Reviewerly/exHarmony huggingface dataset:
Syntax-aware Multilingual Semantic Role Labeling

Recently, semantic role labeling (SRL) has earned a series of success with even higher performance improvements, which can be mainly attributed to syntactic integration and enhanced word representation. However, most of these efforts focus on English, while SRL on multiple languages more than English has received relatively little attention so that is kept underdevelopment. Thus this paper intends to fill the gap on multilingual SRL with special focus on the impact of syntax and contextualized word representation. Unlike existing work, we propose a novel method guided by syntactic rule to prune arguments, which enables us to integrate syntax into multilingual SRL model simply and effectively. We present a unified SRL model designed for multiple languages together with the proposed uniform syntax enhancement. Our model achieves new state-of-the-art results on the CoNLL-2009 benchmarks of all seven languages. Besides, we pose a discussion on the syntactic role among different languages and verify the effectiveness of deep enhanced representation for multilingual SRL.
Syntax-Aware Aspect Level Sentiment Classification with Graph Attention Networks

Aspect level sentiment classification aims to identify the sentiment expressed towards an aspect given a context sentence. Previous neural network based methods largely ignore the syntax structure in one sentence. In this paper, we propose a novel target-dependent graph attention network (TD-GAT) for aspect level sentiment classification, which explicitly utilizes the dependency relationship among words. Using the dependency graph, it propagates sentiment features directly from the syntactic context of an aspect target. In our experiments, we show our method outperforms multiple baselines with GloVe embeddings. We also demonstrate that using BERT representations further substantially boosts the performance.
A Block-Based Union-Find Algorithm to Label Connected Components on GPUs

In this paper, we introduce a novel GPU-based Connected Components Labeling algorithm: the Block-based Union Find. The proposed strategy significantly improves an existing GPU algorithm, taking advantage of a block-based approach. Experimental results on real cases and synthetically generated datasets demonstrate the superiority of the new proposal with respect to state-of-the-art.
Designing Android Gaming News &amp; Information Application Using Java-Based Web Scraping Technique

It is important for gamers to know the latest news update about video games that they like. Researcher finds that there are many people who likes and want to know more about video game, but services available to find news about video games are so limited, espcially on Android platform. The solution of this problem is to design an Android-based application where the apllication can contain the lastest video game news from reliable sources. Aside from news, other necessary video game information like video game releases, video game reviews, the history, franchises, system requirement and also famous people in the industry. All these information will be pulled using a technique called Web Scraping, which is used for extracting data from websites. This research is expected to make an Android application which contains all information and news in video game world that can provide a complete and detailed information. In the end of the research based on our questionare, it was estimated to satisfy 80% of news consumption needs for gamers.
Measurement-induced geometric measures of correlations based on the Hellinger distance and their local decoherence

We apply the modified Brodutch and Modi method of constructing geometric measures of correlations to obtain analytical expressions for measurement-induced geometric classical and quantum correlations based on the Hellinger distance for two-qubit Bell diagonal states. Moreover, we study dynamics of the classical and quantum correlations for these states under decoherence induced by the action of bit flip, bit-phase flip, phase flip, depolarizing and generalized amplitude damping local quantum channels and identify all possible types of the decoherence dynamics. In particular, we show that these correlations exhibit freezing, sudden change and sudden transition between classical and quantum decoherence phenomena.
Image retrieval based on colour and improved NMI texture features

This paper proposes an improved method for extracting NMI features. This method uses Particle Swarm Optimization in advance to optimize the two-dimensional maximum class-to-class variance (2OTSU) in advance. Afterwards, the optimized 2OUSU is introduced into the Pulse Coupled Neural Network (PCNN) to automatically obtain the number of iterations of the loop. We use an improved PCNN method to extract the NMI features of the image. For the problem of low accuracy of single feature, this paper proposes a new method of multi-feature fusion based on image retrieval. It uses HSV colour features and texture features, where, the texture feature extraction methods include: Grey Level Co-occurrence Matrix (GLCM), Local Binary Pattern (LBP) and Improved PCNN. The experimental results show that: on the Corel-1k dataset, compared with similar algorithms, the retrieval accuracy of this method is improved by 13.6%; On the AT&T dataset, the retrieval accuracy is improved by 13.4% compared with the similar algorithm; on the FD-XJ dataset, the retrieval accuracy is improved by 17.7% compared with the similar algorithm. Therefore, the proposed algorithm has better retrieval performance and robustness compared with the existing image retrieval algorithms based on multi-feature fusion.
Mining Negative Frequent regular Itemsets from Data Streams

Many Application in modern days requires capturing continuous data generated from remote sensors.Devices etc. and the data flows in streams, and the data is processed, and the processed results are temporarily stored.The original data is seldom stored.A lot amount of s Knowledge hidden in the data flowing in streams.Data patterns hidden in the data when mined will discover the hidden knowledge.Most of the research focussed on ming frequent items and positive associations.The regularity of occurrence of items is also important in addition to the frequency of occurrence of the itemsets.While positive associations are good, negative associations also revel very interesting findings which will help in taking important decisions.In this approach, novel algorithms, along with its implementation presented that will help ming regular, frequent, and negative itemsets from the data streams.
Image segmentation by improved minimum spanning tree with fractional differential and Canny detector

In this study, we propose an algorithm that uses an improved Minimum Spanning Tree algorithm and a modified Canny edge detector to segment images that contain a considerable amount of noises. First, we use our modified Canny operator to pre-process an image, and record the obtained object boundary information; then, we apply the improved Minimum Spanning Tree algorithm to associate the above information with boundary points in order to separate edges into two classes in the image, namely the inner and boundary regions. In particular, Minimum Spanning Tree algorithm is improved by using Fractional differential and combining the functions of the intra-regional and inter-regional differences with a function for edge weights. Based on the experimental results, compared with the other four exiting algorithms, the new algorithm has the higher accuracy and the better effect for noised image segmentation.
Explainable Anomaly Detection via Feature-Based Localization

Deep learning has made remarkable progress in the field of image anomaly detection. An explanation of the validity of the detection is required for practical application. Conventional methods for localizing abnormal regions are mainly based on image reconstruction errors. However, they cannot directly extract specific features from abnormal regions, which limits localization performance. To address this issue, we developed a method for explainable anomaly detection in an unsupervised manner. We trained a feature extractor to extract features that had both the compactness of the normal state and the descriptiveness of the abnormal state for the input images and their reconstructed images. For explainability, our method localized and visualized abnormal regions by accumulating intermediate layers, which led to a significant difference in features extracted from the input image and the reconstructed image. The quantitative results of the defect segmentation and the qualitative results of anomaly localization from experiments on two datasets showed that our method outperformed conventional methods when it came to localizing abnormal regions.
Characterizing the Deep Neural Networks Inference Performance of Mobile Applications

Today's mobile applications are increasingly leveraging deep neural networks to provide novel features, such as image and speech recognitions. To use a pre-trained deep neural network, mobile developers can either host it in a cloud server, referred to as cloud-based inference, or ship it with their mobile application, referred to as on-device inference. In this work, we investigate the inference performance of these two common approaches on both mobile devices and public clouds, using popular convolutional neural networks. Our measurement study suggests the need for both on-device and cloud-based inferences for supporting mobile applications. In particular, newer mobile devices is able to run mobile-optimized CNN models in reasonable time. However, for older mobile devices or to use more complex CNN models, mobile applications should opt in for cloud-based inference. We further demonstrate that variable network conditions can lead to poor cloud-based inference end-to-end time. To support efficient cloud-based inference, we propose a CNN model selection algorithm called CNNSelect that dynamically selects the most appropriate CNN model for each inference request, and adapts its selection to match different SLAs and execution time budgets that are caused by variable mobile environments. The key idea of CNNSelect is to make inference speed and accuracy trade-offs at runtime using a set of CNN models. We demonstrated that CNNSelect smoothly improves inference accuracy while maintaining SLA attainment in 88.5% more cases than a greedy baseline.
Generating Discourse Inferences from Unscoped Episodic Logical Formulas

Unscoped episodic logical form (ULF) is a semantic representation capturing the predicateargument structure of English within the episodic logic formalism in relation to the syntactic structure, while leaving scope, word sense, and anaphora unresolved.We describe how ULF can be used to generate natural language inferences that are grounded in the semantic and syntactic structure through a small set of rules defined over interpretable predicates and transformations on ULFs.The semantic restrictions placed by ULF semantic types enables us to ensure that the inferred structures are semantically coherent while the nearness to syntax enables accurate mapping to English.We demonstrate these inferences on four classes of conversationallyoriented inferences in a mixed genre dataset with 68.5% precision from human judgments.
Large Margin Training for Attention Based End-to-End Speech Recognition

A method of attention-based end-to-end (E2E) automatic speech recognition (ASR) training, includes performing cross-entropy training of a model, based on one or more input features of a speech signal, performing beam searching of the model of which the cross-entropy training is performed, to generate an n-best hypotheses list of output hypotheses, and determining a one-best hypothesis among the generated n-best hypotheses list. The method further includes determining a character-based gradient and a word-based gradient, based on the model of which the cross-entropy training is performed and a loss function in which a distance between a reference sequence and the determined one-best hypothesis is maximized, and performing backpropagation of the determined character-based gradient and the determined word-based gradient to the model, to update the model.
Designing a Maturity Model for a Distributed Software Organization. An Experience Report

We present early experiences with defining and validating a software maturity model (SMM) for a distributed, research-driven organization of independent and self-organizing teams of diverse cultures, experience and maturity. The paper briefly outlines the model, but focuses on the early stages of building and validating it. Based on that, we identify major factors contributing to the successful deployment of a SMM.
Understanding and Predicting Users’ Rating Behavior: A Cognitive Perspective

Online reviews are playing an increasingly important role in understanding and predicting users’ rating behavior, which brings great opportunities for users and organizations to make better decisio...
Smart System Studied

This paper aims to focus on the smart system as optimized expert knowledge acquisition system as new approaches to natural language understanding system. This method can finish fine processing for any text segment instantly. The module's precision machining can adopt big production method that combines on the line first, complete coverage and accurate grasp each language point and knowledge point and original point even their respective combination. Its characteristics are teachers and students can use the text analyzed method to do the fine processing of the same knowledge module, and only in Chinese or English, through the selection of keywords and terminology and knowledge modules that can be used as the menu to be selected as the way to achieve knowledge with the system. The result is the learning environment that enables human-computer collaboration system namely smart system to optimize the expert knowledge acquisition and the natural language understanding as a research field that has great significance to human beings. Its significance is that this learning environment software based on the National Excellent Courses by using the language chess with the feature of the introduction on the knowledge big production mode for the textual knowledge module finishing at Peking University.
An Empirical Study on the Impact of an Automated Writing Assessment on Chinese College Students’ English Writing Proficiency

Automated writing evaluation (AWE) is an online essay scoring system which can provide feedback and revising advice to teachers and students.In this paper, an empirical study was carried out to explore the impact of Writing Roadmap2.0(WRM2.0)-anautomated writing assessment system on the English writing proficiency, which is reflected in three dimensions-the language form, the contextual structure and the writing quality of non-English major freshmen in China.In this study, 100 participants were divided into the experimental class (EC) and the controlled one (CC) at random, with 50 ones in each class.Both qualitative method and quantitative method were adopted for data collection and analysis, including the pre-and post-tests on WRM2.0, teacher-assessed writing task and interviews.The results revealed that while there was no significant difference in the writing proficiency in pre-test on WRM2.0 between EC and CC, the former outperformed the latter in both post-test on WRM2.0 and teacher-assessed writing task in the final exam in two dimensions: the language form and the writing quality.With regard to the aspect of contextual structure, EC benefited a little on WRM2.0.Generally speaking, this empirical study observed positive impact of WRM2.0 on writing proficiency of L2 students in China.It is expected that the findings will provide references for the further integration of AWE with writing teaching and learning in the EFL classroom.
High Speed Cryptography Architecture for Health Information Exchange

Since countries like India promoting their healthcare departments to be recognized as clinically advanced in the world.The worldwide recognition for the countries in the domain of healthcare is possible only when they adopt best practices like cost effective quality healthcare standards.The secure Health Information Exchange (HIE) is an important way to maintain Electronic Health Record (EHR) which improve healthcare system of a country.Development of electronic health records adds potential benefit in providing reliable access to health information and thereby improving the national healthcare system.However, the prospect of storing, transmitting, and sharing health information through electronic media raises many new challenges on information integration and protection.Protection of patient health information electronically is very crucial and need to develop systems and structures that support secure health information exchange among healthcare providers, patients and customers.The core competency of EHR and HIE is to maintain confidential and supportive relationship among patient and healthcare providers in maintaining patients health information.The confidentiality in health records can be achieved through mutual legal agreements, advocacy and technology that ensure appreciable secure information interchange.So this article also demonstrates effective practices which can be applied to the development of HIEs and presents a security architecture which can be adopted for exchange of health information to leverage common government and commercial practices in electronic health record.
Automatic Code Summarization: A Systematic Literature Review

Background: During software maintenance and development, the comprehension of program code is key to success. High-quality comments can help us better understand programs, but they're often missing or outmoded in today's programs. Automatic code summarization is proposed to solve these problems. During the last decade, huge progress has been made in this field, but there is a lack of an up-to-date survey. Aims: We studied publications concerning code summarization in the field of program comprehension to investigate state-of-the-art approaches. By reading and analyzing relevant articles, we aim at obtaining a comprehensive understanding of the current status of automatic code summarization. Method: In this paper, we performed a systematic literature review over the automatic source code summarization field. Furthermore, we synthesized the obtained data and investigated different approaches. Results: We successfully collected and analyzed 41 selected studies from the different research communities. We exhaustively investigated and described the data extraction techniques, description generation methods, evaluation methods and relevant artifacts of those works. Conclusions: Our systematic review provides an overview of the state of the art, and we also discuss further research directions. By fully elaborating current approaches in the field, our work sheds light on future research directions of program comprehension and comment generation.
Cauchy Matrix Factorization for Tag-Based Social Image Retrieval

User-provided tags associated with social images are essential information for social image retrieval. Unfortunately, these tags are often imperfect to describe the visual contents, which severely degrades the performance of image retrieval. Tag relevance learning models are proposed to improve the descriptive powers of tags mostly based on the Gaussian noise assumption. However, the intrinsic probability distribution of the noise is unknown and other probability distributions may be much better. Towards this end, this paper investigates the applicable probability distributions of tag noise and proposes a novel Cauchy Matrix Factorization (CMF) method for tag-based image retrieval. The Cauchy probability distribution is robust to all kinds of noise and more suitable to model the tagging noise of social images. Therefore, we utilize Cauchy distribution to model noise under the matrix factorization framework. Besides, other five probability density functions, i.e., Gaussian, Laplacian, Poisson, Student-t and Logistic, are investigated to model noise of social tags. To evaluate the performance of different probability distributions, extensive experiments on two widely-used datasets are conducted and results show the robustness of CMF to noisy tags of social images.
Jointly Learning Entity and Relation Representations for Entity Alignment

Entity alignment is a viable means for integrating heterogeneous knowledge among different knowledge graphs (KGs). Recent developments in the field often take an embedding-based approach to model the structural information of KGs so that entity alignment can be easily performed in the embedding space. However, most existing works do not explicitly utilize useful relation representations to assist in entity alignment, which, as we will show in the paper, is a simple yet effective way for improving entity alignment. This paper presents a novel joint learning framework for entity alignment. At the core of our approach is a Graph Convolutional Network (GCN) based framework for learning both entity and relation representations. Rather than relying on pre-aligned relation seeds to learn relation representations, we first approximate them using entity embeddings learned by the GCN. We then incorporate the relation approximation into entities to iteratively learn better representations for both. Experiments performed on three real-world cross-lingual datasets show that our approach substantially outperforms state-of-the-art entity alignment methods.
Student Academic Performance Prediction using Artificial Neural Networks: A Case Study

Students dropout and delay in graduation are significant problems at Katsina State Institute of Technology and Management (KSITM).There are various reasons for that, students' performances during first year is one of the major contributing factors.This study aims at predicting poor students' performances that might lead to dropout or delay in graduation so as to allow the institution to develop strategic programs that will help improve student performance and enable the student to graduate in time without any problem.This study presents a neural network model capable of predicting student's GPA using students' personal information, academic information, and place of residence.A sample of 61 Computer Networking students' dataset was used to train and test the model in WEKA software tool.The accuracy of the model was measured using well-known evaluation criteria.The model correctly predicts 73.68% of students' performance and, specifically, 66.67% of students that are likely to dropout or experience delay before graduating.
Real-Life Challenges in Automotive Release Planning

Context: The use of agile software development is increasing, even in regulated domains like the automotive domain.At the same time, traditional sequential processes are still in use.Collaboration between agile and hybrid projects within these complex traditional product development processes is difficult.Especially the creation and synchronization of a qualification phase plan is challenging.Objective: The aim of this study is to provide insights into the state of the practice to understand challenges related to the combined use of agile and traditional paradigms in release planning in the automotive domain.Method: Based on semi-structured interviews, an online survey with 39 respondents was conducted at Dr. Ing. h.c.F. Porsche AG.Results: We present the challenges identified in release planning, such as lack of transparency regarding the status quo of related projects.Furthermore, we motivate how agile development methods could improve collaboration between projects in release planning.Conclusions: There are many challenges in the context of co-existing agile and traditional projects.We discuss how agile practices like daily standup or continuous integration could address the identified challenges.