network models for move prediction in Go. Here, we develop methods to analyze the inner workings of the resulting deep architectures. Our example network is learned and tested using a database of over 83,000 expert games with over 17 million moves. We present ways of visualizing the learned features (“shapes”) and a method to derive aspects of the motivation behind the expert’s moves. The discussed methods are inspired by recent progress made in constructing saliency maps for image classiﬁcation.
Combine Reflectance with Shading Component for Hyperspectral Image Classification

Intrinsic image decomposition (IID) of hyperspectral images (HSIs) aims to separate the reflectance cube and shading component from the original image data. The reflectance cube contains the spectral information reflecting the intrinsic properties of the material, whereas the shading component contains the spatial information reflecting geometric structure of the object like the surface orientation changes. From the perspective of hyperspectral image classification, combining spectral information with spatial information can be useful for improving the classification performance. In this paper, a new optimization algorithm is proposed for intrinsic image decomposition of hyperspectral images, and composite kernel learning (CKL) method is further utilized to combine reflectance with shading component.
Soylent: a word processor with a crowd inside

This paper introduces architectural and interaction patterns for integrating crowdsourced human contributions directly into user interfaces. We focus on writing and editing, complex endeavors that span many levels of conceptual and pragmatic activity. Authoring tools offer help with pragmatics, but for higher-level help, writers commonly turn to other people. We thus present Soylent, a word processing interface that enables writers to call on Mechanical Turk workers to shorten, proofread, and otherwise edit parts of their documents on demand. To improve worker quality, we introduce the Find-Fix-Verify crowd programming pattern, which splits tasks into a series of generation and review stages. Evaluation studies demonstrate the feasibility of crowdsourced editing and investigate questions of reliability, cost, wait time, and work time for edits.
Modelling Leaf Chlorophyll Content in Coffee (Coffea Arabica) Plantations Using Sentinel 2 Msi Data

Coffee leaf chlorophyll (ChI) is an important proxy for coffee plant photosynthetic rates, nitrogen content, leaf health and yield potential. Whereas the recently launched Sentinel 2 multi -spectral instrument (MSI) data has great potential for plant condition assessment, the value of its spectral settings at variable spatial resolutions in relation to crop canopy cover on ChI content prediction remains largely unexplored. In this study, we apply an empirical model to estimate coffee leaf ChI with Sentinel 2 MSI data. Results showed that coffee biophysical parameters (height and canopy cover) are significantly influenced by stand age while plant water concentration and total ChI are age invariant. Results further showed that the best modelling results (R2=0.69, RMSE=64.4) were achieved when all the bands at 10m spatial resolution with all data were used. We concluded that Sentinel 2 MSI is a valuable dataset for predicting coffee leaf ChI, however, based on our findings, we suggest that finer spatial resolutions of 10m on mature coffee stands should be adopted for better prediction results.
An Empirical Analysis of the Commercial VPN Ecosystem

Global Internet users increasingly rely on virtual private network (VPN) services to preserve their privacy, circumvent censorship, and access geo-filtered content. Due to their own lack of technical sophistication and the opaque nature of VPN clients, however, the vast majority of users have limited means to verify a given VPN service's claims along any of these dimensions. We design an active measurement system to test various infrastructural and privacy aspects of VPN services and evaluate 62 commercial providers. Our results suggest that while commercial VPN services seem, on the whole, less likely to intercept or tamper with user traffic than other, previously studied forms of traffic proxying, many VPNs do leak user traffic---perhaps inadvertently---through a variety of means. We also find that a non-trivial fraction of VPN providers transparently proxy traffic, and many misrepresent the physical location of their vantage points: 5--30% of the vantage points, associated with 10% of the providers we study, appear to be hosted on servers located in countries other than those advertised to users.
Dynamic Bayesian Knowledge Transfer Between a Pair of Kalman Filters

Transfer learning is a framework that includes—among other topics—the design of knowledge transfer mechanisms between Bayesian filters. Transfer learning strategies in this context typically rely on a complete stochastic dependence structure being specified between the participating learning procedures (filters). This paper proposes a method that does not require such a restrictive assumption. The solution in this incomplete modelling case is based on the fully probabilistic design of an unknown probability distribution which conditions on knowledge in the form of an externally supplied distribution. We are specifically interested in the situation where the external distribution accumulates knowledge dynamically via Kalman filtering. Simulations illustrate that the proposed algorithm outperforms alternative methods for transferring this dynamic knowledge from the external Kalman filter.
Inter-Rater Reliability of Pain Provocation Tests for Painful Lumbar Facet Joints. A Pilot Study

Abstract Background With a life-time prevalence of 39%, low back pain (LBP) is one of the most common musculoskeletal disorders. Correct diagnosis of underlying causes is commonly seen as a prerequisite for successful therapy of LBP. Currently, there is no useful, non-invasive clinical test to diagnose painful lumbar zygapophyseal joints. Clinical tests with high diagnostic accuracy are therefore desirable. Inter-rater reliability is commonly seen as a prerequisite of test validity. The objective of this pilot study was thus to evaluate inter-rater reliability of new clinical pain provocation tests for diagnosing painful lumbar zygapophyseal joints. If a clinically significant level of inter-rater reliability were to be determined, this study could constitute a first step towards establishing the clinical utility of this new set of tests in the structural diagnosis of low back disorders. Material and Methods Patients participating in this study were recruited in a specialist hospital throughout June 2015 until September 2016. Both female and male patients with current LBP, with or without buttock or lower extremity symptoms were screened for inclusion. Patients had preferably to be 50 years or older in order to be included in this study. The upper age limit was set at 90 years. They also had to be fluent in German to be able to follow verbal instructions. Exclusion criteria comprised the presence of red flags, surgical spinal fusion at any level of the lumbar spine, discectomy within the past 12 months, radiculopathy and/or radicular pain, interventional treatments for the IVD, SIJs or ZAJs within the past three months, psychiatric disorders, current litigation issues concerning back pain, and central nervous system diseases. Three certified manual therapy instructors tested patients independently. Fleissʼ kappa values and percent agreements were calculated. Results Subjects included a sample of 48 patients (15 males and 33 females) with a mean age of 66.48 years (range 33 – 90) presenting at a specialist hospital with low back pain. The new pain provocation tests for lumbar zygapophyseal joints showed moderate reliability (Fleissʼ κ = 0.46) and an overall percent agreement of 68.8%. Separate tests for the zygapophyseal joints of L5–S1 showed fair reliability (Fleissʼ κ = 0.37) and an overall percent agreement of 64.6%. Unidimensional tests that were used in the study to exclude the intervertebral disc as a nociceptive generator showed fair reliability as well (average κ of 0.28 and 0.39, respectively). Neither drop outs nor adverse events occurred. Conclusion The new pain provocation tests for lumbar zygapophyseal joints showed clinically significant levels of inter-rater reliability. Validation of these tests against fluoroscopy-guided medial branch blocks is warranted.
Business Process Standardization of Multinational Enterprises: Two Distinctive Organizational Models

Business process standardization practice was studied in the context of integration and adaptation. 31 Japanese and non-Japanese MNE were interviewed to find a practice of standardizing business process globally. 4 distinctive types of standardization were identified. Two top down approaches were consistent with organizational characteristics such as clear job specification, in contrasting on those using Kaizen-like approaches, with general characteristic employment as membership, which can be called T-type and J-type, respectively. Both top-down and Kaizen-like approach uses replication strategy to have globally integration but remains flexible when executed at different geographic location to have adaptation needed.
IMS at the PolEval 2018: A Bulky Ensemble Depedency Parser meets 12 Simple Rules for Predicting Enhanced Dependencies in Polish

This paper presents the IMS contribution to the PolEval 2018 Shared Task. We submitted systems for both of the Subtasks of Task 1. In Subtask (A), which was about dependency parsing, we used our ensemble system from the CoNLL 2017 UD Shared Task. The system first preprocesses the sentences with a CRF POS/morphological tagger and predicts supertags with a neural tagger. Then, it employs multiple instances of three different parsers and merges their outputs by applying blending. The system achieved the second place out of four participating teams. In this paper we show which components of the system were the most responsible for its final performance. The goal of Subtask (B) was to predict enhanced graphs. Our approach consisted of two steps: parsing the sentences with our ensemble system from Subtask (A), and applying 12 simple rules to obtain the final dependency graphs. The rules introduce additional enhanced arcs only for tokens with "conj" heads (conjuncts). They do not predict semantic relations at all. The system ranked first out of three participating teams. In this paper we show examples of rules we designed and analyze the relation between the quality of automatically parsed trees and the accuracy of the enhanced graphs.
Data Analysis for Effective Monitoring of Partially Shaded Photovoltaic Systems

The purpose of this study is the development of an algorithmic tool to automate the process of analyzing monitoring data of partially shaded PV systems. The approach is to compare long-term and high-resolution yield data of a partially shaded Photovoltaic (PV) system (investigated PV) with the yield data of an unshaded PV system or the tilted irradiance (reference PV system) and automatically detect the energy loss due to the expected shadow, caused by any surrounding obstacles and distinguish it from any additional energy loss due to other malfunctions.
Deterministic Cramer-Rao Bound for Scanning Radar Sensing

In this paper, the Cramér-Rao Bound (CRB) for scanning radar sensing is investigated, providing an algorithm-independent bound on the angular estimation error. Based on the deterministic signal model, we first derive a numerical CRB for unknown real signal parameters. Then, the approximate closed-form expression of CRBs are further provided for the single target case. Meanwhile, the potential estimation error of various classical super-resolution sensing methods are quantitatively investigated in this paper, and compared with the presented CRB.
Evolutionary MCTS with Flexible Search Horizon

In turn-based multi-action adversarial games each player turn consists of several atomic actions, resulting in an extremely high branching factor. Many strategy board, card, and video games fall into this category, which is currently best played by Evolutionary MCTS (EMCTS) -- searching a tree with nodes representing action sequences as genomes, and edges representing mutations of those genomes. However, regular EMCTS is unable to search beyond the current player's turn, leading to strategic short-sightedness. In this paper, we extend EMCTS to search to any given search depth beyond the current turn, using simple models of its own and the opponent's behavior. Experiments on the game Hero Academy show that this Flexible-Horizon EMCTS (FH-EMCTS) convincingly outperforms several baselines including regular EMCTS, Online Evolutionary Planning (OEP), and vanilla MCTS, at all tested numbers of atomic actions per turn. Additionally, the separate contributions of the behavior models and the flexible search horizon are analyzed.
ros_control: A generic and simple control framework for ROS

In recent years the Robot Operating System (ROS) has become the 'de facto' standard framework for robotics software development. The `ros_control` framework provides the capability to implement and manage robot controllers with a focus on both _real-time performance_ and _sharing of controllers_ in a robot-agnostic way. The primary motivation for a sepate robot-control framework is the lack of realtime-safe communication layer in ROS. Furthermore, the framework implements solutions for controller-lifecycle and hardware resource management as well as abstractions on hardware interfaces with minimal assumptions on hardware or operating system. The clear, modular design of `ros_control` makes it ideal for both research and industrial use and has indeed seen many such applications to date. The idea of `ros_control` originates from the `pr2_controller_manager` framework specific to the PR2 robot but `ros_control` is fully robot-agnostic. Controllers expose standard ROS interfaces for out-of-the box 3rd party solutions to robotics problems like manipulation path planning (`MoveIt! `) and autonomous navigation (the `ROS navigation stack`). Hence, a robot made up of a mobile base and an arm that support `ros_control` doesn't need any additional code to be written, only a few controller configuration files and it is ready to navigate autonomously and do path planning for the arm. `ros_control` also provides several libraries to support writing custom controllers.
Data complexity measures for analyzing the effect of SMOTE over microarrays

Microarray classiﬁcation is a challenging issue for machine learning researchers mainly due to the fact that there is a mismatch between gene dimension and sample size. Besides, this type of data have other properties that can complicate the classiﬁcation task, such as class imbalance. A common approach to deal with the problem of imbalanced datasets is the use of a preprocessing step trying to cope with this imbalance. In this work we analyze the usefulness of the data complexity measures in order to evaluate the behavior of the SMOTE algorithm before and after applying feature gene selection.
Assessment of Simulated Compact Polarimetry of the RCM Medium Resolution sar Modes for Oil Spill Detection

Operational detection and discrimination of oil spills over oceans has received considerable attention due to its impact on marine ecosystem from environmental and political points of view. Synthetic Aperture Radar (SAR) is a valuable instrument for maritime pollution monitoring. The three main requirements for effective operational oil spill detection using SAR are: 1) low noise floor, 2) large area coverage, and 3) maximizing detection and discrimination of pollution and ‘lookalike’ features, by polarization diversity, multiple frequency, etc. In order to reconcile the advantages of fully polarimetric SAR with larger area coverage, compact polarimetry (CP) acquisitions offer a trade-off between the above mentioned requirements. The future Canadian RADARSAT Constellation Mission (RCM) will enable the acquisition of CP SAR data in wide swath imagery, including ScanSAR modes. In this study, we investigate the potential of CP from three RCM SAR modes for oil spill detection. Results indicate that the RCM MR30 SAR mode has promising oil spill detection performance.
Six-Port Technology for 5G Millimeter-Wave Localization Systems

This work deals with six-port technology applied to a localization system operating in the millimeter-wave frequency band. The proposed system is mainly based on a MMIC 60 GHz six-port circuit which is designed and manufactured with passive mixers, 90° phase-shifters, active and passive dividers to build a sensitive vector detector enabling to determine both Angle Of Arrival (AOA) and Time Difference of Arrival (TDOA). Based on this six-port technology, an original localization technique is implemented to minimize the degradation of the location error.
AIS Electronic

To prepare for changes in South Korea’s aging population and travel trends for senior citizens, this article analyzes constraints relate to travel. It also suggests a mobile application that removes analyzed constraints for active seniors’ free-independent travel and motivates active seniors to travel by themselves. For this, we researched the fundamental constraints of individuals’ senior trip that based on the leisure constraints model and applied the Design thinking methodology to propose the solution. Therefore, TRAVEL READER application prototype is developed. By using ICT which provides a tool for active seniors to improve accessing to travel information, this service could eliminate the digital divide, limitation on information, and psychological burdens like fear. Consequently, the application can remove barriers of free-independent travel for seniors and motivate them to enjoy their trip without psychological burdens. It would contribute to promote tourism industry targeting active seniors.
Spatial-Spectral Graph-Based Nonlinear Embedding Dimensionality Reduction for Hyperspectral Image Classificaiton

Dimensionality reduction (DR) is one of the most important tasks to improve the performance of hyperspectral images classification. Recently, a sparse and low-rank graph embedding based method (SLGE) has been proposed to describe the intrinsic structure of data combined with the local and global constraint simultaneously, which is effective to reduce the dimension of hyperspectral data and obtain a better classification accuracy. However, SLGE is based on an assumption that low-dimensional feature can be obtained utilizing a linear projection. Its performance may degrade under nonlinearly distributed data. Moreover, spatial prior of HSI is not considered in the framework. In this paper, we proposed a novel dimensionality reduction method named spatial-spectral graph-based non-linear embedding (SSGNE). To generate a new graph-trained data, the segmentation strategy based on superpixel is adopted. The spatial-spectral graph is constructed by constraining the sparsity and low-rankness simultaneously on graph-trained data set. Finally, the kernel trick is adopted to extend the general graph embedding framework to nonlinearly space, which fully considers the complexity of real data. Experimental results show that the proposed method outperforms the state-of-the-art methods in terms of the classification accuracy.
A STUDY OF BOUNDARY LAYER ROLLS UNDER VARIOUS STORM CONDITIONS

The marine atmospheric boundary layer (MABL) roll plays an important role in the turbulent exchange of momentum, sensible heat, and moisture throughout the boundary layer of tropical cyclones. Hence, rolls are believed to be closely related to storm development and intensification. In this study, based on the RADARSAT-2 dataset including various tropical cyclone (TC) intensities, the roll characteristics are retrieved from synthetic aperture radar (SAR) images via fast Fourier transform (FFT). We investigate the roll wavelengths at variance of TC intensities and found that the roll wavelengths are related to the distance with respect to TC center and TC intensities. This study is promising to bring roll-induced effects into hurricane forecasting model.
Source Model of the 12 November 2017 Mw 7.3 Kermanshah Earthquake (Iran-Iraq Border) Inferred from ALOS-2 Scansar and Sentinel-L Data

In this paper, ground deformation and source model of the 12 November 2017 Kermanshah Earthquake (border region between Iran and Iraq) are investigated by using ALOS-2 ScanSAR and Sentinel-lAiB TOPSAR co-seismic Differential Interferometric Synthetic Aperture Radar (DInSAR) measurements. Geodetic inversion has been performed to constrain source parameters and invert slip distribution on the fault plane. Stress changes on the source fault and neighboring active faults around this area are estimated based on co-seismic deformation. The best-fit source model shows a reverse fault with a relative large right-lateral component, striking 353.5° NNW-SSE and dipping 16.3° NE. The maximum slip is up to 3.8m at 12–14 km depth and the inferred seismic moment is 1.01×1020 Nm, corresponding to Mw 7.3. The positive stress changes on the neighboring active faults indicate that this event may trigger other earthquakes on the Zagros Mountain Front Fault (MFF).
A simplified mechanism for anisotropic constriction in Drosophila mesoderm

ABSTRACT Understanding how forces and material properties give rise to tissue shapes is a fundamental issue in developmental biology. Although Drosophila gastrulation is a well-used system for investigating tissue morphogenesis, a consensus mechanical model that explains all the key features of this process does not exist. One key feature of Drosophila gastrulation is its anisotropy: the mesoderm constricts much more along one axis than along the other. Previous explanations have involved graded stress, anisotropic stresses or material properties, or mechanosensitive feedback. Here, we show that these mechanisms are not required to explain the anisotropy of constriction. Instead, constriction can be anisotropic if only two conditions are met: the tissue is elastic, as was demonstrated in our recent study; and the contractile domain is asymmetric. This conclusion is general and does not depend on the values of model parameters. Our model can explain results from classical tissue-grafting experiments and from more-recent laser ablation studies. Furthermore, our model may provide alternative explanations for experiments in other developmental systems, including C. elegans and zebrafish. Summary: All experimental observation regarding anisotropy of mesoderm constriction in Drosophila may be explained by a simple model that assumes tissue is elastic and the contractile mesodermal domain is asymmetric.