Reviewerly/exHarmony huggingface dataset:
Pre-Trained Language Model Transfer on Chinese Named Entity Recognition

The challenges of natural language processing (NLP) lie in the polysemy and insufficiency of human-labeled training data. Bidirectional Encoder Representations from Transformers (BERT) facilitates pre-training deep bidirectional representations on large-scale unannotated text on the web and has created state-of-the-art performance on various NLP tasks after simple fine-tuning. The representation and application of semantic knowledge are important steps in the entire process of NLP tasks. In this work, for the encoder of our model, we encode an input sequence into contextual representations using pre-trained language model and design a new model that combines neural network with BERT. Experimental results show that our method achieves new state-of-the-art.
Leveraging aspect phrase embeddings for cross-domain review rating prediction

Online review platforms are a popular way for users to post reviews by expressing their opinions towards a product or service, and they are valuable for other users and companies to find out the overall opinions of customers. These reviews tend to be accompanied by a rating, where the star rating has become the most common approach for users to give their feedback in a quantitative way, generally as a Likert scale of 1–5 stars. In other social media platforms like Facebook or Twitter, an automated review rating prediction system can be useful to determine the rating that a user would have given to the product or service. Existing work on review rating prediction focuses on specific domains, such as restaurants or hotels. This, however, ignores the fact that some review domains which are less frequently rated, such as dentists, lack sufficient data to build a reliable prediction model. In this paper, we experiment on 12 datasets pertaining to 12 different review domains of varying level of popularity to assess the performance of predictions across different domains. We introduce a model that leverages aspect phrase embeddings extracted from the reviews, which enables the development of both in-domain and cross-domain review rating prediction systems. Our experiments show that both of our review rating prediction systems outperform all other baselines. The cross-domain review rating prediction system is particularly significant for the least popular review domains, where leveraging training data from other domains leads to remarkable improvements in performance. The in-domain review rating prediction system is instead more suitable for popular review domains, provided that a model built from training data pertaining to the target domain is more suitable when this data is abundant.
Fusion Strategies for Learning User Embeddings with Neural Networks

Growing amounts of online user data motivate the need for automated processing techniques. In case of user ratings, one interesting option is to use neural networks for learning to predict ratings given an item and a user. While training for prediction, such an approach at the same time learns to map each user to a vector, a so-called user embedding. Such embeddings can for example be valuable for estimating user similarity. However, there are various ways how item and user information can be combined in neural networks, and it is unclear how the way of combining affects the resulting embeddings.In this paper, we run an experiment on movie ratings data, where we analyze the effect on embedding quality caused by several fusion strategies in neural networks. For evaluating embedding quality, we propose a novel measure, Pair-Distance Correlation, which quantifies the condition that similar users should have similar embedding vectors. We find that the fusion strategy affects results in terms of both prediction performance and embedding quality. Surprisingly, we find that prediction performance not necessarily reflects embedding quality. This suggests that if embeddings are of interest, the common tendency to select models based on their prediction ability should be reconsidered.
An Adaptive Intelligent Water Drops Algorithm for Set Covering Problem

Today, natural resources are more scarce than ever, so we must make good use of them. To achieve this goal, we can use metaheuristic optimization tools as an alternative to achieve good results in a reasonable amount of time. The present work focuses on the use of adaptive techniques to facilitate the use of this type of tool to obtain good functional parameters. We use a constructive metaheuristic algorithm called Intelligent Water Drops to solve the set covering problem. To demonstrate the efficiency of the proposed method, the obtained results were compared with the standard version using the same initial configuration for both algorithms. Additionally, the Kolmogorov-Smirnov-Lilliefors, Wilcoxon signed-rank and Violin chart tests were applied to statistically validate the results, which showed that metaheuristics with autonomous search have a better behavior than do standard algorithms.
Rumors on the Net: A Brackish Suspension of Speech and Hate

The legitimacy of public memory and socially normative standards of civility is questioned through rumors that abound on online social media platforms. On the Net, the proclivity of rumors is particularly prone to acts of bullying and frameworks of hate speech. Legislative attempts to limit rumors operate differently in France and throughout Europe from the United States. This article examines the impact of online rumors, the mob mentality, and the politicization of bullying critics within a cyber culture that operates within the limitations of law.
Enhanced artificial bee colony with novel search strategy and dynamic parameter

There is only one guiding solution in the search equation of Gaussian bare-bones artificial bee colony algorithm (ABC-BB), which is easy to result in the problem of premature convergence and trapping into the local minimum. In order to enhance the capability of escaping from local minimum without loss of the exploitation ability of ABC-BB, a new triangle search strategy is proposed. The candidate solution is generated among the triangle area formed by current solution, global best solution and any randomly selected elite solution to avoid the premature convergence problem. Moreover, the probability of crossover is controlled dynamically according to the successful search experience, which can enable ABC-BB to adapt all kinds of optimization problems with different landscapes. The experimental results on a set of 23 benchmark functions and two classic real-world engineering optimization problems show that the proposed algorithm is significantly better than ABC-BB as well as several recently-developed state-of-the-art evolution algorithms.
Cultural obstacles to research data management and sharing at TU Delft

Research data management (RDM) is increasingly important in scholarship. Many researchers are, however, unaware of the benefits of good RDM and unsure about the practical steps they can take to improve their RDM practices. Delft University of Technology (TU Delft) addresses this cultural barrier by appointing Data Stewards at every faculty. By providing expert advice and increasing awareness, the Data Stewardship project focuses on incremental improvements in current data and software management and sharing practices. This cultural change is accelerated by the Data Champions who share best practices in data management with their peers. The Data Stewards and Data Champions build a community that allows a discipline-specific approach to RDM. Nevertheless, cultural change also requires appropriate rewards and incentives. While local initiatives are important, and we discuss several examples in this paper, systemic changes to the academic rewards system are needed. This will require collaborative efforts of a broad coalition of stakeholders and we will mention several such initiatives. This article demonstrates that community building is essential in changing the code and data management culture at TU Delft.
SNA Using User Experience

The paper proposes a comparison methodology for the social networks analysis, aiming at the user views by employing a cone model. It is done by incorporating the user experience, by means of the fuzzy K-means algorithm. The proposed work has been applied to the data obtained through Google forms in which the users are asked to rate their experience on the different parameters of the social network in consideration.
Self-Tuning Possibilistic <i>c</i>-Means Clustering Models

The relaxation of the probabilistic constraint of the fuzzy c-means clustering model was proposed to provide robust algorithms that are insensitive to strong noise and outlier data. These goals were achieved by the possibilistic c-means (PCM) algorithm, but these advantages came together with a sensitivity to cluster prototype initialization. According to the original recommendations, the probabilistic fuzzy c-means (FCM) algorithm should be applied to establish the cluster initialization and possibilistic penalty terms for PCM. However, when FCM fails to provide valid cluster prototypes due to the presence of noise, PCM has no chance to recover and produce a fine partition. This paper proposes a two-stage c-means clustering algorithm to tackle with most problems enumerated above. In the first stage called initialization, FCM with two modifications is performed: (1) extra cluster added for noisy data; (2) extra variable and constraint added to handle clusters of various diameters. In the second stage, a modified PCM algorithm is carried out, which also contains the cluster width tuning mechanism based on which it adaptively updates the possibilistic penalty terms. The proposed algorithm has less parameters than PCM when the number of clusters is [Formula: see text]. Numerical evaluation involving synthetic and standard test data sets proved the advantages of the proposed clustering model.
A simplified approach for dynamic security risk management in connected and autonomous vehicles

Connected and autonomous vehicles (CAVs) have the potential to offer safer and more efficient transportation. However, such vehicles operate in complex heterogeneous environments and it is therefore essential to control the dynamic risks that the CAVs face during operation. Given that CAVs can be seriously impacted by cyber-attacks, their security issues have been investigated widely. However, existing approaches fail to adequately consider the dynamicity of the risks that arise and present methods to capture the changes in risks and adaptive mitigations. To bridge these gaps, this paper proposes a systematic approach, which comprises of three modules: a knowledge-based system to support the identification of the critical threats, a monitoring module to detect the changes in security context of the CAV and its surrounding environments, and a simplified assessment module to capture the dynamic risks and adjust the mitigations as needed. We investigate a case study of CAV platooning to evaluate our proposal.
Identification of Practices for Proper Implementation of Requirements in Global Software Development: A Systematic Literature Review Protocol

Effective requirement implementation leads to successful delivery of software.The requirement engineering (RE) is very difficult when implemented locally but the case is worst in global software development (GSD).There exist challenges such as 'Lack of effective communication', 'Organizational change', 'Lack of coordination and collaboration', 'Lack of knowledge-sharing and awareness' in GSD and to minimize the effect of these challenges success factors such as 'Support of collaborative tools', 'Global project management', 'Requirement Engineering modelling', and 'Proper negotiation and discussion' are suggested by many authors.To decrease the effect of challenges and to successfully implement success factors, practices and solutions are needed.The objective of this research is to find the practices needed to successfully implement RE process.Systematic Literature Review (SLR) is conducted for the identification of these practices
The influence of visual experience and cognitive goals on the spatial representations of nociceptive stimuli

Abstract Localizing pain is crucial because it allows for detecting which part of the body is being hurt and identifying in its surrounding which stimulus is producing the damage. Nociceptive inputs should therefore be mapped according to somatotopic (“which limb is stimulated?”) and spatiotopic representations (“where is the stimulated limb?”). Because the body posture constantly changes, the brain has to realign the different spatial representations, for instance when the arms are crossed with the left hand in the right space and vice versa, to adequately guide actions towards the threatening object. Such ability is thought to be dependent on past sensory experience and contextual factors. We compared performances of early blind and normally sighted participants during temporal order judgement tasks. Two nociceptive stimuli were applied, one on each hand, with the hands either uncrossed or crossed. Participants reported which stimulus they perceived as first presented, according to either its location on the body or the position of the stimulated hand, respectively, prioritizing anatomy or external space as task-relevant reference frame. Relative to the uncrossed posture, sighted participants' performances were decreased when the hands were crossed, whatever the instruction be. Early blind participants' performances were affected by crossing the hands during spatial instruction, but not during anatomical instruction. These results indicate that nociceptive stimuli are automatically coded according to both somatotopic and spatiotopic representations, but the integration of the different spatial reference frames depends on early visual experience and ongoing cognitive goals, illustrating the plasticity and the flexibility of the nociceptive system.
Soft Computing Techniques for Noise Filtration in the Image Recognition Processes

In this paper, methods for noise filtration in the image recognition processes are considered. The following noise filtration methods were analyzed: arithmetic averaging filter, geometric averaging filter, median filtering, adaptive median filtration, Gaussian filtration and filtration using soft computing techniques, in particular the fuzzy color preserving Gaussian noise reduction method (FCG filter). Besides, the different types of noise that may occur on a digital image are discussed. All methods were evaluated using metrics like mean squared error (MSE), peak signal-to-noise ratio (PSNR) and structure similarity of images (SSIM). It has been found that all of the above methods can well filter out only a certain type of noise. In this paper, a combination of adaptive median filtering and FGG filter is proposed for removal of combined pulse and Gaussian noises.
Indian Sign Language Spelling Finger Recognition System

ISL (Indian Sign Language) is found to be a very widely used way of communication in different countries. Many sign language recognition systems have been proposed. In the proposed study, ISL fingerspelling recognition system is constructed. The dataset is the images of signs in ISL. Images are preprocessed and later, Canny Edge Detection algorithm is applied over the preprocessed images. SURF feature extraction is done to obtain Image descriptors for each image in the dataset. Later K-Means Clustering is done to obtain BoW of the extracted features. Finally, different Machine Learning Algorithms such as SVM, Naïve Bayes, MLP, KNN, and Random Forest Classifiers are used for matching the image. Classification which provides the maximum accuracy is suggested at the end.
Leverage side information for top‐N recommendation with latent Gaussian process

Summary Currently, Recommender Systems (RS) have been ubiquitously applied to various online applications and obtained tremendous success due to their capability to overcome information overload; however, the available Side Information (SI), such as demographics and attributions of items, is always neglected. Actually, SI could reflect user's interests and preference, and even influence user's decision over items; therefore, it would be greatly helpful to leverage side information to improve the performance of RS, which is already partly certificated in previous research studies. Motivated by this, in this article, a novel multi‐task learning‐based recommendation system referred to as LGP‐RS is proposed, which takes utility functions mapping from the non‐linear side information to low‐rank feature space with Gaussian process (GP) priors, for users and items, respectively. In addition, Laplace approximation method is employed to approximate the posterior distribution for the utility functions, and gradient‐based method is employed to learn the hyperparameters for GP. This flexible approach is able to capture the non‐linear high‐order interactions within side information, reduce the prediction uncertainty, and provide personalized top‐N recommendation. Experimental analysis over three real‐world datasets demonstrates that LGP‐RS could deal with both explicit and implicit side information simultaneously, and significantly outperforms state‐of‐the‐art approaches.
Persuasive Mobile Game Mechanics For User Retention

Retention is an important success factor for mobile social games. However, in game design, very little literature exists on the impact of game elements and their persuasive effects on user retention. We scraped ten high grossing mobile games and play tested them over the course of two weeks to gather persuasive game mechanics. After categorization and filtering of the persuasive mechanics, we simultaneously relate them to base game mechanics and a corresponding psychological theory from behavioral economics and psychology. Our results help increase the accessibility of using persuasive mechanics for future game developers.
Use of Koha-integrated library system by higher education institutions in Malawi

Purpose The purpose of this paper is to study the use of Koha integrated library system by higher education institutions in Malawi. Design/methodology/approach The study made use of the diffusion of innovation theory as an anchoring model. Data were collected using a survey questionnaire that was administered to library managers of higher education institutions registered with the National Council for Higher Education in Malawi. The data collected were analyzed using SPPS version 21 to generate descriptive statistics. Findings The findings revealed that 84.2 per cent of libraries in higher education institutions in Malawi are automated, and 81.3 per cent of them are using Koha ILS and the rest were using proprietary and other open source software. Cost and technical aspects have been discovered to be the primary factors that have assisted in the rapid diffusion of Koha. The study also exposed some factors that negatively impacted the use of Koha in the adopting institutions. The main ones included lack of information and communication technology infrastructure, unreliable Internet connectivity and limited finances. Originality/value Previously, some small-scale case studies have been performed to examine implementation of Koha in some higher education institutions in Malawi. However, this is the first large-scale attempt to investigate use of Koha among higher education institutions in the country. Findings that have shown that Koha can be installed easily and used at minimal cost could assist in its rapid adoption in the higher education institutions, a majority of whom face a myriad of financial challenges.
Adversarial Attacks on Continuous Authentication Security: A Dynamic Game Approach

Identity theft through phishing and session hijacking attacks has become a major attack vector in recent years, and is expected to become more frequent due to the pervasive use of mobile devices. Continuous authentication based on the characterization of user behavior, both in terms of user interaction patterns and usage patterns, is emerging as an effective solution for mitigating identity theft, and could become an important component of defense-in-depth strategies in cyber-physical systems as well. In this paper, the interaction between an attacker and an operator using continuous authentication is modeled as a stochastic game. In the model, the attacker observes and learns the behavioral patterns of an authorized user whom it aims at impersonating, whereas the operator designs the security measures to detect suspicious behavior and to prevent unauthorized access while minimizing the monitoring expenses. It is shown that the optimal attacker strategy exhibits a threshold structure, and consists of observing the user behavior to collect information at the beginning, and then attacking (rather than observing) after gathering enough data. From the operator’s side, the optimal design of the security measures is provided. Numerical results are used to illustrate the intrinsic trade-off between monitoring cost and security risk, and show that continuous authentication can be effective in minimizing security risk.
Hidden Markov Models for Sentiment Analysis in Social Media

User-generated content in social media contain very meaningful information about public sentiment and stance and require automated methods to analyze them extract knowledge from them. The recognition of sentiments, emotions and attitudes in textual data is necessary for understanding public stance and is highly desired for various services and procedures. In this work, we examine the performance of Hidden Markov Models (HMM) in the recognition of sentiments and opinions in text. Hidden Markov Models constitute a quite suitable approach given the special characteristics of the textual data. In particular, they can utilize the sequential nature of textual data, a piece of information that traditional machine learning approaches fail to fully take into account. We performed several experiments in order to assess the performance of different HMM-based methods under various training parameters and architectures. The evaluation results indicate that Hidden Markov Models achieve superior performance compared to traditional machine learning algorithms and highlight that they are scalable and accurate in analyzing user generated content and in specifying opinions and attitudes.
Cyber Security Supervision in the Insurance Sector: Smart Contracts and Chosen Issues

There are still many open issues and questions concerning the supervision and the legal and regulatory assessment of cyber security issues in the insurance sector, especially regarding smart insurance contracts and similar issues. In the present case the focus shall be on the underlying legal framework in the European Union and in Switzerland, including the most relevant ordinances and circulars as well as public and private guidelines, followed by an outlook and some general ideas how Brexit could potentially have an impact on the general recognition and acceptation of smart contracts within the legal and regulatory framework and the society in general.
Neural Cross-Lingual Transfer and Limited Annotated Data for Named Entity Recognition in Danish.

Named Entity Recognition (NER) has greatly advanced by the introduction of deep neural architectures. However, the success of these methods depends on large amounts of training data. The scarcity of publicly-available human-labeled datasets has resulted in limited evaluation of existing NER systems, as is the case for Danish. This paper studies the effectiveness of cross-lingual transfer for Danish, evaluates its complementarity to limited gold data, and sheds light on performance of Danish NER.