Reviewerly/exHarmony huggingface dataset:
Linguistic elements of conversational human voice in online brand communication: Manipulations and perceptions

The conversational human voice (CHV) is an extensively studied and adopted communication style in online brand communication. However, in previous research the way in which CHV is operationalized differs considerably: the type and the number of linguistic elements used to establish a sense of CHV in online brand messages varies. Moreover, it is still unknown how CHV operationalizations contribute to consumers’ perceptions of CHV, which consequently could affect their evaluation regarding the message and the brand. In this paper, we addressed these issues by conducting an integrative literature review and a perception experiment, and consequently present a taxonomy of linguistic elements related to message personalization, informal speech, and invitational rhetoric that can be used to operationalize CHV systematically in future studies in online brand communication. Directions for future research and managerial implications are discussed.
Delay-aware model-based reinforcement learning for continuous control

Action delays degrade the performance of reinforcement learning in many real-world systems. This paper proposes a formal definition of delay-aware Markov Decision Process and proves it can be transformed into standard MDP with augmented states using the Markov reward process. We develop a delay-aware model-based reinforcement learning framework that can incorporate the multi-step delay into the learned system models without learning effort. Experiments with the Gym and MuJoCo platforms show that the proposed delay-aware model-based algorithm is more efficient in training and transferable between systems with various durations of delay compared with state-of-the-art model-free reinforcement learning methods.
The Winner Takes it All: Geographic Imbalance and Provider (Un)fairness in Educational Recommender Systems

Educational recommender systems channel most of the research efforts on the effectiveness of the recommended items. While teachers have a central role in online platforms, the impact of recommender systems for teachers in terms of the exposure such systems give to the courses is an under-explored area. In this paper, we consider data coming from a real-world platform and analyze the distribution of the recommendations w.r.t. the geographical provenience of the teachers. We observe that data is highly imbalanced towards the United States, in terms of offered courses and of interactions. These imbalances are exacerbated by recommender systems, which overexpose the country w.r.t. its representation in the data, thus generating unfairness for teachers outside that country. To introduce equity, we propose an approach that regulates the share of recommendations given to the items produced in a country (visibility) and the position of the items in the recommended list (exposure).
A local search algorithm for k-means with outliers

k-Means is a well-studied clustering problem that finds applications in many fields related to unsupervised learning. It is known that k-means clustering is highly sensitive to the isolated points (called outliers). Such outliers can significantly influence the final cluster configuration and should be removed to obtain quality solutions. In this paper, we study the k-means with outliers problem. Given a set of data points, the problem is to remove a set of outliers such that the k-means clustering cost of the remaining points is minimized. Designing efficient algorithms for this problem remains an active area of research due to its important role in dealing with noisy data. We consider a relaxed objective function and propose a local search algorithm for k-means with outliers. It is shown that the algorithm has better performance guarantees than previously implemented methods. In particular, it yields a constant-factor bi-criteria approximation solution to the problem. Moreover, we show experimentally that the algorithm performs much better than its provable guarantee and dominates other state-of-the-art methods for the problem.
The impact of user perceptions of AR on purchase intention of location-based AR navigation systems

As new AR supported products such as location-based AR navigation systems become available in the consumer market, it is particularly important to understand how user perceptions of AR can be optimized to enhance customer satisfaction and increase purchase intention of these products. However, little research has addressed this gap in the literature so far. Therefore, this study developed a theoretical framework including user perceptions of AR, experience, satisfaction and purchase intention employing experience economy theory. This study's main theoretical contribution is that three user perceptions of AR – spatial ability (sensory domain), sense of presence (feeling domain), and conceptual understanding (cognitive domain) – were found to be key antecedents of consumers' intention to purchase location-based AR navigation systems through the mediation of educational, entertainment, aesthetic, and escape experience. The findings have important implications for future development of location-based AR systems.
Towards a Better Understanding of Query Reformulation Behavior in Web Search

As queries submitted by users directly affect search experiences, how to organize queries has always been a research focus in Web search studies. While search request becomes complex and exploratory, many search sessions contain more than a single query thus reformulation becomes a necessity. To help users better formulate their queries in these complex search tasks, modern search engines usually provide a series of reformulation entries on search engine result pages (SERPs), i.e., query suggestions and related entities. However, few existing work have thoroughly studied why and how users perform query reformulations in these heterogeneous interfaces. Therefore, whether search engines provide sufficient assistance for users in reformulating queries remains under-investigated. To shed light on this research question, we conducted a field study to analyze fine-grained user reformulation behaviors including reformulation type, entry, reason, and the inspiration source with various search intents. Different from existing efforts that rely on external assessors to make judgments, in the field study we collect both implicit behavior signals and explicit user feedback information. Analysis results demonstrate that query reformulation behavior in Web search varies with the type of search tasks. We also found that the current query suggestion/related query recommendations provided by search engines do not offer enough help for users in complex search tasks. Based on the findings in our field study, we design a supervised learning framework to predict: 1) the reason behind each query reformulation, and 2) how users organize the reformulated query, both of which are novel challenges in this domain. This work provides insight into complex query reformulation behavior in Web search as well as the guidance for designing better query suggestion techniques in search engines.
A novel filter feature selection method for text classification: Extensive Feature Selector

As the huge dimensionality of textual data restrains the classification accuracy, it is essential to apply feature selection (FS) methods as dimension reduction step in text classification (TC) domain. Most of the FS methods for TC contain several number of probabilities. In this study, we proposed a new FS method named as Extensive Feature Selector (EFS), which benefits from corpus-based and class-based probabilities in its calculations. The performance of EFS is compared with nine well-known FS methods, namely, Chi-Squared (CHI2), Class Discriminating Measure (CDM), Discriminative Power Measure (DPM), Odds Ratio (OR), Distinguishing Feature Selector (DFS), Comprehensively Measure Feature Selection (CMFS), Discriminative Feature Selection (DFSS), Normalised Difference Measure (NDM) and Max–Min Ratio (MMR) using Multinomial Naive Bayes (MNB), Support-Vector Machines (SVMs) and k-Nearest Neighbour (KNN) classifiers on four benchmark data sets. These data sets are Reuters-21578, 20-Newsgroup, Mini 20-Newsgroup and Polarity. The experiments were carried out for six different feature sizes which are 10, 30, 50, 100, 300 and 500. Experimental results show that the performance of EFS method is more successful than the other nine methods in most cases according to micro- F1 and macro- F1 scores.
BRIGHT: A Bridging Algorithm for Network Alignment

Multiple networks emerge in a wealth of high-impact applications. Network alignment, which aims to find the node correspondence across different networks, plays a fundamental role for many data mining tasks. Most of the existing methods can be divided into two categories: (1) consistency optimization based methods, which often explicitly assume the alignment to be consistent in terms of neighborhood topology and attribute across networks, and (2) network embedding based methods which learn low-dimensional node embedding vectors to infer alignment. In this paper, by analyzing representative methods of these two categories, we show that (1) the consistency optimization based methods are essentially specific random walk propagations from anchor links that might be too restrictive; (2) the embedding based methods no longer explicitly assume alignment consistency but inevitably suffer from the space disparity issue. To overcome these two limitations, we bridge these methods and propose a novel family of network alignment algorithms BRIGHT to handle both plain and attributed networks. Specifically, it constructs a space by random walk with restart (RWR) whose bases are one-hot encoding vectors of anchor nodes, followed by a shared linear layer. Our experiments on real-world networks show that the proposed family of algorithms BRIGHT outperform the state-of-the-arts for both plain and attributed network alignment tasks.
A novel kernel-free least squares twin support vector machine for fast and accurate multi-class classification

Multi-class classification is an important and challenging research topic with many real-life applications. The problem is much harder than the classical binary classification, especially when the given data set is imbalanced. Hidden nonlinear patterns in the data set can further complicate the task of multi-class classification. In this paper, we propose a kernel-free least squares twin support vector machine for multi-class classification. The proposed model employs a special fourth order polynomial surface, namely the double well potential surface, and adopts the ”one-verses-all” classification strategy. An ℓ2 regularization term is added to accommodate data sets with different levels of nonlinearity. We provide some theoretical analysis of the proposed model. Computational results using artificial data sets and public benchmarks clearly show the superior performance of the proposed model over other well-known multi-class classification methods, in particular for imbalanced data sets.
Energy trading IoT system based on blockchain

Energy trading technology based on Internet of Things (IoTs) is one of the research hotspots. However, in the process of energy trading, it is easy to leak the user's personal privacy. In order to solve the problem of privacy leakage in the energy trading IoT system, we explore a secure energy trading method based on blockchain technology and homomorphic encryption technology. Firstly, we propose a homomorphic encryption method for blockchain system. Secondly, we propose a privacy protection method based on blockchain architecture. Finally, we propose an energy trading IoT system based on blockchain. By integrating homomorphic encryption, blockchain and other technologies, we have implemented a secure energy trading system. Compared with other similar systems, our energy trading IoT system based on blockchain can provide more secure user services. In addition, our system can better protect the privacy of users.
Evaluation of clustering and topic modeling methods over health-related tweets and emails

Internet provides different tools for communicating with patients, such as social media (e.g., Twitter) and email platforms. These platforms provided new data sources to shed lights on patient experiences with health care and improve our understanding of patient-provider communication. Several existing topic modeling and document clustering methods have been adapted to analyze these new free-text data automatically. However, both tweets and emails are often composed of short texts; and existing topic modeling and clustering approaches have suboptimal performance on these short texts. Moreover, research over health-related short texts using these methods has become difficult to reproduce and benchmark, partially due to the absence of a detailed comparison of state-of-the-art topic modeling and clustering methods on these short texts.We trained eight state-of- the-art topic modeling and clustering algorithms on short texts from two health-related datasets (tweets and emails): Latent Semantic Indexing (LSI), Latent Dirichlet Allocation (LDA), LDA with Gibbs Sampling (GibbsLDA), Online LDA, Biterm Model (BTM), Online Twitter LDA, and Gibbs Sampling for Dirichlet Multinomial Mixture (GSDMM), as well as the k-means clustering algorithm with two different feature representations: TF-IDF and Doc2Vec. We used cluster validity indices to evaluate the performance of topic modeling and clustering: two internal indices (i.e. assessing the goodness of a clustering structure without external information) and five external indices (i.e. comparing the results of a cluster analysis to an externally known provided class labels).In overall, for number of clusters (k) from 2 to 50, Online Twitter LDA and GSDMM achieved the best performance in terms of internal indices, while LSI and k-means with TF-IDF had the highest external indices. Also, of all tweets (N = 286, 971; HPV represents 94.6% of tweets and lynch syndrome represents 5.4%), for k = 2, most of the methods could respect this initial clustering distribution. However, we found model performance varies with the source of data and hyper-parameters such as the number of topics and the number of iterations used to train the models. We also conducted an error analysis using the Hamming loss metric, for which the poorest value was obtained by GSDMM on both datasets.Researchers hoping to group or classify health related short-text data can expect to select the most suitable topic modeling and clustering methods for their specific research questions. Therefore, we presented a comparison of the most common used topic modeling and clustering algorithms over two health-related, short-text datasets using both internal and external clustering validation indices. Internal indices suggested Online Twitter LDA and GSDMM as the best, while external indices suggested LSI and k-means with TF-IDF as the best. In summary, our work suggested researchers can improve their analysis of model performance by using a variety of metrics, since there is not a single best metric.
Are schools digitally inclusive for all? Profiles of school digital inclusion using PISA 2018

This study used international student achievement data from the OECD's Programme for International Student Assessment (PISA) 2018 to identify the profiles of school digital inclusion (SDI). In digitally inclusive schools, students who live in the digital era benefit from digital technology and a digitally enhanced educational environment without alienation and exclusion. We compiled profiles of SDI by conducting a latent profile analysis on data from 27 countries. Multinomial logistic regression analyses were conducted to examine whether a school's socioeconomic status and type could predict its likelihood of fitting a particular profile across SDI subgroups. Schools were categorized in terms of low-, medium-, and high-level digital inclusion. Socioeconomic status and type of school were significant predictors of profile membership. Based on the results, the practical implications for promoting SDI include digital infrastructure as a prerequisite, teachers' confidence in using digital technology and instructional competence, and the digital skills of teachers and students.
Global Analysis of Atmospheric Transmissivity Using Cloud Cover, Aridity and Flux Network Datasets

Atmospheric transmissivity (τ) is a critical factor in climatology, which affects surface energy balance, measured at a limited number of meteorological stations worldwide. With the limited availability of meteorological datasets in remote areas across different climatic regions, estimation of τ is becoming a challenging task for adequate hydrological, climatic, and crop modeling studies. The availability of solar radiation data is comparatively less accessible on a global scale than the temperature and precipitation datasets, which makes it necessary to develop methods to estimate τ. Most of the previous studies provided region specific datasets of τ, which usually provide local assessments. Hence, there is a necessity to give the empirical models for τ estimation on a global scale that can be easily assessed. This study presents the analysis of the τ relationship with varying geographic features and climatic factors like latitude, aridity index, cloud cover, precipitation, temperature, diurnal temperature range, and elevation. In addition to these factors, the applicability of these relationships was evaluated for different climate types. Thus, empirical models have been proposed for each climate type to estimate τ by using the most effective factors such as cloud cover and aridity index. The cloud cover is an important yet often overlooked factor that can be used to determine the global atmospheric transmissivity. The empirical relationship and statistical indicator provided the best performance in equatorial climates as the coefficient of determination (r2) was 0.88 relatively higher than the warm temperate (r2 = 0.74) and arid regions (r2 = 0.46). According to the results, it is believed that the analysis presented in this work is applicable for estimating the τ in different ecosystems across the globe.
IterDet: Iterative Scheme for Object Detection in Crowded Environments

Deep learning-based detectors tend to produce duplicate detections of the same objects. After that, the detections are filtered via a non-maximum suppression algorithm (NMS) so that there remains only one bounding box per object. This simple greedy scheme is sufficient for isolated objects. However, it often fails in crowded environments since boxes for different objects should be preserved and duplicate detections should be suppressed at the same time. In this work, we propose to obtain predictions following iterative scheme called IterDet. At each iteration, a new subset of objects is detected. Detected boxes from all the previous iterations are considered at the current iteration to ensure that the same object would not be detected twice. This iterative scheme can be applied to both one-stage and two-stage deep learning-based detectors with minor modifications. Through extensive evaluation on 4 diverse datasets with two different baseline detectors, we prove our iterative scheme to achieve significant improvement over the baseline. On CrowdHuman and WiderPerson datasets, we obtain state-of-the-art results. The source code and the trained models are available at https://github.com/saic-vul/iterdet.
SmartHarvest

We can increase the efficiency of public cloud datacenters by harvesting allocated but temporarily idling CPU cores from customer virtual machines (VMs) to run batch or analytics workloads. Even small efficiency gains translate into substantial savings, since provisioning and operating a datacenter costs hundreds of millions of dollars per year. The main challenge is to harvest idle cores with little or no impact on customer VMs, which could be running latency-sensitive services and are essentially black-boxes to the cloud provider.
Youth Trust in Social Media Companies and Expectations of Justice

Social media platforms aspire to deliver fair resolutions after online harassment. Platforms rely on sanctions like removing content or banning users but these punitive responses provide little opportunity for justice or reparation for targets of harassment. This may be especially important for youth, who experience pervasive harassment which can have uniquely harmful effects on their wellbeing. We conducted a text-message based survey with 832 U.S. adolescents and young adults, ages 14-24, to explore their attitudes towards social media companies' responses to online harassment. We find that youth are twice as likely (41% versus 20%) not to trust social media companies' ability to achieve a fair resolution as they are to trust them. Nearly two-thirds (62%) of youth expressed a preference for an apology from the offender after online harassment, and they were twice as likely to prefer a private apology to a public one (29% versus 14%). Preferences also vary by identity, revealing how a one-size-fits-all approach can harm some youth while benefitting others. We reflect on the opportunities and risks associated with institutional trust and restorative justice for supporting youth who experience online harassment.
RAPT360: Reinforcement Learning-Based Rate Adaptation for 360-Degree Video Streaming With Adaptive Prediction and Tiling

Tile-based rate adaption can improve the quality of experience (QoE) for adaptive 360-degree video streaming under constrained network conditions, which, however, is a challenging problem due to the requirements of accurate prediction for users&#x2019; viewports and optimal bitrate allocation for tiles. In this paper, we propose a strategy that deploys reinforcement learning-based Rate Adaptation with adaptive Prediction and Tiling for 360-degree video streaming, named RAPT360, to address these challenges. Specifically, to improve the accuracy of the state-of-the-art viewport prediction approaches, we fit the time-varying Laplace distribution-based probability density function of the prediction error for different prediction lengths. On the basis of that, we develop a viewport identification method to determine the viewport area of a user depending on the buffer occupancy, where the obtained viewport can cover the real viewport with any given probability confidence level. We then propose a viewport-aware adaptive tiling scheme to improve the bandwidth efficiency, where three types of tile granularities are allocated according to the shape and position of the 2-D projection of that viewport. By establishing an adaptive streaming model and QoE metric specific to 360-degree videos, we finally formulate the rate adaptation problem for tile-based 360-degree video streaming as a non-linear discrete optimization problem that targets at maximizing the long-term user QoE under a bandwidth-constrained network. To efficiently solve this problem, we model the rate adaptation logic as a Markov decision process (MDP) and employ the deep reinforcement learning (DRL)-based algorithm to dynamically learn the optimal bitrate allocation of tiles. Extensive experimental results show that RAPT360 achieves a performance gain of at least 1.47 dB on average chunk QoE, including a video quality improvement of at least 1.33 dB, in comparison to the existing strategies for tile-based adaptive 360-degree video streaming.