Reviewerly/exHarmony huggingface dataset:
Novel XGBoost Tuned Machine Learning Model for Software Bug Prediction

As internet users grow, the quantity of data available on the web increases with it. Virtually everything that needs human effort or human presence can be replaced by the Software. While developing an application it follows the Software Development Lifecycle (SDLC). Within the early stages of development, it's a compulsory task to take care of system or bugs to avoid wasting time and effort during initial development phase to forestall any runtime crisis. In this paper, we used the machine learning models – Logistic regression, Decision Tree, Random Forest, Adaboost and XGBoost as state-of-art models for four datasets of NASA-KC2, PC3, JM1, CM1. Later on, new model was proposed based on tuning the existing XGBoost model by changing its parameter namely N_estimator, learning rate, max depth, and subsample. The results achieved were compared with state-of-art models and our model outperformed them for all datasets. The authors believe that this research will contribute in correctly detecting the bugs with machine learning approach.
Assessing the Impact of a Construction Virtual Reality Game on Design Review Skills of Construction Students

Being able to perform an effective design review is a fundamental skill that every design or construction student should possess. Performing a design review requires critical thinking, analysis, and communication skills. To support the acquisition of these skills, this study investigated the impact of a virtual reality game, the design review simulator (DRS), in a classroom environment. To assess the impact of the game, the authors performed a crossover experiment with 120 students. The students were split into groups and were asked to perform design reviews in two treatment conditions, 2D drawings or the DRS. A knowledge test was administered at the end of different treatments to collect quantitative data on the students' performance. Results from this research indicate that the DRS supported students in identifying a higher number of design mistakes. Furthermore, students that played with the DRS after performing 2D drawings reviews scored significantly higher in the knowledge test. The research results contribute to the growing knowledge base on the implementation of VR in the classroom and in supporting students in meeting learning objectives related to design review skills. In particular, the research illustrated that the benefits of VR found in the construction industry in terms of improved communication, user involvement, and feedback collection could be translated into the classroom environment.
HeadBlaster

We present HeadBlaster, a novel wearable technology that creates motion perception by applying ungrounded force to the head to stimulate the vestibular and proprioception sensory systems. Compared to motion platforms that tilt the body, HeadBlaster more closely approximates how lateral inertial and centrifugal forces are felt during real motion to provide more persistent motion perception. In addition, because HeadBlaster only actuates the head rather than the entire body, it eliminates the mechanical motion platforms that users must be constrained to, which improves user mobility and enables room-scale VR experiences. We designed a wearable HeadBlaster system with 6 air nozzles integrated into a VR headset, using compressed air jets to provide persistent, lateral propulsion forces. By controlling multiple air jets, it is able to create the perception of lateral acceleration in 360 degrees. We conducted a series of perception and human-factor studies to quantify the head movement, the persistence of perceived acceleration, and the minimal level of detectable forces. We then explored the user experience of HeadBlaster through two VR applications: a custom surfing game, and a commercial driving simulator together with a commercial motion platform. Study results showed that HeadBlaster provided significantly longer perceived duration of acceleration than motion platforms. It also significantly improved realism and immersion, and was preferred by users compared to using VR alone. In addition, it can be used in conjunction with motion platforms to further augment the user experience.
Security embedded dynamic resource allocation model for cloud data centre

Cloud clients face high security risks while sharing physical resources with multiple users. A malicious cloud user exploits co-residency and hypervisor vulnerabilities to steal and hamper sensitive information of victim virtual machine (VM). To address this alarming issue, this Letter proposes a secure resource allocation model by developing a threat detector based on the analysis of co-located inter-VM relations and a workload predictor in the background. The anomalous network traffic, response speed, bandwidth usage and illegal inter-VM links are considered as security breaches indicator that assists in future threat detection beforehand and consequently guides the secure and resource efficient VM allocation. The comparison of proposed model with state-of-the-art: FCFS, Random-fit and NSGA-II based resource allocation approaches indicate that it significantly reduce security threats by 77.38% and number of active servers up to 5.39% with improved resource utilisation up to 26.84% over Random-fit.
Empowering learning process in secondary education using pervasive technologies

In this paper we present an approach to employing pervasive technologies, such as IoT and mobile technologies, in secondary education. The goal is to develop a comprehensive methodology, IoT infrastructure and a mobile application that would enable secondary students to test their knowledge in interaction with a smart environment. We have developed a series of assignments that students solve using the Android application empowered with augmented reality that interacts with various QR codes, sensors, and actuators placed in a smart classroom. The evaluation of the developed system was done with the students of the Secondary School of Economics in Belgrade, Serbia. Students from the experimental group, after solving assignments using the developed mobile application in interaction with the smart learning environment, have tested their knowledge via an IoT test and filled out a questionnaire, while students in the control group solved the test in a traditional way. The results show that it is possible to increase the level of students’ knowledge by integrating IoT and mobile technologies in the learning process. In addition, the results show that students are ready and interested in using pervasive technologies within their formal education.
Unsupervised feature selection based on adaptive similarity learning and subspace clustering

Feature selection methods have an important role on the readability of data and the reduction of complexity of learning algorithms. In recent years, a variety of efforts are investigated on feature selection problems based on unsupervised viewpoint due to the laborious labeling task on large datasets. In this paper, we propose a novel approach on unsupervised feature selection initiated from the subspace clustering to preserve the similarities by representation learning of low dimensional subspaces among the samples. A self-expressive model is employed to implicitly learn the cluster similarities in an adaptive manner. The proposed method not only maintains the sample similarities through subspace clustering, but it also considers the underlying structure of data based on a regularized regression model. In line with the convergence analysis of the proposed method, the experimental results on benchmark datasets demonstrate the effectiveness of our approach as compared with the state-of-the-art methods.
Learning Cross-Modal Common Representations by Private–Shared Subspaces Separation

Due to the inconsistent distributions and representations of different modalities (e.g., images and texts), it is very challenging to correlate such heterogeneous data. A standard solution is to construct one common subspace, where the common representations of different modalities are generated to bridge the heterogeneity gap. Existing methods based on common representation learning mostly adopt a less effective two-stage paradigm: first, generating separate representations for each modality by exploiting the modality-specific properties as the complementary information, and then capturing the cross-modal correlation in the separate representations for common representation learning. Moreover, these methods usually neglect that there may exist interference in the modality-specific properties, that is, the unrelated objects and background regions in images or the noisy words and incorrect sentences in the text. In this article, we hypothesize that explicitly modeling the interference within each modality can improve the quality of common representation learning. To this end, we propose a novel model private-shared subspaces separation (P3S) to explicitly learn different representations that are partitioned into two kinds of subspaces: 1) the common representations that capture the cross-modal correlation in a shared subspace and 2) the private representations that model the interference within each modality in two private subspaces. By employing the orthogonality constraints between the shared subspace and the private subspaces during the one-stage joint learning procedure, our model is able to learn more effective common representations for different modalities in the shared subspace by fully excluding the interference within each modality. Extensive experiments conducted on cross-modal retrieval verify the advantages of our P3S method compared with 15 state-of-the-art methods on four widely used cross-modal datasets.
Unsupervised Anomaly Detection Using K-Means, Local Outlier Factor and One Class SVM

Anomaly detection is one area that is still being researched today. An anomaly that occurs in data can be utilized in various ways, such as detection of money embezzlement, increasing production, improving the quality of data, preventing attacks on a network and others. This study aims to find anomalies in the data drug use in hospitals consisting of two datasets using the K-Means algorithm, Local Outlier Factor (LOF) and One Class Support Vector Machine (OC-SVM). The results of this study are that the three algorithms can find outliers. Based on its performance in both datasets, OC-SVM outperforms LOF and K-Means.
Bounding Regression Errors in Data-Driven Power Grid Steady-State Models

Data-driven models analyze power grids under incomplete physical information, and their accuracy has been mostly validated empirically using certain training and testing datasets. This paper explores error bounds for data-driven models under all possible training and testing scenarios drawn from an underlying distribution, and proposes an evaluation implementation based on Rademacher complexity theory. We answer critical questions for data-driven models: how much training data is required to guarantee a certain error bound, and how partial physical knowledge can be utilized to reduce the required amount of data. Different from traditional Rademacher complexity that mainly addresses classification problems, our method focuses on regression problems and can provide a tighter bound. Our results are crucial for the evaluation and application of data-driven models in power grid analysis. We demonstrate the proposed method by finding generalization error bounds for two applications, i.e., branch flow linearization and external network equivalent under different degrees of physical knowledge. Results identify how the bounds decrease with additional power grid physical knowledge or more training data.
Lazy fine-tuning algorithms for naïve Bayesian text classification

The naïve Bayes (NB) learning algorithm is widely applied in many fields, particularly in text classification. However, its performance decreases when it is used in domains where its naïve assumption is violated or when the training set is too small to find accurate estimations of the probabilities. In this study, we propose a lazy fine-tuning naïve Bayes (LFTNB) method to address both problems. We propose a local fine-tuning algorithm that uses the nearest neighbors of a query instance to fine-tune the probability terms used by NB. Applying the nearest neighbors only makes the independence assumption more likely to be valid, whereas the fine-tuning algorithm is used to find more accurate estimations of the probability terms. The performance of the LFTNB approach was evaluated using 47 UCI datasets. The results show that the LFTNB method achieves superior performance than classical NB, eager FTNB, and k-nearest neighbor algorithms. We also propose eager and lazy fine-tuning versions of powerful NB-based text classification algorithms, namely, multinomial NB, complement NB, and one-versus-all NB. The empirical results using 18 UCI text classification datasets show that the proposed methods outperform untuned versions of these algorithms.
TinyGNN: Learning Efficient Graph Neural Networks

Recently, Graph Neural Networks (GNNs) arouse a lot of research interest and achieve great success in dealing with graph-based data. The basic idea of GNNs is to aggregate neighbor information iteratively. After k iterations, a k-layer GNN can capture nodes' k-hop local structure. In this way, a deeper GNN can access much more neighbor information leading to better performance. However, when a GNN goes deeper, the exponential expansion of neighborhoods incurs expensive computations in batched training and inference. This takes the deeper GNN away from many applications, e.g., real-time systems. In this paper, we try to learn a small GNN (called TinyGNN), which can achieve high performance and infer the node representation in a short time. However, since a small GNN cannot explore as much local structure as a deeper GNN does, there exists a neighbor information gap between the deeper GNN and the small GNN. To address this problem, we leverage peer node information to model the local structure explicitly and adopt a neighbor distillation strategy to learn local structure knowledge from a deeper GNN implicitly. Extensive experimental results demonstrate that TinyGNN is empirically effective and achieves similar or even better performance compared with the deeper GNNs. Meanwhile, TinyGNN gains a 7.73x--126.59x speed-up on inference over all data sets.
Domain Generalization for Named Entity Boundary Detection via Metalearning

Named entity recognition (NER) aims to recognize mentions of rigid designators from text belonging to predefined semantic types, such as person, location, and organization. In this article, we focus on a fundamental subtask of NER, named entity boundary detection, which aims at detecting the start and end boundaries of an entity mention in the text, without predicting its semantic type. The entity boundary detection is essentially a sequence labeling problem. Existing sequence labeling methods either suffer from sparse boundary tags (i.e., entities are rare and nonentities are common) or they cannot well handle the issue of variable size output vocabulary (i.e., need to retrain models with respect to different vocabularies). To address these two issues, we propose a novel entity boundary labeling model that leverages pointer networks to effectively infer boundaries depending on the input sequence. On the other hand, training models on source domains that generalize to new target domains at the test time are a challenging problem because of the performance degradation. To alleviate this issue, we propose Metabdry, a novel domain generalization approach for entity boundary detection without requiring any access to target domain information. Especially, adversarial learning is adopted to encourage domain-invariant representations. Meanwhile, metalearning is used to explicitly simulate a domain shift during training so that metaknowledge from multiple resource domains can be effectively aggregated. As such, Metabdry explicitly optimizes the capability of “learning to generalize,” resulting in a more general and robust model to reduce the domain discrepancy. We first conduct experiments to demonstrate the effectiveness of our novel boundary labeling model. We then extensively evaluate Metabdry on eight data sets under domain generalization settings. The experimental results show that Metabdry achieves state-of-the-art results against the recent seven baselines.
Calendar Graph Neural Networks for Modeling Time Structures in Spatiotemporal User Behaviors

User behavior modeling is important for industrial applications such as demographic attribute prediction, content recommendation, and target advertising. Existing methods represent behavior log as a sequence of adopted items and find sequential patterns; however, concrete location and time information in the behavior log, reflecting dynamic and periodic patterns, joint with the spatial dimension, can be useful for modeling users and predicting their characteristics. In this work, we propose a novel model based on graph neural networks for learning user representations from spatiotemporal behavior data. Our model's architecture incorporates two networked structures. One is a tripartite network of items, sessions, and locations. The other is a hierarchical calendar network of hour, week, and weekday nodes. It first aggregates embeddings of location and items into session embeddings via the tripartite network, and then generates user embeddings from the session embeddings via the calendar structure. The user embeddings preserve spatial patterns and temporal patterns of a variety of periodicity (e.g., hourly, weekly, and weekday patterns). It adopts the attention mechanism to model complex interactions among the multiple patterns in user behaviors. Experiments on real datasets (i.e., clicks on news articles in a mobile app) show our approach outperforms strong baselines for predicting missing demographic attributes.
A Fast FoV-Switching DASH System Based on Tiling Mechanism for Practical Omnidirectional Video Services

With the development of multimedia technologies and virtual reality display devices, omnidirectional videos have gained popularity nowadays. To reduce the bandwidth requirement for omnidirectional video transmission, tile-based viewport adaptive streaming methods have been proposed in the literatures. Challenges related to decoding the tiles simultaneously with limited number of decoders, and ensuring user's viewing experience during the viewport switch are still to be solved. In this paper, a two-layer fast viewport switching dynamic adaptive streaming over HTTP (DASH) system based on tiling mechanism is proposed, which incorporates the viewing trajectory of end users. To deal with the simultaneously decoding problem, an open group of picture (GOP) technique is proposed to enable merging different types of tiles into a composite stream at the client side. To reduce the quality recovery duration after the viewport change, a fast-switching strategy is also proposed. Moreover, considering the priorities of different types of chunks, a download strategy is further proposed to adapt the bandwidth fluctuations and viewport changes. Experimental results showed that the proposed system can significantly reduce the recovery duration of high quality video by approximately 90%, which can provide a better viewing experience to end users.
Integrating UX work with agile development through user stories: An action research study in a small software company

The integration of user experience (UX) work with agile software development has been addressed in extensive research of challenges and process models. However, in-depth research of context-specific improvements of this integration with actual UX professionals and agile developers in their actual practice is limited. This study examines how the integration of UX work with agile development can be improved in the context of a small Danish Software-as-a-Service (SaaS) company. We used the problem- and solution-oriented action research method over 12 months in the company. During this period, we initially carried out extensive participant observations, recorded 32 semi-structured interviews, and finally conducted two improvement iterations with evaluations of their effect on agility. We identified user stories as an essential indicator of UX integration. Verbose user stories imply problems in collaboration and trust, while concise user stories and deliberation improve integration of UX work with agile development. The conclusion is that integrating UX work with agile development in practice is complex, contextualized, and difficult even for only a small part of it. We propose that concise user stories and deliberation can be useful and well-defined focuses for integrating UX work with agile software development without sacrificing their agility.
Improving Deep Learning for Airbnb Search

The application of deep learning to search ranking was one of the most impactful product improvements at Airbnb. But what comes next after you launch a deep learning model? In this paper we describe the journey beyond, discussing what we refer to as the ABCs of improving search: A for architecture, ℬ for bias and ℂ for cold start. For architecture, we describe a new ranking neural network, focusing on the process that evolved our existing DNN beyond a fully connected two layer network. On handling positional bias in ranking, we describe a novel approach that led to one of the most significant improvements in tackling inventory that the DNN historically found challenging. To solve cold start, we describe our perspective on the problem and changes we made to improve the treatment of new listings on the platform. We hope ranking teams transitioning to deep learning will find this a practical case study of how to iterate on DNNs.
Analytic Study of Double Descent in Binary Classification: The Impact of Loss

Extensive empirical evidence reveals that, for a wide range of different learning methods and data sets, the risk curve exhibits a double-descent (DD) trend as a function of the model size. In our recent coauthored paper [Deng et al., '19], we proposed simple binary linear classification models and showed that the test error of gradient descent (GD) with logistic loss undergoes a DD. In this paper, we complement these results by extending them to GD with square loss. We show that the DD phenomenon persists, but we also identify several differences compared to logistic loss. This emphasizes that crucial features of DD curves (such as their transition threshold and global minima) depend both on the training data and on the learning algorithm. We further study the dependence of DD curves on the size of the training set. Similar to [Deng et al., '19] our results are analytic: we plot the DD curves by first deriving sharp asymptotics for the test error under Gaussian features. Albeit simple, the models permit a principled study, the outcomes of which theoretically corroborate related empirical findings occurring in more complex learning tasks.