Reviewerly/exHarmony huggingface dataset:
Challenges and Best Practices Associated with Sharing Research Materials and Research Data for Communication Scholars

Scientific practices are constantly evolving to meet the best practices and standards available at a point in time. In the current publication environment, advances in communication technology in tandem with concerns about the transparency of extant communication scholarship have encouraged many to reconsider how we share knowledge with one another. Among a broader suite of open science practices, two specific changes have been advocated for: sharing research materials and sharing research data. As part of an ongoing conversation within communication science, this essay summarizes and explains how researchers can critically consider so-called "open materials" and "open data" when publishing in Communication Studies as well as other communication and adjacent journals.
The Effect of Augmented Reality on Consumer Learning for Search and Experience Products in Mobile Commerce

Mobile augmented reality (AR) has emerged as an effective interactive technology for providing visual information on products. As the importance of contextual information grows, mobile AR makes it easier for consumers to visually examine products virtually placed in their existing physical spaces. Despite the popularity and potential of mobile AR in commerce, there is a lack of research confirming that, in terms of providing consumers with shopping experiences, mobile AR is a more influential tool than the traditional method of product presentation. Based on the experience economy and task-media fit theories, this study examines the effect of AR information in mobile shopping on consumer learning, including enjoyment, compared to that of two-dimensional and three-dimensional information. Moreover, we examine the moderating effect of product type on this relationship. We conducted experiments with 212 participants to confirm these relationships. Our results revealed that AR information helps improve consumer learning and purchase intention in mobile environments, and the effect of AR is stronger for experience products than for search products.
Achieving low-entropy secure cloud data auditing with file and authenticator deduplication

Cloud data auditing with file and authenticator deduplication can not only check the integrity of cloud data, but also reduce the cloud’s storage overhead significantly. In this paper, we propose a cloud data auditing scheme supporting file and authenticator deduplication. To the best of our knowledge, the proposed scheme is the first practical one that truly achieves low-entropy security. For the file with low-entropy, the malicious cloud cannot forge any authenticator to pass the auditing verification. The proposed scheme is user-friendly. Users do not need to keep interacting with the Third Party Auditor (TPA) in the auditing phase. We give the detailed security proof to show that the proposed scheme is secure. Comprehensive experiments show the efficiency of our scheme.
A robust algorithm for explaining unreliable machine learning survival models using the Kolmogorov–Smirnov bounds

A new robust algorithm based on the explanation method SurvLIME called SurvLIME-KS is proposed for explaining machine learning survival models. The algorithm is developed to ensure robustness to cases of a small amount of training data or outliers of survival data. The first idea behind SurvLIME-KS is to apply the Cox proportional hazards model to approximate the black-box survival model at the local area around a test example due to the linear relationship of covariates in the model. The second idea is to incorporate the well-known Kolmogorov–Smirnov bounds for constructing sets of predicted cumulative hazard functions. As a result, the robust maximin strategy is used, which aims to minimize the average distance between cumulative hazard functions of the explained black-box model and of the approximating Cox model, and to maximize the distance over all cumulative hazard functions in the interval produced by the Kolmogorov–Smirnov bounds. The maximin optimization problem is reduced to the quadratic program. Various numerical experiments with synthetic and real datasets demonstrate the SurvLIME-KS efficiency.
Predicting continuous integration build failures using evolutionary search

Context: Continuous Integration (CI) is a common practice in modern software development and it is increasingly adopted in the open-source as well as the software industry markets. CI aims at supporting developers in integrating code changes constantly and quickly through an automated build process. However, in such context, the build process is typically time and resource-consuming which requires a high maintenance effort to avoid build failure. Objective: The goal of this study is to introduce an automated approach to cut the expenses of CI build time and provide support tools to developers by predicting the CI build outcome. Method: In this paper, we address problem of CI build failure by introducing a novel search-based approach based on Multi-Objective Genetic Programming (MOGP) to build a CI build failure prediction model. Our approach aims at finding the best combination of CI built features and their appropriate threshold values, based on two conflicting objective functions to deal with both failed and passed builds. Results: We evaluated our approach on a benchmark of 56,019 builds from 10 large-scale and long-lived software projects that use the Travis CI build system. The statistical results reveal that our approach outperforms the state-of-the-art techniques based on machine learning by providing a better balance between both failed and passed builds. Furthermore, we use the generated prediction rules to investigate which factors impact the CI build results, and found that features related to (1) specific statistics about the project such as team size, (2) last build information in the current build and (3) the types of changed files are the most influential to indicate the potential failure of a given build. Conclusion: This paper proposes a multi-objective search-based approach for the problem of CI build failure prediction. The performances of the models developed using our MOGP approach were statistically better than models developed using machine learning techniques. The experimental results show that our approach can effectively reduce both false negative rate and false positive rate of CI build failures in highly imbalanced datasets.
An Evolutionary Deep Learning-Based Anomaly Detection Model for Securing Vehicles

This article proposes a deep learning based approach for cyber attack detection in the vehicles. The proposed method is constructed based on generative adversarial network (GAN) classification to assess the message frames transferring between the electric control unit (ECU) and other hardware in the vehicle. To this end, two networks called generator (G) and discriminator (D) will run an adversarial game to fool each other. In such a process, the most optimal structure is found which distinguish between the model normal behavior and abnormalities. Due to the instabilities existing in the GAN model, a new optimization method based on firefly algorithm is proposed to create a class of generators in a feasible region, i.e. the discriminator D. A three-stage modification method is also devised to increase the algorithm population diversity and reduce the possibility of falling in local optima. The performance of the model is assessed on the experimental dataset recorded from the OBD-II port of an undefined vehicle.
CrossATNet - a novel cross-attention based framework for sketch-based image retrieval

We propose a novel framework for cross-modal zero-shot learning (ZSL) in the context of sketch-based image retrieval (SBIR). Conventionally, the SBIR schema mainly considers simultaneous mappings among the two image views and the semantic side information. Therefore, it is desirable to consider fine-grained classes mainly in the sketch domain using highly discriminative and semantically rich feature space. However, the existing deep generative modeling-based SBIR approaches majorly focus on bridging the gaps between the seen and unseen classes by generating pseudo-unseen-class samples. Besides, violating the ZSL protocol by not utilizing any unseen-class information during training, such techniques do not pay explicit attention to modeling the discriminative nature of the shared space. Also, we note that learning a unified feature space for both the multi-view visual data is a tedious task considering the significant domain difference between sketches and color images. In this respect, as a remedy, we introduce a novel framework for zero-shot SBIR. While we define a cross-modal triplet loss to ensure the discriminative nature of the shared space, an innovative cross-modal attention learning strategy is also proposed to guide feature extraction from the image domain exploiting information from the respective sketch counterpart. In order to preserve the semantic consistency of the shared space, we consider a graph CNN-based module that propagates the semantic class topology to the shared space. To ensure an improved response time during inference, we further explore the possibility of representing the shared space in terms of hash codes. Experimental results obtained on the benchmark TU-Berlin and the Sketchy datasets confirm the superiority of CrossATNet in yielding state-of-the-art results.
Observation Imbalanced Data Text to Predict Users Selling Products on Female Daily with SMOTE, Tomek, and SMOTE-Tomek

Female Daily is a beauty platform that has social media application share users' experiences of beauty by posting images and text in a post. Female Daily has terms of condition to not use the platform for selling in their post. Somehow, users of Female Daily sometimes use the platform for selling beauty products. Post of users in Female Daily records in Female Daily databases. In that data, there are imbalanced data about users' posts that banned (minority class) and post that admin does not ban because it does not contain selling products (majority class). SMOTE and Tomek are techniques for handling imbalanced data by over-sampling and under-sampling techniques repeatedly to manage the data into balance. In this study, we want to evaluate the imbalanced data text in Female Daily using SMOTE, Tomek, and SMOTE-Tomek. Predicting algorithms that we will use are Support Vector Machine (SVM) and Logistic Regression (LR) using transform vector TF-IDF to evaluate the best methods to predict the users selling products on Female Daily. The results of this study show us the effect of SMOTE, Tomek, and SMOTE-Tomek to Precision-Recall in people selling products (majority class) is effects not quite high and also reducing the Precision-Recall, but for people selling products (minority class) is positives improvement. The highest results combination each metrics are; G-Mean combination SMOTE-Tomek with SVM, Precision to minority class combination of SMOTE with SVM, Recall to minority class combination of SMOTE with LR. Experimental results on this study indicate the usefulness of the using SMOTE or SMOTE-Tomek approach.
Online Group Student Peer-Communication as an Element of Open Education

Information and communication technologies transform modern education into a more available learning matrix. One of the unexplored aspects of open education is the constant communicative interaction within the student group by using social media. The aim of the study was to determine principal functions of student-led communication in the educational process, the method for assessing its strong points and the disadvantages disrupting traditional learning. For the primary study of the phenomenon, we used methods that made it possible to propose approaches to further analysis. Netnography is the main research method defining the essence and characteristics of the student-led peer-communication. In our research, we applied data visualization, analytical and quantitative methods and developed a set of quantitative indicators that can be used to assess various aspects of student communication in chats. The elaborated visual model can serve as a simple tool for diagnosing group communication processes. We revealed that online group chats perform a support function in learning. They provide constant informational resource on educational and organizational issues and create emotional comfort. Identified features serve to define shortcomings (e.g., lack of students’ readiness to freely exchange answers to assignments) and significant factors (e.g., underutilized opportunities for self-organization) that exist in the modern system of higher education.
Ghost Removal via Channel Attention in Exposure Fusion

High dynamic range (HDR) imaging is to reconstruct high-quality images with a broad range of illuminations from a set of differently exposed images. Some existing algorithms align the input images before merging them into an HDR image, but artifacts of the registration appear due to misalignment. Recent works try to remove the ghosts by detecting motion region or skipping the registered process, however, the result still suffers from ghost artifacts for scenes with significant motions. In this paper, we propose a novel Multi-scale Channel Attention guided Network (MCANet) to address the ghosting problem. We use multi-scale blocks consisting of dilated convolution layers to extract informative features. The channel attention blocks suppress undesired components and guide the network to refine features to make full use of feature maps. The proposed MCANet recovers the occluded or saturated details and reduces artifacts due to misalignment. Experiments show that the proposed MCANet can achieve state-of-the-art quantitative and qualitative results.
REA: Robust Cross-lingual Entity Alignment Between Knowledge Graphs

Cross-lingual entity alignment aims at associating semantically similar entities in knowledge graphs with different languages. It has been an essential research problem for knowledge integration and knowledge graph connection, and been studied with supervised or semi-supervised machine learning methods with the assumption of clean labeled data. However, labels from human annotations often include errors, which can largely affect the alignment results. We thus aim to formulate and explore the robust entity alignment problem, which is non-trivial, due to the deficiency of noisy labels. Our proposed method named REA (Robust Entity Alignment) consists of two components: noise detection and noise-aware entity alignment. The noise detection is designed by following the adversarial training principle. The noise-aware entity alignment is devised by leveraging graph neural network based knowledge graph encoder as the core. In order to mutually boost the performance of the two components, we propose a unified reinforced training strategy to combine them. To evaluate our REA method, we conduct extensive experiments on several real-world datasets. The experimental results demonstrate the effectiveness of our proposed method and also show that our model consistently outperforms the state-of-the-art methods with significant improvement on alignment accuracy in the noise-involved scenario.
Debiasing Grid-based Product Search in E-commerce

The widespread usage of e-commerce websites in daily life and the resulting wealth of implicit feedback data form the foundation for systems that train and test e-commerce search ranking algorithms. While convenient to collect, implicit feedback data inherently suffers from various types of bias since user feedback is limited to products they are exposed to by existing search ranking algorithms and impacted by how the products are displayed. In the literature, a vast majority of existing methods have been proposed towards unbiased learning to rank for list-based web search scenarios. However, such methods cannot be directly adopted by e-commerce websites mainly for two reasons. First, in e-commerce websites, search engine results pages (SERPs) are displayed in 2-dimensional grids. The existing methods have not considered the difference in user behavior between list-based web search and grid-based product search. Second, there can be multiple types of implicit feedback (e.g., clicks and purchases) on e-commerce websites. We aim to utilize all types of implicit feedback as the supervision signals. In this work, we extend unbiased learning to rank to the world of e-commerce search via considering a grid-based product search scenario. We propose a novel framework which (1) forms the theoretical foundations to allow multiple types of implicit feedback in unbiased learning to rank and (2) incorporates the row skipping and slower decay click models to capture unique user behavior patterns in grid-based product search for inverse propensity scoring. Through extensive experiments on real-world e-commerce search log datasets across browsing devices and product taxonomies, we show that the proposed framework outperforms the state of the art unbiased learning to rank algorithms. These results also reveal important insights on how user behavior patterns vary in e-commerce SERPs across browsing devices and product taxonomies.
A deep neural network model for speakers coreference resolution in legal texts

Coreference resolution is one of the fundamental tasks in natural language processing (NLP), and is of great significance to understand the semantics of texts. Meanwhile, resolving coreference is essential for many NLP downstream applications. Existing methods largely focus on pronouns, possessives and noun phrases resolution in the general domain, while little work is proposed for professional domains such as the legal field. Different from general texts, how to code legal texts and capture the relationship between entities in the text, and then resolve coreference is a challenging problem. For better understanding the legal text, and facilitating a series of downstream tasks in legal text mining, we propose a deep neural network model for coreference resolution in court record documents. Specifically, the pre-trained language model and bi-directional long short-term memory networks are first utilized to encode legal texts. Second, graph neural networks are applied to incorporate reference relations between entities. Finally, two distinct classifiers are used to score the candidate pairs. Results on the dataset show that our model achieves 87.53% F1 score on court record documents, outperforming neural baseline models by a large margin. Further analysis shows that the proposed method can effectively identify the reference relations between entities and model the entity dependencies.
The Communication-Aware Clustered Federated Learning Problem

Federated learning (FL) refers to the adaptation of a central model based on data sets available at multiple remote users. Two of the common challenges encountered in FL are the fact that training sets obtained by different users are commonly heterogeneous, i.e., arise from different sample distributions, and the need to communicate large amounts of data between the users and the central server over the typically expensive up-link channel. In this work we formulate the problem of FL in which different clusters of users observe labeled samples drawn from different distributions, while operating under constraints on the communication overhead. For such settings, we identify that the combination of statistical heterogeneity and communication constraints induces a tradeoff between the ability of the users of each cluster to learn a proper model and the accuracy in aggregating these models into a global inference rule. We propose an algorithm based on multi-source adaptation methods for such communication-aware clustered FL scenarios which allows to balance these performance measures, and demonstrate its ability to achieve improved inference over conventional federated averaging without inducing additional communication overhead.
A Framework for Multi-Agent UAV Exploration and Target-Finding in GPS-Denied and Partially Observable Environments

The problem of multi-agent remote sensing for the purposes of finding survivors or surveying points of interest in GPS-denied and partially observable environments remains a challenge. This paper presents a framework for multi-agent target-finding using a combination of online POMDP based planning and Deep Reinforcement Learning based control. The framework is implemented considering planning and control as two separate problems. The planning problem is defined as a decentralised multi-agent graph search problem and is solved using a modern online POMDP solver. The control problem is defined as a local continuous-environment exploration problem and is solved using modern Deep Reinforcement Learning techniques. The proposed framework combines the solution to both of these problems and testing shows that it enables multiple agents to find a target within large, simulated test environments in the presence of unknown obstacles and obstructions. The proposed approach could also be extended or adapted to a number of time sensitive remote-sensing problems, from searching for multiple survivors during a disaster to surveying points of interest in a hazardous environment by adjusting the individual model definitions.
Using machine learning for predicting cervical cancer from Swedish electronic health records by mining hierarchical representations

Electronic health records (EHRs) contain rich documentation regarding disease symptoms and progression, but EHR data is challenging to use for diagnosis prediction due to its high dimensionality, relative scarcity, and substantial level of noise. We investigated how to best represent EHR data for predicting cervical cancer, a serious disease where early detection is beneficial for the outcome of treatment. A case group of 1321 patients with cervical cancer were matched to ten times as many controls, and for both groups several types of events were extracted from their EHRs. These events included clinical codes, lab results, and contents of free text notes retrieved using a LSTM neural network. Clinical events are described with great variation in EHR texts, leading to a very large feature space. Therefore, an event hierarchy inferred from the textual events was created to represent the clinical texts. Overall, the events extracted from free text notes contributed the most to the final prediction, and the hierarchy of textual events further improved performance. Four classifiers were evaluated for predicting a future cancer diagnosis where Random Forest achieved the best results with an AUC of 0.70 from a year before diagnosis up to 0.97 one day before diagnosis. We conclude that our approach is sound and had excellent discrimination at diagnosis, but only modest discrimination capacity before this point. Since our study objective was earlier disease prediction than such, we propose further work should consider extending patient histories through e.g. the integration of primary health records preceding referral to hospital.