Reviewerly/exHarmony huggingface dataset:
A Novel Ensemble Credit Scoring Model Based on Extreme Learning Machine and Generalized Fuzzy Soft Sets

This paper mainly discusses the hybrid application of ensemble learning, classification, and feature selection (FS) algorithms simultaneously based on training data balancing for helping the proposed credit scoring model perform more effectively, which comprises three major stages. Firstly, it conducts preprocessing for collected credit data. Then, an efficient feature selection algorithm based on adaptive elastic net is employed to reduce the weakly related or uncorrelated variables to get high-quality training data. Thirdly, a novel ensemble strategy is proposed to make the imbalanced training data set balanced for each extreme learning machine (ELM) classifier. Finally, a new weighting method for single ELM classifiers in the ensemble model is established with respect to their classification accuracy based on generalized fuzzy soft sets (GFSS) theory. A novel cosine-based distance measurement algorithm of GFSS is also proposed to calculate the weights of each ELM classifier. To confirm the efficiency of the proposed ensemble credit scoring model, we implemented experiments with real-world credit data sets for comparison. The process of analysis, outcomes, and mathematical tests proved that the proposed model is capable of improving the effectiveness of classification in average accuracy, area under the curve (AUC), H-measure, and Brier’s score compared to all other single classifiers and ensemble approaches.
Encoding multi-granularity structural information for joint Chinese word segmentation and POS tagging

Recent studies show that the joint Chinese word segmentation and POS tagging can enhance the mutual interaction and yield better performances for two tasks. However, existing joint methods fail to effectively take the advantage of the multiple granularity of information, e.g., character, word and subword, which has been proven prominently useful. In this paper, we propose to improve the joint tasks by leveraging such multi-granularity of information, by exploiting the lattice-LSTM and Convolutional Network (GCN) models for effectively encoding the graph information. On five benchmark datasets our proposed model shows highly competitive performances, achieving the new state-of-the-art results in the literature. Further analysis reveals that the multi-granularity information can relieve the out-of-vocabulary and the long-range dependency issues. Also the GCN structure is more effective for encoding the multi-granularity graph information, compared with the lattice structure.
Chinese Sign Language Alphabet Recognition Based on Random Forest Algorithm

Sign language is the language deaf-mute people use to communicate with each other. While people with normal hearing generally can not understand it. Sign language recognition allows hard of hearing people to communicate with general society. In this study, we utilized surface Electromyography (sEMG) to recognize Chinese Sign Language alphabet which is an important part of Chinese Sign Language and recognizing them accurately is critical. For this purpose we attached 8 sEMG sensors on the right forearm of the subjects and collected sEMG signal when they were performing all the 30 alphabet letters. Random forest algorithm was used to classify the data after filtering and feature extraction process. Experimental results showed that random forest algorithm achieved an average recognition rate of 95.48% which was higher than Artificial Neural Networks (ANN) and Support Vector Machine (SVM) and had a more stable performance.
The KoBoCollect for Research Data Collection and Management (An experience in Researching the Socio-Economic Impact of Blood Disease in Banana)

In the research field, smartphone began to be used for field data collection. One application that was used was KoBoCollect. This article shared the experience of using KoBoCollect in research on the socio-economic impact of blood diseases on banana in Sumba Island. The research was conducted by interviewing 64 banana farmers that spread proportionally in four regencies in Sumba Island. The use of KoBoCollect/toolbox made it easy to collect, save and manage research data. KoBoCollect/toolbox provided the advantages of conducting paperless research, able to record data in complex formats such as numeric data, descriptive data, photos, videos, sounds, coordinates and other data, dynamically making forms changes when conducting research, and displaying interactive applications. KoBoCollect can be run off-line, made it possible to collect field data in remote locations and no internet network. The research data was transmitted online so that the data tabulation process was automatic. Simple data analysis such as median values, mode, graphs and distribution maps of data collection were provided in KoBotoolbox. With KoBoCollect/toolbox, researchers quickly find out the results of the research, as in this research it was known that blood disease had an impact on the decrease in the banana population revealed by 81.54% of farmers.
Accuracy of a Novel Virtual Articulator for Recording Three-Dimensional Dentition

To research and develop a novel virtual articulator system (the PN-300) based on computer binocular vision, raster scanning, and simulation technology and to conduct a preliminary evaluation of its accuracy.Two digital cameras were used to build the trajectory-tracking part of the virtual articulator system, and cameras combined with a projection module were used to form the scanning part of the system. The most prominent feature of the PN-300 is its ability to simultaneously obtain the 3D data of the subject's teeth and the movement trajectory of the mandible relative to the maxilla. The PN-300 recorded the linear, circular, and rectangular quadrilateral movements of a high-accuracy 3D electronic translation stage. The accuracy of measurement of the inclination of incisal guidance derived from the PN-300 based on the PROTAR evo7 articulator was also estimated.The measurement error was below 100 μm for the linear and circular movements, and the angle error was within 0.2 degrees for the rectangular quadrilateral movements. The error of inclination of protrusive incisal guidance was 1.51 ± 0.68 degrees, and for incisal guidance was 0.82 ± 0.55 degrees. Trajectories and incisal 3D data obtained by the PN-300 were combined with data from plaster models and CBCT to simulate mandibular movement and to calculate the trajectories of the condyle.The PN-300 achieved a good accuracy for recording mandibular movement and can be expected to calculate the movement of the condyle.
Groundwater contaminant source characterization with simulation model parameter estimation utilizing a heuristic search strategy based on the stochastic-simulation statistic method

In this study, a heuristic search strategy based on stochastic-simulation statistic (S–S) approach was developed for groundwater contaminant source characterization (GCSC) with simulation model parameter estimation. First, single kernel extreme learning machine (KELM) was built as surrogate system of the numerical simulation model to reduce huge computational load while evaluating the likelihood. However, compared with single KELM, multi-kernel extreme learning machine (MK-ELM) is more flexible for large amounts of data. To improve the approximation accuracy of the surrogate system to numerical simulation model, the MK-ELM surrogate system was first developed. Then, a heuristic search iterative process was first designed for GCSC with simulation model parameter estimation. The self-adaptive sampling method was proved to be more efficient than one-time sampling. Based on this idea, a self-adaptive feedback correction step was inserted into the heuristic search iterative process to ameliorate the training samples of the surrogate system in the posterior region, which further improved accuracy of simultaneous identification results. Finally, the identification results were obtained when the iteration terminated. The proposed approaches were tested in a hypothetical case study. It was shown that the heuristic search strategy can be used to assist in groundwater contaminant source characterization with simulation model parameter estimation.
Faasm: Lightweight Isolation for Efficient Stateful Serverless Computing

Serverless computing is an excellent fit for big data processing because it can scale quickly and cheaply to thousands of parallel functions. Existing serverless platforms isolate functions in ephemeral, stateless containers, preventing them from directly sharing memory. This forces users to duplicate and serialise data repeatedly, adding unnecessary performance and resource costs. We believe that a new lightweight isolation approach is needed, which supports sharing memory directly between functions and reduces resource overheads. We introduce Faaslets, a new isolation abstraction for high-performance serverless computing. Faaslets isolate the memory of executed functions using software-fault isolation (SFI), as provided by WebAssembly, while allowing memory regions to be shared between functions in the same address space. Faaslets can thus avoid expensive data movement when functions are co-located on the same machine. Our runtime for Faaslets, Faasm, isolates other resources, e.g. CPU and network, using standard Linux cgroups, and provides a low-level POSIX host interface for networking, file system access and dynamic loading. To reduce initialisation times, Faasm restores Faaslets from already-initialised snapshots. We compare Faasm to a standard container-based platform and show that, when training a machine learning model, it achieves a 2x speed-up with 10x less memory; for serving machine learning inference, Faasm doubles the throughput and reduces tail latency by 90%.
Background Subtraction Using an Adaptive Local Median Texture Feature in Illumination Changes Urban Traffic Scenes

Background subtraction is commonly employed in foreground object detection in urban traffic scenes.Most of the current color or texture feature-based background subtraction models are easily contaminated by sudden and gradual illumination variations in urban traffic scenes.To resolve this deficiency, an adaptive local median texture feature, which extracts the adaptive distance threshold employing the median information in a predefined local region of a pixel and Weber's law, is introduced.In addition, a sample consensus-based model that evolved from portable visual background extractor is proposed using an adaptive local median texture feature.Then, the foreground is labeled by comparing the input video frames feature with the model.Moreover, to adapt the dynamic background, the random update scheme is used to update the model.Extensive experimental results on the public Change Detection data set of 2014 (CDnet2014) and the real-world urban traffic videos demonstrate that our background subtraction method is superior to the other state-of-the-art texture-feature-based methods.The qualitative and quantitative results show the encouraging efficiency of the proposed technique to deal with sudden and gradual illumination variations in real-world urban traffic scenes.
Performance of CAMS Radiation Service and HelioClim-3 databases of solar radiation at surface: evaluating the spatial variation in Germany

Abstract. The present work deals with the spatial consistency of two well-known databases of solar radiation received at ground level: the CAMS Radiation Service database version 3.2, abbreviated as CAMS-Rad and the HelioClim-3 database version 5, abbreviated as HC3v5. Both databases are derived from satellite images. They are validated against 10 min means of irradiance for the period 2010–2018 recorded in a network of 26 ground stations in Germany operated by the Deutscher Wetterdienst (DWD). For the CAMS-Rad database, the correlation coefficient between ground measurements and estimates ranges between 0.83 and 0.92 for all sky conditions. The bias ranges from −41 and 32 W m−2 (−11 % and 10 % of the mean irradiance). The standard deviation ranges between 89 and 129 W m−2 (25 % and 39 %). For the HC3v5 database, the correlation coefficient ranges between 0.90 and 0.95. The bias and the standard deviation are comprised between −22 and 16 W m−2 (−6 % and 5 %), and between respectively 70 and 104 W m−2 (20 % and 31 %). For the CAMS Rad database, overestimation is observed in the South, and underestimation in the North with a faint tendency of the bias to increase from East to West. For the HC3v5 database, the bias is fairly homogeneous across Germany. For both databases, there is no noticeable spatial trend in the standard deviation.
Multitenancy for Fast and Programmable Networks in the Cloud.

Fast and programmable network devices are now readily available, both in the form of programmable switches and smart network-interface cards. Going forward, we envision that these devices will be widely deployed in the networks of cloud providers (e.g., AWS, Azure, and GCP) and exposed as a programmable surface for cloud customers—similar to how cloud customers can today rent CPUs, GPUs, FPGAs, and ML accelerators. Making this vision a reality requires us to develop a mechanism to share the resources of a programmable network device across multiple cloud tenants. In other words, we need to provide multitenancy on these devices. In this position paper, we design compile and run-time approaches to multitenancy. We present preliminary results showing that our design provides both efficient utilization of the resources of a programmable network device and isolation of tenant programs from each other.
Chapter 6 Reasoning in Knowledge Graphs: An Embeddings Spotlight

In this chapter we introduce the aspect of reasoning in Knowledge Graphs. As in Chap. 2 , we will give a broad overview focusing on the multitude of reasoning techniques: spanning logic-based reasoning, embedding-based reasoning, neural network-based reasoning, etc. In particular, we will discuss three dimensions of reasoning in Knowledge Graphs. Complementing these dimensions, we will structure our exploration based on a pragmatic view of reasoning tasks and families of reasoning tasks: reasoning for knowledge integration, knowledge discovery and application services.
Multi-view clustering by exploring complex mapping relationship between views

Almost all of the existing methods assume that the samples between different views have a strict one-to-one relationship whether it is for complete multi-view data or for partial multi-view data. In this paper, we refer to the neglected many-to-many relationship between cross-view samples as the complex mapping relationship between views. To address this issue, we propose a resultful Complex Mapping Multi-View Clustering (CMMVC) method by exploring the complex mapping relationship between views. We firstly construct a complex mapping relationship matrix for each pair of views by using the nearest neighbor relationship between cross-view samples. Then the complex mapping relationship matrix is introduced into the framework of multi-view clustering based on non-negative matrix factorization to guide multi-view information fusion in order to obtain more compact representation of multi-view data space. Finally, we give the objective function of CMMVC and an effective optimization scheme. The experimental results demonstrate the advantages of the proposed CMMVC method on multi-view clustering tasks by mining the complex mapping relationship between different views.
Investigating the Effectiveness of YouTube as a Learning Tool among EFL Students at Baghdad University

YouTube is not just a platform that individuals share, upload, comment on videos; teachers and educators can utilize it to the best maximum so that students can have benefits.This study aims at investigating how active and influential YouTube can be in the educational process and how it is beneficial for language teachers to enhance the skills of students.The study demonstrates different theoretical frameworks that tackle the employment of technology to enhance the learning/teaching process.It relies on the strategies of Berk (2009) for using multimedia media, video clips in particular to develop the abilities of teachers for using technology in classrooms.To achieve the objective of the study, the researchers develop a questionnaire and apply it to fourth-year college students, University of Baghdad, to give evidence and to prove the effectiveness of technology in the academic field.The paper examines classes where computers can be employed, and also shows the challenges that face teachers and educators concerning this application.The researchers conclude that YouTube is an essential tool in classrooms as it attracts the attention of students and develops their mentality and creativity.It also helps cover the materials comprehensively, especially language.YouTube brings the fun element into classes, which thereby meet the interests of students.Such findings have a significant impact on the learning process as the students will find the educational environment more encouraging and exciting.Besides, they find the material presented worth studying, and this way, they would appreciate the efforts exerted in explaining the information.The research intends to be of value to teachers for the use of technology and for students to have a better comprehension of the materials presented.
Simple Compounded-Label Training for Fact Extraction and Verification

Automatic fact checking is an important task motivated by the need for detecting and preventing the spread of misinformation across the web.The recently released FEVER challenge provides a benchmark task that assesses systems' capability for both the retrieval of required evidence and the identification of authentic claims.Previous approaches share a similar pipeline training paradigm that decomposes the task into three subtasks, with each component built and trained separately.Although achieving acceptable scores, these methods induce difficulty for practical application development due to unnecessary complexity and expensive computation.In this paper, we explore the potential of simplifying the system design and reducing training computation by proposing a joint training setup in which a single sequence matching model is trained with compounded labels that give supervision for both sentence selection and claim verification subtasks, eliminating the duplicate computation that occurs when models are designed and trained separately.Empirical results on FEVER indicate that our method: (1) outperforms the typical multi-task learning approach, and (2) gets comparable results to top performing systems with a much simpler training setup and less training computation (in terms of the amount of data consumed and the number of model parameters), facilitating future works on the automatic fact checking task and its practical usage.
Dynamic Sentence Boundary Detection for Simultaneous Translation

Simultaneous Translation is a great challenge in which translation starts before the source sentence finished.Most studies take transcription as input and focus on balancing translation quality and latency for each sentence.However, most ASR systems can not provide accurate sentence boundaries in realtime.Thus it is a key problem to segment sentences for the word streaming before translation.In this paper, we propose a novel method for sentence boundary detection that takes it as a multi-class classification task under the endto-end pre-training framework.Experiments show significant improvements both in terms of translation quality and latency.
Sarcasm Detection using Context Separators in Online Discourse

Sarcasm is an intricate form of speech, where meaning is conveyed implicitly.Being a convoluted form of expression, detecting sarcasm is an assiduous problem.The difficulty in recognition of sarcasm has many pitfalls, including misunderstandings in everyday communications, which leads us to an increasing focus on automated sarcasm detection.In the second edition of the Figurative Language Processing (FigLang 2020) workshop, the shared task of sarcasm detection released two datasets, containing responses along with their context sampled from Twitter and Reddit.In this work, we use RoBERT a large to detect sarcasm in both the datasets.We further assert the importance of context in improving the performance of contextual word embedding based models by using three different types of inputs -Response-only, Context-Response, and Context-Response (Separated).We show that our proposed architecture performs competitively for both the datasets.We also show that the addition of a separation token between context and target response results in an improvement of 5.13% in the F1-score in the Reddit dataset.
Harnessing deep learning algorithms to predict software refactoring

During software maintenance, software systems need to be modified by adding or modifying source code. These changes are required to fix errors or adopt new requirements raised by stakeholders or market place. Identifying thetargeted piece of code for refactoring purposes is considered a real challenge for software developers. The whole process of refactoring mainly relies on software developers’ skills and intuition. In this paper, a deep learning algorithm is used to develop a refactoring prediction model for highlighting the classes that require refactoring. More specifically, the gated recurrent unit algorithm is used with proposed pre-processing steps for refactoring predictionat the class level. The effectiveness of the proposed model is evaluated usinga very common dataset of 7 open source java projects. The experiments are conducted before and after balancing the dataset to investigate the influence of data sampling on the performance of the prediction model. The experimental analysis reveals a promising result in the field of code refactoring prediction
A Three-bar Truss Design using Single-solution Simulated Kalman Filter Optimizer

&#x0D; &#x0D; &#x0D; &#x0D; Three-bar truss is a structure that is important in the field of civil engineering. Design of the three-bar truss is thus an interesting problem in engineering. Metaheuristic approach has been employed to get near-optimal solution of this problem. A study to investigate the effectiveness of a new single-solution simulated Kalman filter (ssSKF) algorithm in three-bar truss design is presented in this paper. The solution obtained by the ssSKF is as good as hybrid lightning search algorithm-simplex method and better than solutions produced other algorithms in literature.&#x0D; &#x0D; &#x0D; &#x0D;