Reviewerly/exHarmony huggingface dataset:
A Novel ELM Ensemble for Time Series Prediction

This paper presents a novel methodology for time series prediction. It is based on Extreme Learning Machines and an adaptive ensemble techniques. It is tested successfully on the CIF 2016 competition datasets which are composed of 72 time series in total. Among those, 48 time series are artificial with each having 108 training data points and 12 testing points. So for each artificial time series, there are 120 values, which is more than that of the rest 24 real time series.
YNU-HPCC at SemEval-2019 Task 6: Identifying and Categorising Offensive Language on Twitter

This document describes the submission of team YNU-HPCC to SemEval-2019 for three Sub-tasks of Task 6: Sub-task A, Sub-task B, and Sub-task C. We have submitted four systems to identify and categorise offensive language.The first subsystem is an attentionbased 2-layer bidirectional long short-term memory (BiLSTM).The second subsystem is a voting ensemble of four different deep learning architectures.The third subsystem is a stacking ensemble of four different deep learning architectures.Finally, the fourth subsystem is a bidirectional encoder representations from transformers (BERT) model.Among our models, in Sub-task A, our first subsystem performed the best, ranking 16th among 103 teams; in Sub-task B, the second subsystem performed the best, ranking 12th among 75 teams; in Sub-task C, the fourth subsystem performed best, ranking 4th among 65 teams.
m_y at SemEval-2019 Task 9: Exploring BERT for Suggestion Mining

This paper presents our system to the SemEval-2019 Task 9, Suggestion Mining from Online Reviews and Forums.The goal of this task is to extract suggestions such as the expressions of tips, advice, and recommendations.We explore Bidirectional Encoder Representations from Transformers (BERT) focusing on target domain pre-training in Subtask A which provides training and test datasets in the same domain.In Subtask B, the cross domain suggestion mining task, we apply the idea of distant supervision.Our system obtained the third place in Subtask A and the fifth place in Subtask B, which demonstrates its efficacy of our approaches.
YNU-HPCC at SemEval-2019 Task 9: Using a BERT and CNN-BiLSTM-GRU Model for Suggestion Mining

Consumer opinions towards commercial entities are generally expressed through online reviews, blogs, and discussion forums.These opinions largely express positive and negative sentiments towards a given entity; however, they may also contain suggestions for improving the entity.In this task, we extract suggestions from a given unstructured text, in contrast to the traditional opinion mining systems.This type suggestion mining is more applicable and extends capabilities.In this paper, we propose the use of bidirectional encoder representation learned from transformers (BERT) to address the problem of domain specific suggestion mining in task A. In detail, BERT is also used to extract feature vectors and perform fine-tuning for the task .For Task B, we applied an ensemble model to combine the BiLSTM, CNN, and GRU models, which can perform cross domain suggestion mining.Officially released results show that our system performs better than the baseline algorithm does.
Distributional Semantics in the Real World: Building Word Vector Representations from a Truth-Theoretic Model

Distributional semantics models (DSMs) are known to produce excellent representations of word meaning, which correlate with a range of behavioural data.As lexical representations, they have been said to be fundamentally different from truth-theoretic models of semantics, where meaning is defined as a correspondence relation to the world.There are two main aspects to this difference: a) DSMs are built over corpus data which may or may not reflect 'what is in the world'; b) they are built from word co-occurrences, that is, from lexical types rather than entities and sets.In this paper, we inspect the properties of a distributional model built over a set-theoretic approximation of 'the real world'.To achieve this, we take the annotation a large database of images marked with objects, attributes and relations, convert the data into a representation akin to first-order logic and build several distributional models using various combinations of features.We evaluate those models over both relatedness and similarity datasets, demonstrating their effectiveness in standard evaluations.This allows us to conclude that, despite prior claims, truth-theoretic models are good candidates for building graded lexical representations of meaning.
DESAIN SISTEM PEMBELAJARAN JARAK JAUH BERBASIS SMART CLASSROOM MENGGUNAKAN LAYANAN LIVE VIDEO WEBCASTING

The research aims to provide broad access to education for people in the Industrial Revolution 4.0 through various higher education institutions that actively participate using the Blended Learning model. The long-term goal of this research is to develop innovative and flexible learning models based on Smart Classroom. System design in this study was developed to realize a balance between virtual teaching (virtual class) and face-to-face (physical class) that takes place at the same time using the Live Video Webcasting service. The research method uses a quantitative approach and the type of research is R &amp; D adopting the Thiagarajan 4D development model. This research produces a Smart Classroom system based on Streaming Webcast through several stages, namely: (1) System Design, (2) System Creation, (3) System Testing. This developed system allows interaction in learning in the physical class as well as teaching in a virtual class remotely.Keywords: Revolusi Industri 4.0, Blended Learning, Smart Classroom, Live Video Webcasting.
Pairing-Free Identity-Based Encryption with Authorized Equality Test in Online Social Networks

Online social networking applications have become more and more popular in the advance of the technological age. Much of our personal information has been disclosed in social networking activities and privacy-preserving still remains a research challenge in social network. Public key encryption scheme with equality test(PKEET), which is an extension of public key encryption with keyword search (PEKS), seems to be a solution. PKEET enables the tester to check whether two given ciphertexts are derived from the same plaintext. Recently, Zhu et al. proposed a pairing-free public key encryption scheme with equality test based on the traditional public key cryptosystem. However, it suffers from certificates management issue. In this paper, we propose a pairing-free identity-based encryption scheme with authorized equality test(PF-IBEAET). The PF-IBEAET scheme also provides fine-grained authorizations. We prove that the scheme is one way secure against chosen identity and chosen ciphertext attack (OW-ID-CCA) and indistinguishable against chosen-identity and chosen-ciphertext attack (IND-ID-CCA) in the random oracle model (ROM). Performance analysis shows that the scheme achieves a better performance than similar schemes.
A Hybrid Scheme for an Interoperable Identity Federation System Based on Attribute Aggregation Method

Several countries have invested in building their identity management systems to equip citizens with infrastructures and tools to benefit from e-services. However, current systems still lack the interoperability requirement, which is the core issue that could lower the wide benefits of having an identity management system. In fact, in the existing systems, the user is allowed to choose only one partial identity from an identity provider (IdP) during a single session with a service provider (SP). However, in some scenarios, an SP needs to retrieve information about user’s identities managed by multiple IdPs. The potential method to tackle these shortcomings is attribute aggregation from multiple identity providers. A number of initiatives and projects on attribute aggregation have been explored. Nevertheless, these constructions do not fulfill some identity management requirements. This paper describes a new flexible model that aims to provide the necessary mechanisms to ensure attribute aggregation in order to meet the interoperability challenges of current identity management systems. The proposed scheme is a scalable solution, based on identity federation technologies, that introduces a new IdP called an account linking provider (ALP). The purpose of this ALP is to link together different accounts, holding end users’ attributes, whenever more than one source of data is needed to grant access to the requested web resource in a single session. Furthermore, the proposed identity federation system is based on a streamlined, cost-effective, and interoperable architecture, which makes this model suitable for large-scale identity federation environments.
Deconstructing multimodality: visual properties and visual context in human semantic processing

Multimodal semantic models that extend linguistic representations with additional perceptual input have proved successful in a range of natural language processing (NLP) tasks.Recent research has successfully used neural methods to automatically create visual representations for words.However, these works have extracted visual features from complete images, and have not examined how different kinds of visual information impact performance.In contrast, we construct multimodal models that differentiate between internal visual properties of the objects and their external visual context.We evaluate the models on the task of decoding brain activity associated with the meanings of nouns, demonstrating their advantage over those based on complete images.
DS at SemEval-2019 Task 9: From Suggestion Mining with neural networks to adversarial cross-domain classification

Suggestion Mining is the task of classifying sentences into suggestions or non-suggestions. SemEval-2019 Task 9 sets the task to mine suggestions from online texts. For each of the two subtasks, the classification has to be applied on a different domain. Subtask A addresses the domain of posts in suggestion online forums and comes with a set of training examples, that is used for supervised training. A combination of LSTM and CNN networks is constructed to create a model which uses BERT word embeddings as input features. For subtask B, the domain of hotel reviews is regarded. In contrast to subtask A, no labeled data for supervised training is provided, so that additional unlabeled data is taken to apply a cross-domain classification. This is done by using adversarial training of the three model parts label classifier, domain classifier and the shared feature representation. For subtask A, the developed model archives a F1-score of 0.7273, which is in the top ten of the leader board. The F1-score for subtask B is 0.8187 and is ranked in the top five of the submissions for that task.
A Survey on Security Challenges of Virtualization Technology in Cloud Computing

Virtualization has become a widely and attractive employed technology in cloud computing environments.Sharing of a single physical machine between multiple isolated virtual machines leading to a more optimized hardware usage, as well as make the migration and management of a virtual system more efficiently than its physical counterpart.Virtualization is a fundamental technology in a cloud environment.However, the presence of an additional abstraction layer among software and hardware causes new security issues.Security issues related to virtualization technology have become a significant concern for organizations due to arising some new security challenges.This paper aims to identify the main challenges and risks of virtualization in cloud computing environments.Furthermore, it focuses on some common virtual-related threats and attacks affect the security of cloud computing.The survey was conducted to obtain the views of the cloud stakeholders on virtualization vulnerabilities, threats, and approaches that can be used to overcome them.Finally, we propose recommendations for improving security, and mitigating risks encounter virtualization that necessary to adopt secure cloud computing.
AGBuilder: An AI Tool for Automated Attack Graph Building, Analysis, and Refinement

Attack graphs are widely used for modeling attack scenarios that exploit vulnerabilities in computer systems and networked infrastructures. Essentially, an attack graph illustrates a what-if analysis, thereby, helping the network administrator to plan for potential security threats. However, current attack graph representations not only suffer from scaling issues, but also are difficult to generate. Despite efforts from the research community there are no automated tools for generating attack graphs from textual descriptions of vulnerabilities such as those from the Common Vulnerabilities and Exposures (CVE) in the National Vulnerability Database (NVD). Additionally, there is little support for incremental updates and refinements to an attack graph model. This is needed to reflect changes to an attack graph that arise because of changes to the vulnerability state of the underlying system being modeled. In this work, we present an artificial intelligence (AI) based planning tool, AGBuilder – Attack Graph Builder, for automatically generating, updating and refining attack graphs. A key contribution of AGBuilder is that it uses textual descriptions of vulnerabilities to automatically generate attack graphs. Another significant contribution is that, using AGBuilder, we describe a methodology to incrementally update attack graphs when the system changes. This aspect has not been addressed in prior research and is a crucial step for achieving resiliency in the face of evolving adversarial strategies. Finally, AGBuilder has the ability to reuse smaller attack graphs, e.g., when building a network of networks, and join them together to create larger attack graphs.
TuEval at SemEval-2019 Task 5: LSTM Approach to Hate Speech Detection in English and Spanish

The detection of hate speech, especially in online platforms and forums, is quickly becoming a hot topic as anti-hate speech legislation begins to be applied to public discourse online.The HatEval shared task was created with this in mind; participants were expected to develop a model capable of determining whether or not input (in this case, Twitter posts in English and Spanish) could be considered hate speech (designated as Subtask A), if they were aggressive, and whether the tweet was targeting an individual, or speaking generally (Subtask B).We approached this Subtask by creating a LSTM model with an embedding layer.We found that our model performed considerably better on English language input when compared to Spanish language input.In English, we achieved an F1-Score of 0.466 for Subtask A and 0.462 for Subtask B; In Spanish, we achieved scores of 0.617 and 0.612 on Subtask A and Subtask B, respectively.
Eigenvector Crossover in the Efficient jSO Algorithm

In this paper, a new variant of an efficient adaptive jSO algorithm is presented. The original jSO uses popular binomial crossover which is applied in a standard coordinate system. Many problems tend to rotate the coordinate system in one or more axes. This is the reason why a crossover variant using Eigen coordinate system replaces the original binomial version of crossover in jSO. The newly proposed jSOe performs significantly better compared with the original jSO when solving 90 problems of the CEC 2017 benchmark set.&#x0D;
Finding Concurrency Exploits on Smart Contracts

Smart contracts have been widely used on Ethereum to enable business services across various application domains. However, they are prone to different forms of security attacks due to the dynamic and non-deterministic blockchain runtime environment. In this work, we highlighted a general miner-side type of exploit, called concurrency exploit, which attacks smart contracts via generating malicious transaction sequences. Moreover, we designed a systematic algorithm to automatically detect such exploits. In our preliminary evaluation, our approach managed to identify real vulnerabilities that cannot be detected by other tools in the literature.
Research Issues on Data Centric Security and Privacy Model for Intelligent Internet of Things based Healthcare

Recently, as the information technology (IT) environment rapidly changes to the cloud, big data and Internet of things (IoT) era, there are necessity to take much thorough security management not only for storing data but also for encryption, movement
UBC-NLP at SemEval-2019 Task 6: Ensemble Learning of Offensive Content With Enhanced Training Data

We examine learning offensive content on Twitter with limited, imbalanced data.For the purpose, we investigate the utility of using various data enhancement methods with a host of classical ensemble classifiers.Among the 75 participating teams in SemEval-2019 sub-task B, our system ranks 6th (with 0.706 macro F 1score).For sub-task C, among the 65 participating teams, our system ranks 9th (with 0.587 macro F 1 -score).
DBMS-KU at SemEval-2019 Task 9: Exploring Machine Learning Approaches in Classifying Text as Suggestion or Non-Suggestion

This paper describes the participation of DBMS-KU team in the SemEval 2019 Task 9, that is, suggestion mining from online reviews and forums.To deal with this task, we explore several machine learning approaches, i.e., Random Forest (RF), Logistic Regression (LR), Multinomial Naive Bayes (MNB), Linear Support Vector Classification (LSVC), Sublinear Support Vector Classification (SSVC), Convolutional Neural Network (CNN), and Variable Length Chromosome Genetic Algorithm-Naive Bayes (VLCGA-NB).Our system obtains reasonable results of F1-Score 0.47 and 0.37 on the evaluation data in Subtask A and Subtask B, respectively.In particular, our obtained results outperform the baseline in Subtask A. Interestingly, the results seem to show that our system could perform well in classifying Non-suggestion class.
TakeLab at SemEval-2019 Task 4: Hyperpartisan News Detection

In this paper, we demonstrate the system built to solve the SemEval-2019 task 4: Hyperpartisan News Detection (Kiesel et al., 2019), the task of automatically determining whether an article is heavily biased towards one side of the political spectrum.Our system receives an article in its raw, textual form, analyzes it, and predicts with moderate accuracy whether the article is hyperpartisan.The learning model used was primarily trained on a manually prelabeled dataset containing news articles.The system relies on the previously constructed SVM model, available in the Python Scikit-Learn library.We ranked 6th in the competition of 42 teams with an accuracy of 79.1% (the winning team had 82.2%).
Valoración del alumnado sobre el uso de clickers y vídeo tutoriales en la educación superior

The implementation of undergraduate and postgraduate degrees in Spanish universities has been accompanied by a review of the teaching methodologies used. These methodologies incorporating, in many cases, new Information and Communication Technologies (ICT). The ICT of Audience Response Systems (ARS), Clickers, and videos (mUVies) were used in a quantitative subject of a degree offered by the Universitat de València (UV), in an attempt to mitigate the low teaching load of the same and provide students with support material inside and outside the classroom. The work presented examines the students' perception of the use of these ICTs in their learning process and also the volume of work involved in its implementation. Using descriptive and inferential analyses of the opinions obtained in the ICT surveys and the results of the continuous assessment and the final exam, we conclude that the students valued the use of both ICTs very positively with average score higher than 8 (out of 10), rating better the VIDEOS-mUVies than Clickers. As for the volume of work that the implementation of ICTs, this is estimated at about 7.5 hours for students and about 132.5 hours for teachers.
bhanodaig at SemEval-2019 Task 6: Categorizing Offensive Language in social media

This paper describes the work that our team bhanodaig did at Indian Institute of Technology (ISM) towards OffensEval i.e. identifying and categorizing offensive language in social media.Out of three sub-tasks, we have participated in sub-task B: automatic categorization of offensive types.We perform the task of categorizing offensive language, whether the tweet is targeted insult or untargeted.We use Linear Support Vector Machine for classification.The official ranking metric is macroaveraged F1.Our system gets the score 0.5282 with accuracy 0.8792.However, as new entrant to the field, our scores are encouraging enough to work for better results in future.
Selection and Structuring of Training Multimedia Educational Materials for University Students: Practical Recommendations

The purpose of the article is to develop practical recommendations on the selection and structuring of educational material for a multimedia and training complex on occupational safety for future civil engineers.450 people participated in the experimental work: 50 teachers, 400 students of the federal state budgetary educational institution of higher education.A survey with open and close questions was used in the research.Later it was analyzed using the methodology of quantitative research.The significance of the results obtained is that the methods of selection of educational material ensure the formation of competences, skills of research activities, practice-oriented orientation and compliance with students' expectations.The identified criteria (knowledge, activity, competence) contribute to the integration of educational material; the formation of knowledge, skills, abilities, competences to ensure occupational safety.
ELECTRONIC SOCIAL NETWORKING SITES AS A UNIVERSAL TOOL FOR DEVELOPING FUTURE TEACHERS’ DIGITAL COMPETENCE

The aim of the research is to study the opportunities of using the electronic social networks for developing future teachers’ cognitive, creative, communicative and collaborative skills. The research was conducted at Donbas State Pedagogical University. 90 future primary school teachers and 40 university lecturers are involved in the research work that includes the use of social networks for developing cognitive, creative, communicative, and collaborative skills while studying the university courses. The research results are presented as the comparative analysis of the indicators of future teachers’ cognitive, creative, communicative, and collaborative skills which were obtained at the initial (2014) and final (2018) stages of the experimental programme. The author analyses the research results according to five blocks: students’ preference and motivation to use electronic social networking sites; the use of the electronic social networking sites for developing the cognitive skills; the use of the electronic social networking sites for developing creative skills; the use of the electronic social networking sites for developing collaborative skills; the use of the electronic social networking sites for developing communicative skills. The author claims that future teachers’ digital competence improves while taking part in individual and group learning; in personal learning; while using social networks for the purpose of informing about the university events, for conducting extracurricular activities, non-formal and informal education, project activities etc. It was proved that the conditions, which contribute to the successful implementation of social networking sites in the educational process of higher educational institution, include an active, conscious and cultural use of electronic social networking sites by students, as well as university lecturers’ understanding and concern about implementing these educational digital resources in their professional activities.