Reviewerly/exHarmony huggingface dataset:
Mandarin Students' Perceptions of Smartphone Applications in Mandarin Learning

The brilliant advancement of mobile technologies provides a vivid instructional approach which suits the millennial students where learning happens ubiquitously.Therefore, awareness that mobile-assisted language learning (MALL) encourages personalized learning is the focus.Smartphone, the most excellent creation has impressed everyone, and it offers enormous potential for integration into language learning.Smartphone applications (SA) enable students to learn nowhere, in the class or out of the formal class.However, the way of students perceive SA as the Mandarin learning tools have not yet explored broadly.Thus, this preliminary study aims to examine Mandarin students' perceptions of SA along with its usability, effectiveness, and satisfaction and the correlations of gender, course, and activities via SA against the items mentioned.A quantitative method was used through questionnaires distribution for data collection.79 of undergraduates from an east coast university were chosen as the respondents to answer the survey.The survey was conducted in the 10th week of a 14 weeks Mandarin course.The collected data were then analysed using SPSS for the descriptive statistics.Two types of smartphone applications included dictionary based and teaching and learning based were identified.The statistics show that all the respondents consented that learning Mandarin via SA has influenced their language performance; task accomplishment and personal study positively.In addition, the students were engaging, satisfied and willing to continue using smartphones in their study.All in all, SA is a potential tool for MALL.However, students need guidance in ensuring the productiveness of SA in MALL.
The solubility of palladium (Pd) in crude oil at 150, 200 and 250 °C and its application to ore genesis

Although most economic palladium deposits occur in mafic igneous rocks and are the products of magmatic processes, there are a number of polymetallic deposits hosted by organic-rich (bituminous) black shales in which the palladium reaches exploitable concentrations. Considering that palladium is rarely concentrated by hydrothermal processes, and as organic-rich black shales are the source beds for the production of hydrocarbons and bitumen commonly records the path taken by these hydrocarbons, it is reasonable to entertain the possibility that liquid hydrocarbons may act as an ore fluid for palladium. In order to test the potential of liquid hydrocarbons to act as ore fluids, Pd (0) metal wires were reacted with three crude oils, A, B and C (A being the lightest and C being the heaviest crude oil), at temperatures of 150, 200 and 250 ˚C. After reaction at 150 ˚C, the Pd concentrations in crude oils A, B and C were 60 ± 20 ppb, 19 ± 4 ppb, and 128 ± 29 ppb, respectively. The corresponding concentrations at 200 ˚C, were 18 ± 6 ppb, 17 ± 6 ppb, and 50 ± 15 ppb, respectively, and at 250 ˚C, were 16 ± 2 ppb, 11 ± 1 ppb and 26 ± 2 ppb, respectively (n = 3). The measured solubility of Pd was highest in the heaviest crude oil, C. All three oils reached their maximum Pd concentration at 150 ˚C, with Pd concentration decreasing progressively as temperature increased to 250 ˚C. To gain insights into the mechanism controlling the dissolution of Pd in crude oil, we analysed the reacted Pd wires using X-ray Photoelectron Spectroscopy (XPS). The XPS spectrum displays a well-defined thiol (R-SH) peak at a binding energy of 163eV, which indicates that Pd has an affinity for reduced organosulfur compounds. Moreover, Pd concentrations were observed to increase with the total thiol content of the oil, thereby corroborating the results of the XPS analyses. Our results clearly show that Pd solubility in crude oil is promoted by the presence of thiols. As thiol-rich hydrocarbons usually develop in deep, high temperature (100–140 °C) carbonate sequences containing abundant sulfate mineralization, it is thought that the thiols are the products of thermochemical sulfate reduction (TSR) (Machel, 2001). We therefore propose that liquid hydrocarbons, which have interacted with sulfate at relatively high temperature, may serve as effective ore fluids for palladium.
Semantic Image Segmentation for Autonomous Driving Using Fully Convolutional Networks

In this paper, an application of semantic image segmentation is implemented in order to support autonomous driving of autonomous vehicles using deep learning based methods. The application is performed by Fully Convolutional Network (FCN) architectures obtained by making changes in Convolutional Neural Network (CNN) architectures. SYNTHIA- San Francisco (SF) is used as the dataset in the experimental studies performed for the application. The experimental studies are conducted using FCN architectures named FCN-AlexNet, FCN-32s, FCN-16s and FCN-8s. Considering these architectures and dataset, this study is carried out for the first time in the literature. The validations of the network models used for experimental studies are compared on the dataset. In addition, segmentation inferences are visualized and thus the segmentation precisions of the FCN architectures are observed. Experimental results are shown that FCNs are suitable for segmentation applications that can assist the autonomous driving of autonomous vehicles. However, it is thought that the experimental results can contribute to the literature and the researchers working on autonomous driving.
Comparative Evaluation of Techniques for Detection of Phishing URLs

One of the popular cyberattacks today is phishing. It combines social engineering and online identity theft to delude Internet users into submitting their personal information to cybercriminals. Reports have shown continuous increase in the number and sophistication of this attack worldwide. Phishing Uniform Resource Locator (URL) is a malicious web address often created to look like legitimate URL, in order to deceive unsuspecting users. Many algorithms have been proposed to detect phishing URLs and classify them as benign or phishing. Most of these detection algorithms are based on machine learning and detect using inherent characteristics of the URLs. In this study, we examine the performance of a number of such techniques. The algorithms were tested using three publicly available datasets. Our results revealed, overall, the Random Forest algorithm as the best performing algorithm, achieving an accuracy of 97.3%.
An Ontology Alignment Approach Combining Word Embedding and the Radius Measure

Ontology alignment plays a key role in achieving interoperability on the semantic Web. Inspired by the success of word embedding techniques in several NLP tasks, we propose a new ontology alignment approach based on the combination of word embedding and the radius measure. We tested our system on the OAEI ( http://oaei.ontologymatching.org/ ) conference track and then applied it to aligning ontologies in a real-world case study. The experimental results show that using word embedding and the radius measure make it possible to determine, with good accuracy, not only equivalence relations, but also hierarchical relations between concepts.
Using Machine Learning to Predict Ranking of Webpages in the Gift Industry

We use machine learning to predict the search engine rank of webpages. We use a list of keywords for 30 content blogs of an e-commerce company in the gift industry to retrieve 733 content pages occupying the first-page Google rankings and predict their rank using 30 ranking factors. We test two models, Light Gradient Boosting Machine (LightGBM) and Extreme Gradient Boosted Decision Trees (XGBoost), finding that XGBoost performs better for predicting actual search rankings, with an average accuracy of 0.86. The feature analysis shows the most impactful features are (a) internal and external links, (b) security of the web domain, and (c) length of H3 headings, and the least impactful features are (a) keyword mentioned in domain address, (b) keyword mentioned in the H1 headings, and (c) overall number of keyword mentions in the text. The results highlight the persistent importance of links in search-engine optimization. We provide actionable insights for online marketers and content creators.
Geolocation with Attention-Based Multitask Learning Models

Geolocation, predicting the location of a post based on text and other information, has a huge potential for several social media applications.Typically, the problem is modeled as either multi-class classification or regression.In the first case, the classes are geographic areas previously identified; in the second, the models directly predict geographic coordinates.The former requires discretization of the coordinates, but yields better performance.The latter is potentially more precise and true to the nature of the problem, but often results in worse performance.We propose to combine the two approaches in an attentionbased multitask convolutional neural network that jointly predicts both discrete locations and continuous geographic coordinates.We evaluate the multi-task (MTL) model against singletask models and prior work.We find that MTL significantly improves performance, reporting large gains on one data set, but also note that the correlation between labels and coordinates has a marked impact on the effectiveness of including a regression task.
Generate Structured Radiology Report from CT Images Using Image Annotation Techniques: Preliminary Results with Liver CT

A medical annotation system for radiology images extracts clinically useful information from the images, allowing the machines to infer useful abstract semantics and become capable of automatic reasoning and making diagnostic decision. It also supplies human-interpretable explanation for the images. We have implemented a computerized framework that, given a liver CT image, predicts radiological annotations with high accuracy, in order to generate a structured report, which includes predicting very specific high-level semantic content. Each report of a liver CT image is related to different inhomogeneous parts like the liver, lesion, and vessel. We put forward a claim that gathering all kinds of features is not suitable for filling all parts of the report. As a matter of fact, for each group of annotations, one should find and extract the best feature that results in the best answers for that specific annotation. To this end, the main challenge is discovering the relationships between these specific semantic concepts and their association with the low-level image features. Our framework was implemented by combining a set of the state-of-the-art low-level imaging features. In addition, we propose a novel feature (DLBP (deep local binary pattern)) based on LBP that incorporates multi-slice analysis in CT images and further improves the performance. In order to model our annotation system, two methods were used, namely multi-class support vector machine (SVM) and random subspace (RS) which is an ensemble learning method. Applying this representation leads to a high prediction accuracy of 93.1% despite its relatively low dimension in comparison with the existing works.
Detection of Handwritten Document Forgery by Analyzing Writers’ Handwritings

Since digitization is yet to be adopted globally, handwritten documents are still in use in many places. Handwritten documents are prone to get forged thanks to acts like the versatility of tampering which are very frequent among skilful fraudsters. Our research work focuses on one of the major problems to detect whether a document is treated as false or not based on an analysis of the handwriting of the content writers. Mostly, legal documents are scripted authentically by a single person. If the content is a combination of more than one person, then it will be treated like a forged document. The proposed work is formulated as a binary classification problem. Various contour related sliding window based features are extracted from word images of the corresponding handwritten document. The same writer with different handwriting styles are also considered here as well. Bagging meta-classifier is trained for classification of the extracted features. The accuracy of this proposed work is $$89.64\%$$ on IAM dataset is quite sound. We have also tested our method on IDRBT check image dataset. However, since there is a lack of direct implementation on this particular problem we could not make a comparative analysis of the proposed method.
Interleaving Planning and Control for Deformable Object Manipulation

We present a framework for deformable object manipulation that interleaves planning and control, enabling complex manipulation tasks without relying on high-fidelity modeling or simulation. The key question we address is when should we use planning and when should we use control to achieve the task? Planners are designed to find paths through complex configuration spaces, but for highly underactuated systems such as deformable objects achieving a specific configuration is very difficult even with high-fidelity models. Conversely, controllers can be designed to achieve specific configurations, but they can be trapped in undesirable local minima due to obstacles. Our approach consists of three components: (1) A global motion planner to generate gross motion of the deformable object; (2) A local controller for refinement of the configuration of the deformable object; and (3) A novel deadlock prediction algorithm to determine when to use planning versus control. By separating planning from control we are able to use different representations of the deformable object, reducing overall complexity and enabling efficient computation of motion. We demonstrate that our framework is able to successfully perform several manipulation tasks in simulation which cannot be performed using either our controller or planner alone.
A scalable multi-signal approach for the parallelization of self-organizing neural networks

Self-Organizing Neural Networks (SONNs) have a wide range of applications with massive computational requirements that often need to be satisfied with optimized parallel algorithms and implementations. In literature, SONN have been generally parallelized with GPU computing according to a single-signal paradigm: each GPU thread manages one or more nodes of the network and works concurrently on one input signal at the time. This paper presents two contributions. The first one is the experimental proof that the single-signal approach for SONNs is not optimal for the task, as it is intrinsically sequential at its core and thus inherently limited in its performance. The non-optimality of the single-signal paradigm is illustrated via a specific and simplified benchmark. The second contribution is the introduction of a new multi-signal paradigm for the parallelization of SONNs, whereby multiple signals are processed at once in each iteration hence allowing different GPU threads to work on different signals. The advantages of the multi-signal approach are shown through several benchmarks involving the Self-Organizing Adaptive Map (SOAM) algorithm as a basis for evaluation. Having a graph-based termination condition that depends on the features of the network being grown, the SOAM algorithm allows assessing both functional equivalence and performances of the paradigm proposed without relying on arbitrary thresholds. Nonetheless, the evaluation proposed has a broader scope since it refers to a unified framework for the GPU parallelization of a generic SONN.
Promoting Sustainable Agricultural Practices Through Incentives

For a better and more sustainable use of irrigation water in this article we present a system that incentivises and rewards virtuous behaviour in agricultural practices in a given multi-actor ecosystem while maintaining yield and improving quality of crops. The ongoing experimentation illustrated here takes place in the Northern Alps region of Italy and is made of IoT monitoring and actuating devices that, with the support of blockchain technologies and smart contracts enable the release of value (monetary or through value-add services) to the contributing actors based on monitored behaviours. Besides the specifics of the particular application the article illustrates how the presented framework could also be the common base for similar type of precision agriculture applications where widespread distributed IoT monitoring technologies and ledgers can be used to incentivise virtuous behaviours and fulfill given stakeholders interests.
Cursor Control Using Eye Ball Movement

A Few people and groups are not able to operate the computer because of their illnesses. In this scenario, it is more sound, to introduce a method of computer operation, which is easily accessible, even considering the disabilities of the differently abled. The Human eye can be considered as a perfect substitute of computer operating hardware. In this paper an Internet protocol camera has been used to take the image of an eye frame for cursor movement. In this regard, we need to focus on the job of the EYE, to begin with. For Pupil identification we are using Raspberry pi which can deal with the cursor of the computer and in this task, even an Eye Aspect Ratio (EAR) is ascertained which talks to the snaps of the eye (left or right) by utilizing the Open Source Computer Vision module of the Python programming dialect. The main goal of our proposed technique is to enhance the computer experience of the physically challenged, to help them overcome difficulties like usage of a mouse.
L<sub>0</sub> Gradient Regularized Low-Rank Tensor Model for Hyperspectral Image Denoising

Spatial spectral total variation has been widely used in the hyperspectral image (HSI) denoising algorithms. However, the total variation norm only penalizes the gradient magnitudes of HSIs, which may influence the recovery of denoised image edges. In this paper, we proposed a new l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> gradient regularized low-rank tensor model for removing HSI mixed noises. The low-rank tensor Tucker decomposition is applied for recovering the clean HSI part by using the HSI global correlation. The l0 gradient regularization controls the number of non-zero gradients directly and is designed to explore the spatial-spectral property of hyperspectral images and preserve important image features. The optimization problem is solved by the Augmented Lagrange Multiplier (ALM) method efficiently. The real-world data experiment demonstrates the better performance of our method.
Requirements Intelligence with OpenReq Analytics

With the rise of social media like Twitter and distribution platforms like app stores, users have various ways to express their opinions about software products. Popular software vendors get user feedback thousandfold per day. Research has shown that such feedback contains valuable information for software development teams. However, a manual analysis of user feedback is cumbersome and hard to manage. We present OpenReq Analytics, a software requirements intelligence service, that collects, processes, analyzes, and visualizes user feedback.
Creating, Enriching and Valorizing Treebanks of Ancient Greek

This paper shows the extent to which treebanks of Ancient Greek play a central role in the ongoing Pedalion project at the University of Leuven.Building on diverse treebanks readily available today, the project aims to make progress in the automated parsing of classical and postclassical Greek texts.Rather than developing new technology as such, our project endeavours to make deliberate and methodical use of the technology that already exists, essentially by combining and adapting both technology and data.This contribution offers a 'roadmap' of our project, surveying (a) the existing work on which we can rely, (b) the strategies which we adopt to reach better results in the automated processing of Ancient Greek and (c) the deliverables that have already been realised or are forthcoming.
Going Deeper Into Embedding Learning for Video Object Segmentation

In this paper, we investigate the principles of consistent training, between given reference and predicted sequence, for better embedding learning of semi-supervised video object segmentation. To accurately segment the target objects given the mask at the first frame, we realize that the expected feature embeddings of any consecutive frames should satisfy the following properties: 1) global consistency in terms of both foreground object(s) and background; 2) robust local consistency under a various object moving rate; 3) environment consistency between the training and inference process; 4) receptive consistency between the receptive fields of network and the variable scales of objects; 5) sampling consistency between foreground and background pixels to avoid training bias. With the principles in mind, we carefully design a simple pipeline to lift both accuracy and efficiency for video object segmentation effectively. With the ResNet-101 as the backbone, our single model achieves a J&F score of 81.0% on the validation set of Youtube-VOS benchmark without any bells and whistles. By applying multi-scale & flip augmentation at the testing stage, the accuracy can be further boosted to 82.4%. Code will be made available.
Reparameterizing Distributions on Lie Groups

Reparameterizable densities are an important way to learn probability distributions in a deep learning setting. For many distributions it is possible to create low-variance gradient estimators by utilizing a `reparameterization trick'. Due to the absence of a general reparameterization trick, much research has recently been devoted to extend the number of reparameterizable distributional families. Unfortunately, this research has primarily focused on distributions defined in Euclidean space, ruling out the usage of one of the most influential class of spaces with non-trivial topologies: Lie groups. In this work we define a general framework to create reparameterizable densities on arbitrary Lie groups, and provide a detailed practitioners guide to further the ease of usage. We demonstrate how to create complex and multimodal distributions on the well known oriented group of 3D rotations, SO(3), using normalizing flows. Our experiments on applying such distributions in a Bayesian setting for pose estimation on objects with discrete and continuous symmetries, showcase their necessity in achieving realistic uncertainty estimates.
MTSS: Learn from Multiple Domain Teachers and Become a Multi-Domain Dialogue Expert

How to build a high-quality multi-domain dialogue system is a challenging work due to its complicated and entangled dialogue state space among each domain, which seriously limits the quality of dialogue policy, and further affects the generated response. In this paper, we propose a novel method to acquire a satisfying policy and subtly circumvent the knotty dialogue state representation problem in the multi-domain setting. Inspired by real school teaching scenarios, our method is composed of multiple domain-specific teachers and a universal student. Each individual teacher only focuses on one specific domain and learns its corresponding domain knowledge and dialogue policy based on a precisely extracted single domain dialogue state representation. Then, these domain-specific teachers impart their domain knowledge and policies to a universal student model and collectively make this student model a multi-domain dialogue expert. Experiment results show that our method reaches competitive results with SOTAs in both multi-domain and single domain setting.