Reviewerly/exHarmony huggingface dataset:
Translation and Writing of Bilingual Language Landscape in Coastal Landscape Design of Coastal Cities

He, J., 2020. Translation and writing of bilingual language landscape in coastal landscape design of coastal cities. In: Bai, X. and Zhou, H. (eds.), Advances in Water Resources, Environmental Protection, and Sustainable Development. Journal of Coastal Research, Special Issue No. 115, pp. 154-156. Coconut Creek (Florida), ISSN 0749-0208.Under the contemporary education mode, bilingual teaching also needs continuous innovation and reform. Now the most important task is to improve students' vocabulary promotion stock. In most schools in our country, bilingual teaching is mainly carried out in a very traditional way, so it is necessary to change teaching strategies in order to better help students improve their vocabulary. In order to improve the students' bilingual achievement, we must improve the students' bilingual learning efficiency first. This paper analyzes the current situation of bilingual resource development teaching, then puts forward corresponding measures to bilingual resource development teaching from other angles. Hope to bring a little help to the landscape development of coastal cities.
Feature Detection and Tracking for Visual Effects: Augmented Reality and Video Stabilization

Halmaoui, Houssam Haqiq, AbdelkrimIn this article, we propose two visual effects applications of feature tracking: augmented reality and video stabilization. The originality of the proposed methods lies in the combination and the adaptation of computer vision algorithms to visual effects applications in order to improve the process of visual effects in filmmaking. The first step involves feature detection and tracking. Different multi-scale feature detection algorithms have been tested: Harris-Laplace, Hessian-Laplace and Laplacian of Gaussian. An experiment was conducted to evaluate the tracking quantitatively. Then, we evaluate tracking for visual effects applications. For augmented reality, the result of tracking combined with perspective transformation are used to add an image to a planar surface of a scene filmed by a moving camera. We also evaluate the tracking for 3D objects insertion or matchmoving. For video stabilization, the result of tracking is used to estimate the geometrical transformation between frames.
An Operational Framework for Evaluating the Performance of Learning Record Stores

Nowadays, Learning Record Stores (LRS) are increasingly used within digital learning systems to store learning experiences. Multiple LRS software have made their appearance in the market. These systems provide the same basic functional features including receiving, storing and retrieving learning records. Further, some of them may offer varying features like visualization functions and interfacing with various external systems. However, the non-functional requirements such as scalability, response time and throughput may differ from one LRS to another. Thus, for a specific organization, choosing the appropriate LRS is of high importance, since adopting a non-optimized one in terms of non-functional requirements may lead to a loss of money, time and effort. In this paper, we focus on the performance aspect and we introduce an operational framework for analyzing the performance behaviour of LRS under a set of test scenarios. Moreover, the use of our framework provides the user with the possibility to choose the suitable strategy for sending storing requests to optimize their processing while taking into account the underlying infrastructure. A set of metrics are used to provide performance measurements at the end of each test. To validate our framework, we studied and analyzed the performances of two open source LRS including Learning Locker and Trax.
Training Data Optimization for Pairwise Learning to Rank

This paper studies data optimization for Learning to Rank (LtR), by dropping training labels to increase ranking accuracy. Our work is inspired by data dropout, showing some training data do not positively influence learning and are better dropped out, despite a common belief that a larger training dataset is beneficial. Our main contribution is to extend this intuition for noisy- and semi- supervised LtR scenarios: some human annotations can be noisy or out-of-date, and so are machine-generated pseudo-labels in semi- supervised scenarios. Dropping out such unreliable labels would contribute to both scenarios. State-of-the-arts propose Influence Function (IF) for estimating how each training instance affects learn- ing, and we identify and overcome two challenges specific to LtR. 1) Non-convex ranking functions violate the assumptions required for the robustness of IF estimation. 2) The pairwise learning of LtR incurs quadratic estimation overhead. Our technical contributions are addressing these challenges: First, we revise estimation and data optimization to accommodate reduced reliability; Second, we devise a group-wise estimation, reducing cost yet keeping accuracy high. We validate the effectiveness of our approach in a wide range of ad-hoc information retrieval benchmarks and real-life search engine datasets in both noisy- and semi-supervised scenarios.
A Multi-phase Iterative Approach for Anomaly Detection and Its Agnostic Evaluation

Data generated by sets of sensors can be used to perform predictive maintenance on industrial systems. However, these sensors may suffer faults that corrupt the data. Because the knowledge of sensor faults is usually not available for training, it is necessary to develop an agnostic method to learn and detect these faults. According to these industrial requirements, the contribution of this paper is twofold: 1) an unsupervised method based on the successive application of specialized anomaly detection methods; 2) an agnostic evaluation method using a supervised model, where the data labels come from the unsupervised process. This approach is demonstrated on two public datasets and on a real industrial dataset.
Improved KCF Long-term Gesture Tracking Based On GMM And ELM

Considering the shape, color and motion characteristics of gesture in human-computer interaction, and aiming at the problems of KCF algorithm in the case of scale change, fast movement and non-rigid deformation of gesture target, a long-term gesture tracking method combining GMM skin color detection, ELM and KCF is proposed. Firstly, the GMM skin color model is obtained by cross-validation training method, and the GMM skin color filter result is refined by Bayesian estimation. According to the search strategy, ELM is used to further determine the gesture area and gesture category. Then, the KCF tracker is improved by combining the onedimensional scale filter, and is initialized by the detection result of the gesture detector. Finally, the tracking result is evaluated by the designed confidence model. If the confidence is less than the threshold, the gesture detector is used to reinitialize the improved KCF filter and restart tracking. Otherwise, continue to track. The experimental results show that the method can track the deformed gesture in human-computer interaction quickly and accurately in real time, and has good adaptability to scale changes.
The Role of Contextualization in Individuals’ Vulnerability to Phishing Attempts

Hackers who engage in phishing manipulate their victims into revealing confidential information by exploiting their motives, habits, and cognitive biases. Drawing on heuristic-systematic processing and the anchoring effect, this study examines how the contextualization of phishing messages, in the form of modifications to their framing and content, affects individuals’ susceptibility to phishing. This study also investigates if there is a discrepancy between the way individuals believe they will react to phishing attempts and their actual reactions. Using two fake phishing campaigns and an online survey, we find that individuals are more susceptible to phishing attempts when the phishing messages they receive are specific to their context, thereby appealing to their psychological vulnerabilities. There is also a significant gap between how individuals believe they will react and their actual reactions to phishing attempts.
Indexing Data on the Web: A Comparison of Schema-Level Indices for Data Search

Indexing the Web of Data offers many opportunities, in particular, to find and explore data sources. One major design decision when indexing the Web of Data is to find a suitable index model, i.e., how to index and summarize data. Various efforts have been conducted to develop specific index models for a given task. With each index model designed, implemented, and evaluated independently, it remains difficult to judge whether an approach generalizes well to another task, set of queries, or dataset. In this work, we empirically evaluate six representative index models with unique feature combinations. Among them is a new index model incorporating inferencing over RDFS and owl:sameAs. We implement all index models for the first time into a single, stream-based framework. We evaluate variations of the index models considering sub-graphs of size 0, 1, and 2 hops on two large, real-world datasets. We evaluate the quality of the indices regarding the compression ratio, summarization ratio, and F1-score denoting the approximation quality of the stream-based index computation. The experiments reveal huge variations in compression ratio, summarization ratio, and approximation quality for different index models, queries, and datasets. However, we observe meaningful correlations in the results that help to determine the right index model for a given task, type of query, and dataset.
Anomaly Detection for Industrial Control Networks Based on Improved One-Class Support Vector Machine

In traditional network anomaly detection algorithms, the anomaly threshold needs to be defined manually. Keeping this as background, this study proposes an anomaly detection algorithm (VAEOCSVM), which combines the variable auto-encoder (VAE) and one-class support vector machine (OCSVM) to realize anomaly detection in industrial control networks. First, the VAE model is used to obtain the distribution of the original normal sample data represented by the low-dimensional code; the reconstruction error of the VAE model is merged into the new input. Then, using OCSVM’s hinge-loss objective function and the random Fourier feature fitting radial basis function (RBF) kernel method, the OCSVM model is represented and solved using the deep neural network and gradient descent method. Finally, the decision function of the OCSVM model is constructed by using the solved parameter information to realize the detection of abnormal data. The proposed algorithm is compared with other machine-learning-based anomaly detection algorithms in terms of multiple indicators such as precision, recall, and [Formula: see text] score. The experimental results using various datasets show that the proposed algorithm has a better outlier recognition ability than the machine-learning-based anomaly detection algorithms.
Nurse care activity recognition challenge

Although activity recognition has been studied considerably for the last two decades, it is still not so easy to handle complicated activity classes in a specific domain. The 2nd Nurse Care Activity Recognition Challenge Using Lab and Field Data aims to explore a part of those complicated activities by focusing on the nurse caring. Our team, "UCLab", found that the main problem in the challenge is the imbalance and unevenness of the dataset, each of which often happens in real-field data. Considering the problem, we approached the challenge using a Random Forest-based method with multiple preprocessing to classify 12 activity modes. Our approach consists of the following steps: We first preprocessed the acceleration data to obtain uniformly sampled signals. Then we extracted acceleration data with respect to each row of the given label data and extracted feature values. We adopted Random Forest for classification and performed post-processing to the predicted data obtained from the classifier. As a result, we obtained 51.5% accuracy with the trial-based evaluation.
An unsupervised stereo matching cost based on sparse representation

Supervised stereo matching costs need to learn model parameters from public datasets with ground truth disparity maps. However, it is not so easy to obtain the ground truth disparity maps, thus making the supervised stereo matching costs difficult to apply in practice. This paper proposes an unsupervised stereo matching cost based on sparse representation (USMCSR). This method does not rely on the ground truth disparity maps, besides, it also can reduce the effects of the illumination and exposure changes, thus making it suitable for measuring similarity between pixels in stereo matching. In order to achieve higher computational efficiency, we further propose an efficient parallel method for solving sparse representation coefficients. The extended experimental results on three commonly used datasets demonstrate the effectiveness of the proposed method. Finally, the verification results on the monocular video clip show the USMCSR can also work well without ground truth disparity maps.
On Pruning for Score-Based Bayesian Network Structure Learning

Many algorithms for score-based Bayesian network structure learning (BNSL), in particular exact ones, take as input a collection of potentially optimal parent sets for each variable in the data. Constructing such collections naively is computationally intensive since the number of parent sets grows exponentially with the number of variables. Thus, pruning techniques are not only desirable but essential. While good pruning rules exist for the Bayesian Information Criterion (BIC), current results for the Bayesian Dirichlet equivalent uniform (BDeu) score reduce the search space very modestly, hampering the use of the (often preferred) BDeu. We derive new non-trivial theoretical upper bounds for the BDeu score that considerably improve on the state-of-the-art. Since the new bounds are mathematically proven to be tighter than previous ones and at little extra computational cost, they are a promising addition to BNSL methods.
A High-Accuracy, Cost-Effective People Counting Solution Based on Visual Depth Data

Real-time people counting has become a critical task due to its applications in a wide range of areas such as security, safety, statistics and commerce, implying that the demand for systems that offer such a capability has risen. Consequently, it is important to make it possible for the public to afford a precise, robust people counting system. Therefore, we aim to propose an efficient solution that requires low-cost hardware. Hopefully the people counting product derived from this solution will have a reasonable purchase price when put up for sale. Following the minimal hardware requirement, our system only relies on a depth camera plus a cheap embedded processor. A detection/tracking module forms the core of the underlying theoretical approach whose main functionality is to detect and count any entry/exit occurrences through a generic entrance. Our testing and validation experiments reveal that the proposed system yields a highly satisfactory accuracy rate and can compete closely with similar technologies currently available on the market.
Design and implementation of a Luganda text normalization module for a speech synthesis software program

This paper describes a Luganda text normalization module, a crucial component needed for a Luganda Text to Speech system. We describe the use of a rule-based approach for detection, classification and verbalization of Luganda text. At the core of this module are the Luganda grammar rules that were hand-built to normalize Non-Standard Words (NSWs) from different semiotic and noun classes. Input text is first analyzed, matched against handcrafted patterns developed using regular expressions to detect any NSWs. Upon detection, NSWs are tokenized and classified into one of the semiotic classes and then if necessary, into one of the Luganda noun classes. These are subsequently verbalized, each according to its semiotic as well as noun class, and a new text file is produced. We tested the module with 7 datasets and achieved average detection and normalization rates of 82% and 77.7% respectively.
Source Camera Identification Using WLBP Descriptor

In this paper we introduce a camera identification method using WLBP texture descriptor. This descriptor has previously been used for texture and face classifiers. In the proposed method, we proposed to use WLBP operator in camera classification application to identify the imaging camera. In our method, the two-dimensional histogram of Weber's features and LBP for camera identification are investigated. For this purpose, experiments were conducted on Dresden database. The proposed method has reached the accuracy of 99.52% on nine digital cameras of different models. In compressed JPEG images with the compression quality factor of 70% the method reached the accuracy of 89.04%. The results indicate that the proposed method has a high degree of accuracy in comparison to other proposed method and exhibits relatively good robustness to compression.
Winds from seattle

The database research community has been notably successful in impacting the industry and academia since the invention of the relational model. Examples of innovation in the last decade include columnar storage for data analytic platforms, cloud data services, HTAP systems, and a new generation of data wrangling systems. Despite this success, critical self-assessment by the community and identifying key opportunities for the future is essential if we are to continue the tradition of impactful research. In the Fall of 2018, following a long tradition that dates back to 1988 [4], and five years after the last such meeting [1], a group of approximately thirty database researchers gathered at the University of Washington, Seattle for two days to discuss the opportunities we have as a community for impactful research. A report from that meeting is now available [2]. The discussions in the Seattle meeting focused not just on technical challenges and opportunities but also on topics related to how we organize ourselves as a community. This VLDB panel follows on from a previous discussion at SIGMOD 2020 [3], to provide a forum for the broader database community to review and debate the findings from the Seattle Report on Database Research [2] as well as to identify other challenges, and opportunities that need to be taken into account.
Feasibility of using environmental covariates and machine learning to predict the spatial variability of selected heavy metals in soils

&amp;lt;p&amp;gt;Heavy metal contamination in soil is a major environmental issue intensified by rapid industrial and population growth. Understanding the spatial distribution of soil contamination by heavy metals in the ecosystem is a necessary precondition to monitor soil health and to assess the ecological risks. The main sources of heavy metals in soil are natural and anthropogenic sources. Natural sources are typically released of heavy metals from rock by weathering and atmospheric precipitation. Anthropogenic sources are related to industrialization, rapid urbanization, agricultural practices, and military activities. We analyzed a total of 358 topsoil samples (0&amp;amp;#8211;30 cm) collected in Golestan province in the northeast of Iran based on a regular square grid networks with 1,700 squares each sized 2.5 km&amp;amp;#178;(random sampling within the grid). From these samples, we determined the spatial distribution of Cd, Cu, Ni, Zn, and Pb using random forest (RF). A multi-spectral image (Landsat 8), and environmental derivatives calculated from terrain attributes, climatic parameters, parent material, land use maps, distances to mine sectors, main roads, industrial sites, and rivers were used as covariates to predict the spatial distribution of concentrations of heavy metals. The multi-collinearity of the predictors was examined by the variance inflation factor (VIF), and a feature selection process (genetic algorithm) was applied to avoid noise and optimize the selected input variables for the final model. The predictive accuracy of RF model was assessed by the mean prediction error (ME), root mean squared error (RMSE), and coefficient of determination (R&amp;lt;sup&amp;gt;2&amp;lt;/sup&amp;gt;) using 5-fold cross-validation technique. The results showed that the concentration levels (mg kg&amp;lt;sup&amp;gt;-1&amp;lt;/sup&amp;gt;) of Cd, Cu, Pb, Ni, and Zn varied from 0.02 to 2.75, 9.70 to 93.70, 6.80 to 114.20, 9.50 to 93.20, and 25.10 to 417.4, respectively. The best prediction performance was for Ni (RMSE=9.9 mg kg&amp;lt;sup&amp;gt;-1 &amp;lt;/sup&amp;gt;and R&amp;lt;sup&amp;gt;2&amp;lt;/sup&amp;gt;=56.6%), and the lowest prediction performance for Cd (RMSE=0.4 mg kg&amp;lt;sup&amp;gt;-1 &amp;lt;/sup&amp;gt;and R&amp;lt;sup&amp;gt;2&amp;lt;/sup&amp;gt;=28.0%). Environmental covariates that control soil moisture and water flow along with climatic factors were the most important variables to define the spatial distribution of soil heavy metals. We conclude that the RF model using easily accessible environmental covariates is a promising, cost-effective and fast approach to monitor the spatial distribution of heavy metal contamination in soils.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;&amp;lt;strong&amp;gt;Keywords:&amp;lt;/strong&amp;gt; Heavy metals; digital soil mapping; machine learning; random forest; spatial variation; soil pollution.&amp;lt;/p&amp;gt;
Fuzzy squareness: A new approach for measuring a shape

In this paper, we define a new fuzzy squareness measure to quantify how much a given fuzzy shape matches a fuzzy square. The new fuzzy shape-based measure is naturally defined and theoretically well-founded, resulted in that its behavior can be understood and predicted in advance. It runs through the interval (0,1] and takes the maximum value equals 1 if and only if the shape measured is a fuzzy square. The new fuzzy squareness measure is also invariant to similarity transformations. Several various experiments to illustrate the behavior of the new measure, and to verify all the theoretically proven results are also shown. Effectiveness and usefulness of the new fuzzy squareness measure are demonstrated in the tasks of object classification performed on three large well-known modern image datasets such as MPEG-7 CE-1, Swedish Leaf, and Portuguese Leaves datasets.
Prediction Method of Code Review Time Based on Hidden Markov Model

Pull-Request (PR) is the primary method for developers to contribute code in GitHub. Code review can effectively ensure the quality of the code to be merged. The time of code review will affect the development progress of the entire project. Some researchers predict the duration of the review based on the initial attributes of PR as input attributes for building their prediction model. However, these methods ignore the temporal nature of these activities. In this paper, we propose a new method that uses the hidden Markov model (HMM) and the time series of developer activities. By considering the chronological sequence of developer activities, critical activities in PR are extracted to form a key activity sequence, by which HMM is used. To classify sequences, we collected the historical data of 5 projects from GitHub and conducted experiments. The results show that this method can effectively identify and predict PR’s duration to be reviewed at an early stage.