Reviewerly/exHarmony huggingface dataset:
Project Manager Leadership Behavior: Task-Oriented versus Relationship-Oriented

In this paper, we examined managers' leadership behavior when working on a simulated team project regarding task-oriented versus relationship-oriented leadership behavior to effectively achieve successful project completion.Managers attending an advanced project management development program responded to the Fielder Leadership Behavior Style Self-Assessment, which is a useful framework to determine task-oriented versus relationship-oriented leadership behavioral styles.The degree of task-oriented versus relationshiporiented leadership behavior styles was assessed to determine the approach taken by the managers for achieving successful project completion.A Pearson's chi-square test was conducted to determine whether the observed values were significantly different from an expected value of five.The findings can contribute to better understanding the leadership styles, which characterize project management accomplishment.stakeholders' expectations.To accomplish project completion success, project managers must have a good understanding of how to apply the tools and techniques of quality project management (Anderson, 1992;PmBOK, 2017).However, project management is more than applying the technical skills of doing the project work by using specific knowledge, methods, and techniques, such as planning, scheduling, budgeting, and computing, and working with numbers, templates, charts, and graphs.Crucially, project managers, project managers must lead the project team members, interact efficiently, and influence other stakeholders (Hardy-Vallee, 2012;Kumar, 2009).
The Future of Data-driven Personas: A Marriage of Online Analytics Numbers and Human Attributes

The massive volume of online analytics data about customers has led to novel opportunities for user segmentation.However, getting real value from data remains challenging for many organizations.One of the recent innovations in online analytics is data-driven persona generation that can be used to create high-quality human representations from online analytics data.This manuscript (a) summarizes the potential of data-driven persona generation for online analytics, (b) characterizes nine open research questions for data-driven persona generation, and (c) outlines a research agenda for making persona analytics more useful for decision makers.
Margin-Based Pareto Ensemble Pruning: An Ensemble Pruning Algorithm That Learns to Search Optimized Ensembles

The ensemble pruning system is an effective machine learning framework that combines several learners as experts to classify a test set. Generally, ensemble pruning systems aim to define a region of competence based on the validation set to select the most competent ensembles from the ensemble pool with respect to the test set. However, the size of the ensemble pool is usually fixed, and the performance of an ensemble pool heavily depends on the definition of the region of competence. In this paper, a dynamic pruning framework called margin-based Pareto ensemble pruning is proposed for ensemble pruning systems. The framework explores the optimized ensemble pool size during the overproduction stage and finetunes the experts during the pruning stage. The Pareto optimization algorithm is used to explore the size of the overproduction ensemble pool that can result in better performance. Considering the information entropy of the learners in the indecision region, the marginal criterion for each learner in the ensemble pool is calculated using margin criterion pruning, which prunes the experts with respect to the test set. The effectiveness of the proposed method for classification tasks is assessed using datasets. The results show that margin-based Pareto ensemble pruning can achieve smaller ensemble sizes and better classification performance in most datasets when compared with state-of-the-art models.
Unsupervised collective-based framework for dynamic retraining of supervised real-time spam tweets detection model

Twitter is one of the most popular social platforms. It has changed the way of communication and information dissemination through its real-time messaging mechanism. Recently, it has been used by researchers and industries as a new source of data for various intelligent systems, such as tweet sentiment analysis and recommendation systems, which require high data quality. However, due to its flexibility and popularity, Twitter has become the main target for spamming activities such as phishing legitimate users or spreading malicious software, which introduces new security issues and waste resources. Therefore, researchers have developed various machine-learning algorithms to reveal Twitter spam. However, as spammers have become smarter and more crafty, the characteristics of the spam tweets are varying over time making these methods inefficient to detect new spammers tricks and strategies. In addition, some of the employed methods (e.g. blacklisting) or spammer features (e.g. graph-based features) are extremely time-consuming, which hinders the ability to detect spammer activities in real-time. In this paper, we introduce a framework to deal with the volatility of the spam contents and new spamming patterns, called the spam drift. The framework combines the strength of unsupervised machine learning approach, which learns from unlabeled tweets, to retrain a real-time supervised tweet-level spam detection model in a batch mode. A set of experiments on a large-scale data set show the effectiveness of the proposed online unsupervised method in adaptively discovers and learns the patterns of new spam activities and achieve stable recall values reaching more than 95%. Although the average spam precision of our method is around 60%, the high spam recall values show the ability of our proposed method in reducing spam drift problems compared to traditional machine learning algorithms.
Exploiting Emojis for Sarcasm Detection

Modern social media platforms largely rely on text. However, the written text lacks the emotional cues of spoken and face-to-face dialogue, ambiguities are common, which is exacerbated in the short, informal nature of many social media posts. Sarcasm represents the nuanced form of language that individuals state the opposite of what is implied. Sarcasm detection on social media is important for users to understand the underlying messages. The majority of existing sarcasm detection algorithms focus on text information; while emotion information expressed such as emojis are ignored. In real scenarios, emojis are widely used as emotion signals, which have great potentials to advance sarcasm detection. Therefore, in this paper, we study the novel problem of exploiting emojis for sarcasm detection on social media. We propose a new framework ESD, which simultaneously captures various signals from text and emojis for sarcasm detection. Experimental results on real-world datasets demonstrate the effectiveness of the proposed framework.
Token-level Dynamic Self-Attention Network for Multi-Passage Reading Comprehension

Multi-passage reading comprehension requires the ability to combine cross-passage information and reason over multiple passages to infer the answer.In this paper, we introduce the Dynamic Self-attention Network (Dyn-SAN) for multi-passage reading comprehension task, which processes cross-passage information at token-level and meanwhile avoids substantial computational costs.The core module of the dynamic self-attention is a proposed gated token selection mechanism, which dynamically selects important tokens from a sequence.These chosen tokens will attend to each other via a self-attention mechanism to model long-range dependencies.Besides, convolutional layers are combined with the dynamic self-attention to enhance the model's capacity of extracting local semantic.The experimental results show that the proposed DynSAN achieves new state-of-the-art performance on the SearchQA, Quasar-T and Wiki-Hop datasets.Further ablation study also validates the effectiveness of our model components.
Instance-Level Facial Attributes Transfer with Geometry-Aware Flow

We address the problem of instance-level facial attribute transfer without paired training data, e.g., faithfully transferring the exact mustache from a source face to a target face. This is a more challenging task than the conventional semantic-level attribute transfer, which only preserves the generic attribute style instead of instance-level traits. We propose the use of geometry-aware flow, which serves as a wellsuited representation for modeling the transformation between instance-level facial attributes. Specifically, we leverage the facial landmarks as the geometric guidance to learn the differentiable flows automatically, despite of the large pose gap existed. Geometry-aware flow is able to warp the source face attribute into the target face context and generate a warp-and-blend result. To compensate for the potential appearance gap between source and target faces, we propose a hallucination sub-network that produces an appearance residual to further refine the warp-and-blend result. Finally, a cycle-consistency framework consisting of both attribute transfer module and attribute removal module is designed, so that abundant unpaired face images can be used as training data. Extensive evaluations validate the capability of our approach in transferring instance-level facial attributes faithfully across large pose and appearance gaps. Thanks to the flow representation, our approach can readily be applied to generate realistic details on high-resolution images1.
Investigating Cognitive Effects in Session-level Search User Satisfaction

User satisfaction is an important variable in Web search evaluation studies and has received more and more attention in recent years. Many studies regard user satisfaction as the ground truth for designing better evaluation metrics. However, most of the existing studies focus on designing Cranfield-like evaluation metrics to reflect user satisfaction at query-level. As information need becomes more and more complex, users often need multiple queries and multi-round search interactions to complete a search task (e.g. exploratory search). In those cases, how to characterize the user's satisfaction during a search session still remains to be investigated. In this paper, we collect a dataset through a laboratory study in which users need to complete some complex search tasks. With the help of hierarchical linear models (HLM), we try to reveal how user's query-level and session-level satisfaction are affected by different cognitive effects. A number of interesting findings are made. At query level, we found that although the relevance of top-ranked documents have important impacts (primacy effect), the average/maximum of perceived usefulness of clicked documents is a much better sign of user satisfaction. At session level, perceived satisfaction for a particular query is also affected by the other queries in the same session (anchor effect or expectation effect). We also found that session-level satisfaction correlates mostly with the last query in the session (recency effect). The findings will help us design better session-level user behavior models and corresponding evaluation metrics.
On explaining machine learning models by evolving crucial and compact features

Feature construction can substantially improve the accuracy of Machine Learning (ML) algorithms. Genetic Programming (GP) has been proven to be effective at this task by evolving non-linear combinations of input features. GP additionally has the potential to improve ML explainability since explicit expressions are evolved. Yet, in most GP works the complexity of evolved features is not explicitly bound or minimized though this is arguably key for explainability. In this article, we assess to what extent GP still performs favorably at feature construction when constructing features that are (1) Of small-enough number, to enable visualization of the behavior of the ML model; (2) Of small-enough size, to enable interpretability of the features themselves; (3) Of sufficient informative power, to retain or even improve the performance of the ML algorithm. We consider a simple feature construction scheme using three different GP algorithms, as well as random search, to evolve features for five ML algorithms, including support vector machines and random forest. Our results on 21 datasets pertaining to classification and regression problems show that constructing only two compact features can be sufficient to rival the use of the entire original feature set. We further find that a modern GP algorithm, GP-GOMEA, performs best overall. These results, combined with examples that we provide of readable constructed features and of 2D visualizations of ML behavior, lead us to positively conclude that GP-based feature construction still works well when explicitly searching for compact features, making it extremely helpful to explain ML models.
The Use of Deep Learning Distributed Representations in the Identification of Abusive Text

The selection of optimal feature representations is a critical step in the use of machine learning in text classification. Traditional features (e.g. bag of words and n-grams) have dominated for decades, but in the past five years, the use of learned distributed representations has become increasingly common. In this paper, we summarise and present a categorisation of the stateof-the-art distributed representation techniques, including word and sentence embedding models. We carry out an empirical analysis of the performance of the various feature representations using the scenario of detecting abusive comments. We compare classification accuracies across a range of off-the-shelf embedding models using 10 labelled datasets gathered from different social media platforms. Our results show that multi-task sentence embedding models perform best with consistently highest classification results in comparison to other embedding models. We hope our work can be a guideline for practitioners in selecting appropriate features in text classification task, particularly in the domain of abuse detection.
An Item–Item Collaborative Filtering Recommender System Using Trust and Genre to Address the Cold-Start Problem

Item-based collaborative filtering is one of the most popular techniques in the recommender system to retrieve useful items for the users by finding the correlation among the items. Traditional item-based collaborative filtering works well when there exists sufficient rating data but cannot calculate similarity for new items, known as a cold-start problem. Usually, for the lack of rating data, the identification of the similarity among the cold-start items is difficult. As a result, existing techniques fail to predict accurate recommendations for cold-start items which also affects the recommender system’s performance. In this paper, two item-based similarity measures have been designed to overcome this problem by incorporating items’ genre data. An item might be uniform to other items as they might belong to more than one common genre. Thus, one of the similarity measures is defined by determining the degree of direct asymmetric correlation between items by considering their association of common genres. However, the similarity is determined between a couple of items where one of the items could be cold-start and another could be any highly rated item. Thus, the proposed similarity measure is accounted for as asymmetric by taking consideration of the item’s rating data. Another similarity measure is defined as the relative interconnection between items based on transitive inference. In addition, an enhanced prediction algorithm has been proposed so that it can calculate a better prediction for the recommendation. The proposed approach has experimented with two popular datasets that is Movielens and MovieTweets. In addition, it is found that the proposed technique performs better in comparison with the traditional techniques in a collaborative filtering recommender system. The proposed approach improved prediction accuracy for Movielens and MovieTweets approximately in terms of 3.42% &amp; 8.58% mean absolute error, 7.25% &amp; 3.29% precision, 7.20% &amp; 7.55% recall, 8.76% &amp; 5.15% f-measure and 49.3% and 16.49% mean reciprocal rank, respectively.
An Efficient Framework for Sharing a File in a Secure Manner Using Asymmetric Key Distribution Management in Cloud Environment

Cloud computing is a platform to share the data and resources used among various organizations, but the survey shows that there is always a security threat. Security is an important aspect of cloud computing. Hence, the responsibility underlines to the cloud service providers for providing security as the quality of service. However, cloud computing has many challenges in security that have not yet been addressed well. The data accessed or shared through any devices from the cloud environment are not safe because they are likely to have various attacks like Identity Access Management (IAM), hijacking an account or a service either by internal/external intruders. The cryptography places a major role to secure the data within the cloud environment. Therefore, there is a need for standard encryption/decryption mechanism to protect the data stored in the cloud, in which key is the mandatory element. Every cloud provider has its own security mechanisms to protect the key. The client cannot trust the service provider completely in spite of the fact that, at any instant, the provider has full access to both data and key. In this paper, we have proposed a new system which can prevent the exposure of the key as well as a framework for sharing a file that will ensure security (CIA) using asymmetric key and distributing it within the cloud environment using a trusted third party. We have compared RSA with ElGamal and Paillier in our proposed framework and found RSA gives a better result.
Global-connected network with generalized ReLU activation

Recent Progress has shown that exploitation of hidden layer neurons in convolutional neural networks (CNN) incorporating with a carefully designed activation function can yield better classification results in the field of computer vision. The paper firstly introduces a novel deep learning (DL) architecture aiming to mitigate the gradient-vanishing problem, in which the earlier hidden layer neurons could be directly connected with the last hidden layer and fed into the softmax layer for classification. We then design a generalized linear rectifier function as the activation function that can approximate arbitrary complex functions via training of the parameters. We will show that our design can achieve similar performance in a number of object recognition and video action benchmark tasks, such as MNIST, CIFAR-10/100, SVHN, Fashion-MNIST, STL-10, and UCF YoutTube Action Video datasets, under significantly less number of parameters and shallower network infrastructure, which is not only promising in training in terms of computation burden and memory usage, but is also applicable to low-computation, low-memory mobile scenarios for inference.
Computational Psychometrics Approach to Holistic Learning and Assessment Systems

Learning and assessment systems have grown and taken shape to incorporate concepts from both models for assessment and models for learning. In this paper we argue that a third dimension is necessary. Not only is it important to understand what the capabilities of a learner are, and how to grow and expand these capabilities, but we must consider where the learner is headed; we need to consider models for navigation. This holistic perspective of learning and assessment systems is encapsulated in the extended learning and assessment system, a framework for conducting research. Fundamental to this framework is the role of computational psychometrics to facilitate the abstraction from raw data to conceptual models. We provide several examples of research projects and describe how they fit into the described framework.
A Parameterized Runtime Analysis of Evolutionary Algorithms for the Euclidean Traveling Salesperson Problem

We contribute to the theoretical understanding of evolutionary algorithms and carry out a parameterized analysis of evolutionary algorithms for the Euclidean traveling salesperson problem (Euclidean TSP). We exploit structural properties related to the optimization process of evolutionary algorithms for this problem and use them to bound the runtime of evolutionary algorithms. Our analysis studies the runtime in dependence of the number of inner points $k$ and shows that simple evolutionary algorithms solve the Euclidean TSP in expected time O(nk(2k-1)!). Moreover, we show that, under reasonable geometric constraints, a locally optimal 2-opt tour can be found by randomized local search in expected time $O(n2kk!).
Domain Agnostic Real-Valued Specificity Prediction

Sentence specificity quantifies the level of detail in a sentence, characterizing the organization of information in discourse. While this information is useful for many downstream applications, specificity prediction systems predict very coarse labels (binary or ternary) and are trained on and tailored toward specific domains (e.g., news). The goal of this work is to generalize specificity prediction to domains where no labeled data is available and output more nuanced realvalued specificity ratings.We present an unsupervised domain adaptation system for sentence specificity prediction, specifically designed to output real-valued estimates from binary training labels. To calibrate the values of these predictions appropriately, we regularize the posterior distribution of the labels towards a reference distribution. We show that our framework generalizes well to three different domains with 50%-68% mean absolute error reduction than the current state-of-the-art system trained for news sentence specificity. We also demonstrate the potential of our work in improving the quality and informativeness of dialogue generation systems.
Adversarial Similarity Metric Learning for Kinship Verification

Given a pair of facial images, it is an interesting yet challenging problem to determine if there is a kin relation between them.Recent research on that topic has made encouraging progress by learning a kin similarity metric from kinship data.However, most of the existing metric learning algorithms cannot handle hard samples very well, i.e., some ambiguous test pairs cannot be well classified due to some compounding factors, such as the large age gap or gender difference between the parents and children.To address this, we propose an Adversarial Similarity Metric Learning (ASML) method in this paper.More specifically, ASML consists of two adversarial phases: confusion and discrimination.In confusion phase, ambiguous adversarial pairs are automatically generated to challenge the learned similarity metric; while in discrimination phase, the learned metric tries its best to adjust itself to distinguish both the original pairs and the generated adversarial pairs.Consequently, a robust and discriminative similarity metric can be learned by iteratively performing the two adversarial phases.Experiments on the two widely used kinship datasets demonstrate the efficacy of our proposed ASML method in comparison with the state-of-the-art metric learning solutions to kinship verification.