Reviewerly/exHarmony huggingface dataset:
IB-GAN: A Unified Approach for Multivariate Time Series Classification under Class Imbalance

Classification of large multivariate time series with strong class imbalance is an important task in real-world applications. Standard methods of class weights, over-sampling, or parametric data augmentation do not always yield significant improvements for predicting minority classes of interest. Non-parametric data augmentation with Generative Adversarial Networks (GANs) offers a promising solution. We propose Imputation Balanced GAN (IB-GAN), a novel method that joins data augmentation and classification in a one-step process via an imputation-balancing approach. IB-GAN uses imputation and resampling techniques to generate higher quality samples from randomly masked vectors than from white noise, and augments classification through a class-balanced set of real and synthetic samples. Imputation hyperparameter pmiss allows for regularization of classifier variability by tuning innovations introduced via generator imputation. IB-GAN is simple to train and model-agnostic, pairing any deep learning classifier with a generator-discriminator duo and resulting in higher accuracy for under-observed classes. Empirical experiments on open-source UCR data and a 90K product dataset show significant performance gains against state-of-the-art parametric and GAN baselines.MSC codestime seriesimbalanced classificationdata augmentationmachine learninggenerative adversarial networks
Catch me if you can: effects of AR-enhanced presence on the mobile game experience

Purpose The purpose of the present research was to examine the effects of content, spatial, temporal and social presences stimulated by augmented reality (AR) technology on game enjoyment and continuing behavioral intention. Design/methodology/approach A total of 355 mobile AR game players participated in an online survey posted via the nationwide crowdsourcing web service in the US. A structural equation modeling was conducted using a maximum-likelihood estimation procedure to test the relationships among the variables. Findings Results of structural equation modeling revealed the mechanism through which multiple dimensions of presence on the mobile AR game generated positive effects on consumer responses and showed the effects of four dimensions of presence stimulated by the AR technology on game enjoyment, performance and behavioral intention. Content, spatial, temporal and social presences are integrated to create a sense of realness. These dimensions of presence simultaneously increased game enjoyment that influenced the perceived game performance, commitment to it and ultimately the intention to play other mobile AR games. Originality/value Although AR technology brings a unique experience to the game player, research on the effects of its use in mobile games on consumer responses is currently limited. The results of this study add value to the existing mobile game literature and provide practical insights for mobile game service providers on how to enhance players’ game enjoyment and continuing behavior.
Conceptual spaces and the strength of similarity-based arguments

Central to the conceptual spaces framework is the thought that concepts can be studied mathematically, by geometrical and topological means. Various applications of the framework have already been subjected to empirical testing, mostly with excellent results, demonstrating the framework's usefulness. So far untested is the suggestion that conceptual spaces may help explain certain inferences people are willing to make. The experiment reported in this paper focused on similarity-based arguments, testing the hypothesis that the strength of such arguments can be predicted from the structure of the conceptual space in which the items being reasoned about are represented. A secondary aim of the experiment concerned a recent inferentialist semantics for indicative conditionals, according to which the truth of a conditional requires the presence of a sufficiently strong inferential connection between its antecedent and consequent. To the extent that the strength of similarity-based inferences can be predicted from the geometry and topology of the relevant conceptual space, such spaces should help predict truth ratings of conditionals embodying a similarity-based inferential link. The results supported both hypotheses.
Distributed Mean-Field Density Estimation for Large-Scale Systems

This work studies how to estimate the mean-field density of large-scale systems in a distributed manner. Such problems are motivated by the recent swarm control technique that uses mean-field approximations to represent the collective effect of the swarm, wherein the mean-field density (especially its gradient) is usually used in feedback control design. In the first part, we formulate the density estimation problem as a filtering problem of the associated mean-field partial differential equation (PDE), for which we employ kernel density estimation (KDE) to construct noisy observations and use filtering theory of PDE systems to design an optimal (centralized) density filter. It turns out that the covariance operator of observation noise depends on the unknown density. Hence, we use approximations for the covariance operator to obtain a suboptimal density filter, and prove that both the density estimates and their gradient are convergent and remain close to the optimal one using the notion of input-to-state stability (ISS). In the second part, we continue to study how to decentralize the density filter such that each agent can estimate the mean-field density based on only its own position and local information exchange with neighbors. We prove that the local density filter is also convergent and remains close to the centralized one in the sense of ISS. Simulation results suggest that the centralized suboptimal density filter is able to generate convergent density estimates, and the local density filter is able to converge and remain close to the centralized filter.
Understanding UX Better: A New Technique to Go beyond Emotion Assessment

User experience (UX) is a quality aspect that considers the emotions evoked by the system, extending the usability concept beyond effectiveness, efficiency, and satisfaction. Practitioners and researchers are aware of the importance of evaluating UX. Thus, UX evaluation is a growing field with diverse approaches. Despite various approaches, most of them produce a general indication of the experience as a result and do not seek to capture the problem that gave rise to the bad UX. This information makes it difficult to obtain relevant results to improve the application, making it challenging to identify what caused a negative user experience. To address this gap, we developed a UX evaluation technique called UX-Tips. This paper presents UX-Tips and reports two empirical studies performed in an academic and an industrial setting to evaluate it. Our results show that UX-Tips had good performance in terms of efficiency and effectiveness, making it possible to identify the causes that led to a negative user experience, and it was easy to use. In this sense, we present a new technique suitable for use in both academic and industrial settings, allowing UX evaluation and finding the problems that may lead to a negative experience.
A Novel Facial Expression Recognition System using BMCSA Based Adaptive Neuro-Fuzzy Inference System

In Emotion Recognition (ER), automatic Facial Expression Recognitions (FER) is actually a dynamically emerging research. Recently, the focus is given to two main issues (i.e.) overfitting because of the devoid of adequate training data as well as expression-unassociated variations, for instance, head pose, illumination, along with identity bias. Here, Brownian motion based Crow Search Algorithm for Adaptive Neuro Fuzzy Inference System (BMCSA-ANFIS) is proposed for doing the FER of human images. It mostly concentrates on recognizing an individual’s Facial Expressions (FE) as of a single image. The proposed method comprises ‘4’ stages. Initially, the inputted facial images are taken from a database that is openly available. Then, the images (inputted) are pre-processed by performing filtering (noise removal) along with energy normalization. After that, the feature extraction is performed utilizing Local directional patterns (LDP) along with Appearances Model (AAM) centered Scale Invariants Features Transform (SIFT) that extracts the features as of the preprocessed images for doing FER. Then, the system utilizes the Information Gain based Linear Discriminants Analysis (IG-LDA) to lessen the extracted features (dimensionality reduction). Subsequent to Feature Reduction (FR), the reduced features are rendered to BMCSA-ANFIS for classification that classifies the result of the inputted images into ‘6’ fundamental emotions: happy, angry, disgust, surprise, sad, together with neutral by considering the reduced features. The experimentation’s outcomes are evaluated and that clearly exhibited that the proposed techniques perform superiorly to other existent algorithms for FER.
A Predictive Multi-Tenant Database Migration and Replication in the Cloud Environment

With the rapid adoption of multi-tenant databases, the cloud provider consolidates multiple tenants’ database on server machines, where the tenants share a common application and database instances. To ensure the quality of service (QoS) for the leased resources, both sides (i.e., the user and the provider) create a Service Level Agreement (SLA). Higher SLA violations result in high SLA contractual penalties and increase the possibility of losing the tenant. In addition, the unusual workload patterns of each tenant transactions require seamless adjustments due to the sudden burden changes and variability. As a result, to satisfy simultaneously availability and performance tenant requirements, it is necessary to perform reliable tenant migration and replication to distribute the workload to a flexible set of sites and avoid SLA violations. In this research, a cluster-based multi-tenant database management system (CB-MT DBMS) is proposed, which takes the migration and replication decisions in advance by monitoring and acting before the violation of the SLA occurs. In addition, a dynamic proactive multi-tenant database migration and replication MTDB-MR algorithm is proposed to reduce collisions and inconsistencies between migration and replication decisions for a group of violated tenants. Experimental results show that the proposed MTDB-MR algorithm is the ideal candidate for migration and replication of the violated multi-tenant databases, as it minimizes the total number of SLA violations, the number of multi-tenant clients SLA violations, client sites average response time and total execution time of each multi-tenant client site as compared to the previous algorithms.
Multi-Task Dense Retrieval via Model Uncertainty Fusion for Open-Domain Question Answering

Multi-task dense retrieval models can be used to retrieve documents from a common corpus (e.g., Wikipedia) for different opendomain question-answering (QA) tasks.However, Karpukhin et al. (2020) shows that jointly learning different QA tasks with one dense model is not always beneficial due to corpus inconsistency.For example, SQuAD only focuses on a small set of Wikipedia articles while datasets like NQ and Trivia cover more entries, and joint training on their union can cause performance degradation.To solve this problem, we propose to train individual dense passage retrievers (DPR) for different tasks and aggregate their predictions during test time, where we use uncertainty estimation as weights to indicate how probable a specific query belongs to each expert's expertise.Our method reaches state-of-the-art performance on 5 benchmark QA datasets, with up to 10% improvement in top-100 accuracy compared to a joint-training multi-task DPR on SQuAD.We also show that our method handles corpus inconsistency better than the joint-training DPR on a mixed subset of different QA datasets.
CodRED: A Cross-Document Relation Extraction Dataset for Acquiring Knowledge in the Wild

Existing relation extraction (RE) methods typically focus on extracting relational facts between entity pairs within single sentences or documents.However, a large quantity of relational facts in knowledge bases can only be inferred across documents in practice.In this work, we present the problem of crossdocument RE, making an initial step towards knowledge acquisition in the wild.To facilitate the research, we construct the first human-annotated cross-document RE dataset CodRED.Compared to existing RE datasets, CodRED presents two key challenges: Given two entities, (1) it requires finding the relevant documents that can provide clues for identifying their relations; (2) it requires reasoning over multiple documents to extract the relational facts.We conduct comprehensive experiments to show that CodRED is challenging to existing RE methods including strong BERT-based models.We make CodRED and the code for our baselines publicly available at https://github.com/thunlp/CodRED.
On the Profitability of Selfish Blockchain Mining Under Consideration of Ruin

Mining blocks on a blockchain equipped with a proof of work consensus protocol is well known to be resource consuming. A miner bears the operational cost, mainly electricity consumption and IT gear, of mining and is compensated by a capital gain when a block is discovered. This paper aims at quantifying the profitability of mining when the possible event of ruin is also considered. This is done by formulating a tractable stochastic model and using tools from applied probability and analysis, including the explicit solution of a certain type of advanced functional differential equation. The expected profit at a future time point is determined for the situation when the miner follows the protocol as well as when the miner withholds blocks. The obtained explicit expressions allow us to analyze the sensitivity with respect to the different model components and to identify conditions under which selfish mining is a strategic advantage.
A study on self-healing concrete

This research work is carried out to find the self-healing time and mechanical properties of concrete by using Bacillus Subtilis and Escherichia Coli. These bacterias has the capability of healing the cracks by itself. M20 concrete designed as per IS 10262–2009 is taken as reference sample. Different mixes were casted varying the percentage of bacteria. The above-mentioned bacteria were mixed in percentages of 2%, 3%, 4% and 6% by weight of cement separately and combination of both bacteria at 50% each to make total 2% and 3% of mixed bacteria by weight of cement. Calcium Lactate (a calcium-based nutrient for the bacteria) is mixed at a constant 5% for all mix variations. All the mixing, casting, curing and testing is done as per standard practice. Slump cone test gave an increased workability of 45% with bacteria Bacillus Subtilis. Standard size cubes of 150 mm were casted (nine for each mix), cured and tested at the age of 7, 14, 28 days. Some samples were tested in Compression testing machine till cracks were evident and measurable. The bacteria gets activated when water comes in contact with it, starts to feed on calcium lactate and grow resulting in self-healing of cracks in concrete. It is observed that the cracks were healed within 48 hrs for 2% Bacillus Subtilis, 32 hrs for 3% Bacillus Subtilis, and 72 hrs for 4% Bacillus Subtilis, even after 72 hrs no evident healing was observed for 6% Bacillus Subtilis all percentages of Escherichia Coli and mix bacteria. Hence the optimum dosage for self-healing of concrete is 3% Bacillus Subtilis. Both Compressive strength and Split Tensile strength, tests are done as per IS 516-2002 (reaffirmed 2018) and results gave an increase of 60%, strength for samples with 3% Escherichia Coli bacteria.
Relations and compositions between interval-valued complex fuzzy sets and applications for analysis of customers’ online shopping preferences and behavior

Analyzing the relations and patterns that exist in complex data sets is an integral part of the research in complex fuzzy set theory. The main object of study in this paper is the interval-valued complex fuzzy set (IV-CFS) model. This adaptation of complex fuzzy sets can handle datasets with a time-periodic feature, and the partial ignorance that exists in the data as well as the process of assigning values for the membership functions, in addition to modeling multi-dimensional data. This paper focuses on finding the patterns and relations between complex data sets using the properties of interval-valued complex fuzzy sets (IV-CFSs). To achieve this objective, this paper establishes the concept of relations and the composition operation for IV-CFSs using the extensive properties of the Cartesian product. Some of the algebraic properties of the relations and compositions are also introduced to define the equivalence relation between IV-CFSs. The proposed method is then applied to an MCDM problem related to customers’ online shopping preferences and behavior. A detailed case study of this MCDM problem is then presented through the interpretation of the results that were obtained. A brief comparison is then presented between our proposed method and other methods in literature used to analyze patterns between complex data sets.
Research on Optimization of Random Forest Algorithm Based on Spark

As society has developed, increasing amounts of data have been generated by various industries. The random forest algorithm, as a classification algorithm, is widely used because of its superior performance. However, the random forest algorithm uses a simple random sampling feature selection method when generating feature subspaces which cannot distinguish redundant features, thereby affecting its classification accuracy, and resulting in a low data calculation efficiency in the stand-alone mode. In response to the aforementioned problems, related optimization research was conducted with Spark in the present paper. This improved random forest algorithm performs feature extraction according to the calculated feature importance to form a feature subspace. When generating a random forest model, it selects decision trees based on the similarity and classification accuracy of different decision. Experimental results reveal that compared with the original random forest algorithm, the improved algorithm proposed in the present paper exhibited a higher classification accuracy rate and could effectively classify data.
Image Captioning with multi-level similarity-guided semantic matching

Image Captioning is a cross-modal task that needs to automatically generate coherent natural sentences to describe the image contents. Due to the large gap between vision and language modalities, most of the existing methods have the problem of inaccurate semantic matching between images and generated captions. To solve the problem, this paper proposes a novel multi-level similarity-guided semantic matching method for image captioning, which can fuse local and global semantic similarities to learn the latent semantic correlation between images and generated captions. Specifically, we extract the semantic units containing fine-grained semantic information of images and generated captions, respectively. Based on the comparison of the semantic units, we design a local semantic similarity evaluation mechanism. Meanwhile, we employ the CIDEr score to characterize the global semantic similarity. The local and global two-level similarities are finally fused using the reinforcement learning theory, to guide the model optimization to obtain better semantic matching. The quantitative and qualitative experiments on large-scale MSCOCO dataset illustrate the superiority of the proposed method, which can achieve fine-grained semantic matching of images and generated captions.
Building a Precision Medicine Delivery Platform for Clinics: The University of California, San Francisco, BRIDGE Experience

Despite an ever-expanding number of analytics with the potential to impact clinical care, the field currently lacks point-of-care technological tools that allow clinicians to efficiently select disease-relevant data about their patients, algorithmically derive clinical indices (eg, risk scores), and view these data in straightforward graphical formats to inform real-time clinical decisions. Thus far, solutions to this problem have relied on either bottom-up approaches that are limited to a single clinic or generic top-down approaches that do not address clinical users' specific setting-relevant or disease-relevant needs. As a road map for developing similar platforms, we describe our experience with building a custom but institution-wide platform that enables economies of time, cost, and expertise. The BRIDGE platform was designed to be modular and scalable and was customized to data types relevant to given clinical contexts within a major university medical center. The development process occurred by using a series of human-centered design phases with extensive, consistent stakeholder input. This institution-wide approach yielded a unified, carefully regulated, cross-specialty clinical research platform that can be launched during a patient's electronic health record encounter. The platform pulls clinical data from the electronic health record (Epic; Epic Systems) as well as other clinical and research sources in real time; analyzes the combined data to derive clinical indices; and displays them in simple, clinician-designed visual formats specific to each disorder and clinic. By integrating an application into the clinical workflow and allowing clinicians to access data sources that would otherwise be cumbersome to assemble, view, and manipulate, institution-wide platforms represent an alternative approach to achieving the vision of true personalized medicine.
SfM Flow: A comprehensive toolset for the evaluation of 3D reconstruction pipelines

We present SfM Flow, a Blender add-on that provides a toolset for the evaluation of three-dimensional reconstructions obtained form images. 3D reconstruction is increasingly becoming popular and, to date, many techniques are available to perform it. Choosing which technology to use for a specific 3D reconstruction task can be resource and time-consuming. By using this tool it is possible to create images of a virtual 3D scene, perform 3D reconstructions starting from the generated images using Structure from Motion pipelines, and evaluate the accuracy of the obtained 3D reconstruction. The evaluation is carried out comparing the 3D reconstruction with the virtual scene’s geometry, that constitutes an exact ground truth, without the need for complex setup and dedicated hardware for the acquisition of a real scene. Furthermore, SfM Flow includes support for different lighting, depth of field, and motion blur setups, thus allowing to stress the 3D reconstruction pipelines under common critical conditions.
Human Factors in Cybersecurity

Technological solutions in the mobile and digital era are becoming more helpful in informing the population, educational systems, monitoring, tracking the individuals, working, and spending time from home. On the other hand, the valuable information within such systems is posed to the risk of breaches at the individual and organizational level. As a result, cyber threats are constantly evolving. Many security incidents and data breaches are associated with the human factor. Respectively, this work highlights the importance of human factors in cybersecurity. Firstly, this article gives a brief overview of the topic and its significance. Then we present the most common risks in the cybersecurity field and their impacts. The third part emphasizes the role of human factors in security and elaborates on the behavioral approaches. Our conclusions are drawn in the last detail. To further our research, we plan to investigate behavioral science theories on understanding the influence of human factors in cybersecurity.