Reviewerly/exHarmony huggingface dataset:
Addressing Syntax-Based Semantic Complementation: Incorporating Entity and Soft Dependency Constraints into Metonymy Resolution

State-of-the-art methods for metonymy resolution (MR) consider the sentential context by modeling the entire sentence. However, entity representation, or syntactic structure that are informative may be beneficial for identifying metonymy. Other approaches only using deep neural network fail to capture such information. To leverage both entity and syntax constraints, this paper proposes a robust model EBAGCN for metonymy resolution. First, this work extracts syntactic dependency relations under the guidance of syntactic knowledge. Then the work constructs a neural network to incorporate both entity representation and syntactic structure into better resolution representations. In this way, the proposed model alleviates the impact of noisy information from entire sentences and breaks the limit of performance on the complicated texts. Experiments on the SemEval and ReLocaR dataset show that the proposed model significantly outperforms the state-of-the-art method BERT by more than 4%. Ablation tests demonstrate that leveraging these two types of constraints benefits fine pre-trained language models in the MR task.
Prediction of production facility priorities using Back Propagation Neural Network for bus body building industries: a post pandemic research article

The pandemic recession has caused enormous disturbances in many industrialized countries. The massive disruption of the supply chain of production is affecting manufacturing companies operating in and around India. Particularly the medium-sized bus body building works have been reduced, due to its compound anomalies. The integrated view of the production facility priorities is not an easy task. Since it is difficult for available labour to conduct an entire project, the completion of a production process is delayed. But still, the dilemma remains as to how production managers can correctly interpret the priorities of the facility. Indeed, this is a problem missing from the previous study. Fortunately, in the current competitive environment, it is essentially needed. This study has been used Back Propagation Neural Network (BPNN) approach for predicting production facility priorities. The experimental results confirm the suitability of the model for predicting priorities. A real-world problem is taken into account in making use of the model output. In this sense, this total solution facilitates production managers in assessing and enhancing the production facilities. The findings emphasize the priority of "equipment effectiveness, labour scheduling and communication" in order to strengthen the post-pandemic production facility.
Location-privacy preserving partial nearby friends querying in urban areas

This work studies the location-privacy preserving proximity querying in the context of proximity-based services (PBSs), a special kind of location-based services (LBSs). The users register with the trusted PBS provider and specify their own personalized location privacy profile to be enforced against the curious friends (other registered users). Due to the urban area constraint, the user mobility is only on the city road network which is modeled as a weighted directed graph. The users share their own precise locations with the PBS provider and also query the nearby friends, the metric of which is defined on the shortest path on the graph. The proposed location privacy model ensures the location anonymity of the friends on the graph. To this end, two anonymity models, called weak location k-anonymity and strong location k-anonymity, are introduced to protect against the identified consecutive attack scenarios. The attack scenarios model the belief of the attacker (the query issuer) on the whereabouts of the friends. The PBS provider simulates the belief of each attacker on every users’ whereabouts and suppresses some friends from the query result to ensure the location anonymity of each and every user at all times. Effective and efficient algorithms, needing no cryptographic protocols, have been developed to provide weak/strong location k-anonymity. An extensive experimental evaluation, mainly addressing the issues of privacy/utility tradeoff and runtime efficiency, on two real graphs with a simulated mobility is presented.
The Influence of Data Storytelling on the Ability to Recall Information

With ever-increasing amounts of complex data, we need compelling ways to distill this information into meaningful, memorable and engaging insights. Data storytelling is an emerging visualization paradigm that aims to "tell a story" with data in order to elicit deeper reflections in an effective manner. However, the effects of adding a narrative to a visualization on the memorability of the information remain speculative. Based on a review of related work, we synthesize a framework of data storytelling principles with concrete actions for every principle. We use this framework to design an online, controlled experiment to test compare traditional data visualizations with data storytelling visualizations in terms of their effects on short-term and long-term recall of information displayed in the visualizations. In general, despite long-held assumptions in the visualization community, we find no significant differences in recall between traditional visualizations and data storytelling visualization. However, we find indications that the cognitive load induced by different chart types and self-assessed prior knowledge on the chart topics could possibly have a moderating effect on information recall.
Learning Analytics and Educational Data Mining

In recent years, the use of analytics and data mining – methodologies that extract useful information from large datasets – has become commonplace in science and business. When these methods are used in education, they are referred to as learning analytics (LA) and educational data mining (EDM). For example, adaptive learning platforms – those that respond uniquely to each learner – require learning analytics to model the learner's current state of knowledge. The researcher can conduct second-by-second analyses of phenomena that occur over long periods of time or in an individual learning session. Large datasets are required for these analyses. In most cases, the data are gathered automatically – such as keystrokes, eye movement, or assessments – and are analyzed using algorithms based in learning sciences research. This chapter reviews prediction methods, structure discovery, relationship mining, and discovery with models.
A Hybrid Spam Detection Framework for Social Networks

The widespread use of social networks has caused these platforms to become the target of malicious people. Although social networks have their own spam detection systems, these systems sometimes may not prevent spams in their social networks. Spam contents and messages threaten the security and performance of users of these networks. A spam account detection framework based on three components is proposed in this study. Short link analysis, machine learning and text analysis are the components used together in the proposed framework. First, a dataset was created for this purpose and the attributes of spam accounts were determined. Later, the hyperlinks in the messages in this dataset were analyzed through link analysis component. The machine learning component was modelled through attributes. Moreover, the messages of the social network users were analyzed through text analysis method. A web-based application of the proposed model was put into practice. As a result of the experimental studies carried out thanks to the framework, it was determined that the proposed framework showed a performance of 95.69 %. The success of this article was calculated according to the F-measure and precision evaluation metrics under the influence of sensitive content rate. It is aimed to detect spam accounts on social network and the spam detection policy of these networks is intended to support.
Homography‐based uncalibrated visual servoing with neural‐network‐assisted robust filtering scheme and adaptive servo gain

Abstract In this paper, a homography‐based uncalibrated visual servo system with neural‐network‐assisted robust filtering scheme and adaptive servo gain is presented. This system employs a homography‐based task function which is robust to image defects. A neural‐network‐assisted robust filtering method which combines the new form of smooth variable structure filter (SVSF) with a radial basis function (RBF) neural network is proposed to estimate the total Jacobian between task function and robot joints. The RBF neural network in this filtering method plays the role as a corrector to further improve the accuracy and compensate the interference caused by the measurement errors of image features. The controller that directly controls the robot joints based on the estimated total Jacobian is designed for achieving the robustness to robot parameters errors. By adopting this filtering scheme, the visual servo system shows better accuracy and convincing anti‐interference ability. In addition, a novel Q‐learning strategy is introduced for this homography‐based system to make adaptive adjustment for the servo gain. This adaptive gain enables the system to achieve a faster convergence speed while ensuring the accuracy. Several simulations and experiments have been carried out to verify the performance of the proposed system.
A New Robust Molding of Heat and Mass Transfer Process in MHD Based on Adaptive-Network-Based Fuzzy Inference System

This study concerns with the Process intensification deal with the complex fluids in mixing processes of many industries and its performance is based on the flow of fluid, magnetohydrodynamic (MHD) heat and mass transfer. This paper proposes a dynamic control model based on adaptive-network-based fuzzy inference system (ANFIS), weighted logistic regression and robust relevance vector machine (RRVM). Suitable similarity variables are applied to convert the flow equations into higher order ordinary differential equations and solved numerically. The surface-contour plots are utilized to visualize the influence of active parameters on velocity, thermal, nanoparticles concentration and motile microorganism’s density. The hybrid-learning algorithm comprised of gradient descent and least-squares method is employed for training the ANFIS. A novel RRVM is presented to predict the endpoint. RRVM solves the problem of sensitivity to outlier characteristic of classical relevance vector machine (RVM), thus obtaining higher prediction accuracy. The key idea of the proposed RRVM is to introduce individual noise variance coefficient to each training sample. In the process of training, the noise variance coefficients of outliers gradually decrease so as to reduce the impact of outliers and improve the robustness of the model. To compare the proposed RRVM and other methods with outliers, the Monte Carlo simulation study has been performed. The simulation results showed that, based on mean squared error (MSE), mean absolute error (MAE), root mean squared error (RMSE) and coefficient of determination (R^2) criteria, the proposed RRVM give better performance than other methods when the data contain outliers. While when the dataset does not contain outliers, the results showed that the classical RVM is more efficient than other methods.
Comparative Evaluation of Genetic Operators in Cartesian Genetic Programming

Cartesian Genetic Programming (CGP) is a graph-based genetic programming technique developed by Miller & Thompson in 2000 which has several advantages over the traditional form of tree-based Genetic programming (GP). In the standard form, CGP uses only mutation as a genetic operator whereas the use of traditional crossover operators of GP when applied to CGP led to disrupted results. Various researchers have come out with new genetic operators which when applied to standard CGP or its variants have given better results either in the form of better convergence or efficiency. In this paper, we performed a comparative evaluation of four genetic operators, namely standard crossover, Clegg’s crossover, Graph-based crossover, and forking operator, on standard CGP and tested them on standard benchmark datasets such as Koza-2, Koza-3, and Nguyen-2. Our evaluation shows that both Clegg’s crossover and forking operator performs better than the others.
Smart-Bot Assistant for College Information System

A chatbot is a software application that facilitates online conversation through text or speech. Our proposed chatbot for college enquiry is a simple web application that aims to provide information regarding college. This chatbot provides information like admission procedure, courses offered, fee structure, placement statistics, and contact details of the college. This proposed chatbot uses natural language processing libraries to understand customer questions and automate responses to them, simulating human conversation and AIML (Artificial Intelligence Markup Language) to write rules which are used by chatbot response systems.
Semantic Constraints Matrix Factorization Hashing for cross-modal retrieval

Cross-modal hashing methods have attracted considerable attention due to their low memory usage and high query speed in large-scale cross-modal retrieval. During the encoding process, there still remains two crucial bottlenecks: how to equip hash codes with cross-modal semantic information, and how to rapidly obtain hash codes. In this paper, we propose Semantic Constraints Matrix Factorization Hashing (SCMFH) which simultaneously considers modality-specific and cross-modal semantic information for hash codes with closed solution. Specifically, the original representation of each modality is factorized into modality-specific semantic representations, and then the cross-modal semantic similarity matrices is used to constrain correlation of modality-specific representation. Finally, all the modality-specific representations are regressed to the common hash codes, and the fast hash codes with a closed solution is obtained. Extensive experimental have been carried out on public datasets. The results show that the method outperforms many current cross-modal hashing methods in terms of mean average precision (mAP), up to 2.7%.
The Impact of the Practical Security Test during the Software Development Lifecycle

With the advent of the 5G era, due to the innovation of 5G architecture, open source is widely used under the software-defined everything architecture, software security is even more important. According to National Institute of Standards and Technology (NIST) Special Publication (SP) 800- 64 Vol2 (Security Considerations in the System Development Life Cycle; SSDLC), the key security roles and responsibilities that are needed in development of most information systems. Sufficient information about the SDLC will improve the development on the secure software. On this study, how to ensure software security from the initial requirement to the final release, and even the issues of operation and disposal will be explored. We deploy different test methods in different phases of SDLC, including Software Composition Analysis (SCA), Interactive Application Security Testing (IAST), Static Application Security Testing (SAST), and Dynamic Application Security Testing (DAST), etc. From the experiment, the initial security requirements are improved by detecting the problems, and then the security of the software is improved.
Rapport building by Chinese celebrities on Weibo and Facebook

Abstract The paper examines how Chinese celebrities build rapport with their followers on social media, with a comparison between the Chinese mainland (on Weibo) and Hong Kong (on Facebook) celebrities. Rapport building is conceptualized as language use in promoting social bonding and emotional involvement and as achieved via at least two aspects: use of relational acts and choice of interactional features. Twelve months of postings by twelve most-followed Weibo and Facebook celebrities from the Chinese mainland and Hong Kong in 2017 have been retrieved and analyzed. It is found that the prevalent relational acts commonly used by both Weibo and Facebook celebrities to build rapport with their followers include sharing information, retweeting information and directives, among others; and the commonly prevalent interactive features include the use of colloquialism, emoji &amp; emoticons, and hashtags or @, and the like. Nonetheless, significant differences also exist in that while Weibo celebrities tend to use more acts of showing stance, Facebook celebrities use more acts of showing appreciation; Weibo celebrities tend to use more colloquialism, emoji &amp; emoticons, and internet slang, Facebook celebrities tend to use more English codemixing and vernacular expressions. Finally, the paper concludes with a discussion and explanation of the commonalities and differences between Weibo and Facebook celebrities.
Smart Communication and Security by Key Distribution in Multicast Environment

The service providers are aiming to provide multicast applications, primarily in the area of content delivery and secure wireless networks, due to the increased adoption of network systems and demand for secured wireless networks communication. Cryptography enables the users to send information across insecure networks using data encryption and decryption with key management. The research paper proposes a unique way of safeguarding network systems using cryptographic keys, as well as a fuzzy-based technique for improving security by reducing symmetric and asymmetric key overhead. To enable efficient communication, fuzzy-based rules with security triads and cryptographic key management methods are used. When the key distribution is decentralized, security implementation becomes more difficult, and multiple types of attacks are possible. Fuzzy logic-based key management methods are used in addition to offering a novel technique for secure cryptography systems. The novelty of the work is that the simulation work is also carried out to verify the data in on-demand distance vector (AODV) multicast wireless routing that supports 100 nodes with network performance parameters such as delay, control overhead, throughput, and packet delivery ratio. The system supports up to 128-bit key embedded with 128-bit plain data in cryptographic encryption and decryption.
Keyphrase Extraction for Technical Language Processing

Keyphrase extraction is an important facet of annotation tools that offer the provision of the metadata necessary for technical language processing (TLP). Because TLP imposes additional requirements on typical natural language processing (NLP) methods, we examined TLP keyphrase extraction through the lens of a hypothetical toolkit which consists of a combination of text features and classifiers suitable for use in low-resource TLP applications. We compared two approaches for keyphrase extraction: The first which applied our toolkit-based methods that used only distributional features of words and phrases, and the second was the Maui automatic topic indexer, a well-known academic method. Performance was measured against two collections of technical literature: 1153 articles from Journal of Chemical Thermodynamics (JCT) curated by the National Institute of Standards and Technology Thermodynamics Research Center (TRC) and 244 articles from Task 5 of the Workshop on Semantic Evaluation (SemEval). Both collections have author-provided keyphrases available; the SemEval articles also have reader-provided keyphrases. Our findings indicate that our toolkit approach was competitive with Maui when author-provided keyphrases were first removed from the text. For the TRC-JCT articles, the Maui automatic topic indexer reported an F -measure of 29.4 % while our toolkit approach obtained an F -measure of 28.2 %. For the SemEval articles, our toolkit approach using a Naïve Bayes classifier resulted in an F -measure of 20.8 %, which outperformed Maui’s F -measure of 18.8 %.
Verifying Whiley Programs with Boogie

Abstract The quest to develop increasingly sophisticated verification systems continues unabated. Tools such as Dafny, Spec#, ESC/Java, SPARK Ada and Whiley attempt to seamlessly integrate specification and verification into a programming language, in a similar way to type checking. A common integration approach is to generate verification conditions that are handed off to an automated theorem prover. This provides a nice separation of concerns and allows different theorem provers to be used interchangeably. However, generating verification conditions is still a difficult undertaking and the use of more “high-level” intermediate verification languages has become commonplace. In particular, Boogie provides a widely used and understood intermediate verification language. A common difficulty is the potential for an impedance mismatch between the source language and the intermediate verification language. In this paper, we explore the use of Boogie as an intermediate verification language for verifying programs in Whiley. This is noteworthy because the Whiley language has (amongst other things) a rich type system with considerable potential for an impedance mismatch. We provide a comprehensive account of translating Whiley to Boogie which demonstrates that it is possible to model most aspects of the Whiley language. Key challenges posed by the Whiley language included: the encoding of Whiley’s expressive type system and support for flow typing and generics; the implicit assumption that expressions in specifications are well defined; the ability to invoke methods from within expressions; the ability to return multiple values from a function or method; the presence of unrestricted lambda functions; and the limited syntax for framing. We demonstrate that the resulting verification tool can verify significantly more programs than the native Whiley verifier which was custom-built for Whiley verification. Furthermore, our work provides evidence that Boogie is (for the most part) sufficiently general to act as an intermediate language for a wide range of source languages.
Interactive Media in Public Spaces:

When designing public interactive environments, new advances in computing and data collection techniques from the users can enhance the public’s engagement and interaction with the designed space. Consequently, ethical questions arise as the ambiguity surrounding user information extraction and analysis may lead to privacy issues and biases. This paper examines topics of surveillance, privacy, and bias through two interactive media projects exhibited in public spaces. In particular, this paper focuses on analyzing the curation and manipulation of a database of an AI-powered and computer vision-based interactive installation that uses advances in computing such as DeepLearning and Natural Language Processing but runs into privacy and gender bias issues. This paper aims to showcase how decision-making, data curation, and algorithmic processes can directly impact and reinforce surveillance, privacy, and bias in public spaces.