Reviewerly/exHarmony huggingface dataset:
Face Recognition Based on MTCNN and Integrated Application of FaceNet and LBP Method

Technology of face recognition has developed rapidly in the past three decades. Various face recognition methods have been proposed by a lot of research. The primary objective of the development of face recognition is improving the accuracy. Multi-task Cascaded Convolutional Networks (MTCNN) is an effective method to detect faces, which identifies the position of the face in the picture and marks five landmarks through deep Convolutional Neural Network (CNN). FaceNet is a technology of face recognition, which is also based on CNN technology, exhibit high accuracy. Local Binary Pattern (LBP) is a traditional technology of face recognition. Despite a lower accuracy than FaceNet, it has many advantages such as grayscale invariance and illumination insensitivity. In this research, we propose an enhanced model of face recognition which is based on MTCNN and integrated application of FaceNet and LBP method. The work that described in this article using LBP parallel FaceNet to improve the illumination robustness of the model only consists of MTCNN and FaceNet. Experiments show that the enhanced model is very effective in improving the illumination robustness.
Spherical World in Miniature: Exploring the Tiny Planets Metaphor for Discrete Locomotion in Virtual Reality

We explore the concept of a Spherical World in Miniature (SWIM) for discrete locomotion in Virtual Reality (VR). A SWIM wraps a planar WIM around a physically embodied sphere and thereby implements the metaphor of a tangible Tiny Planet that can be rotated and moved, enabling scrolling, scaling, and avatar teleportation. The scaling factor is set according to the sphere&#x0027;s distance from the head-mounted display (HMD), while rotation moves the current viewing window. Teleportation is triggered with a dwell time when looking at the sphere and keeping it still. In a lab study (N&#x003D;20), we compare our SWIM implementation to a planar WIM with an established VR controller technique using physical buttons. We test both concepts in a navigation task and also investigate the effects of two different screen sizes. Our results show that the SWIM, despite its less direct geometrical transformation, performed superior in most evaluations. It outperformed the planar WIM not only in terms of task completion time (TCT) and accuracy but also in subjective ratings.
Industry-Scale IR-Based Bug Localization: A Perspective from Facebook

We explore the application of Information Retrieval (IR) based bug localization methods at a large industrial setting, Facebook. Facebook's code base evolves rapidly, with thousands of code changes being committed to a monolithic repository every day. When a bug is detected, it is often time-sensitive and imperative to identify the commit causing the bug in order to either revert it or fix it. This is complicated by the fact that bugs often manifest with complex and unwieldy features, such as stack traces and other metadata. Code commits also have various features associated with them, ranging from developer comments to test results. This poses unique challenges to bug localization methods, making it a highly non-trivial operation. In this paper we lay out several practical concerns for industry-level IR-based bug localization, and propose Bug2Commit, a tool that is designed to address these concerns. We also assess the effectiveness of existing IR-based localization techniques from the software engineering community, and find that in the presence of complex queries or documents, which are common at Facebook, existing approaches do not perform as well as Bug2Commit. We evaluate Bug2Commit on three applications at Facebook: client-side crashes from the mobile app, server-side performance regressions, and mobile simulation tests for performance. We find that Bug2Commit outperforms the accuracy of existing approaches by up to 17%, leading to reduced time for triaging regressions and attributing bugs found in simulations.
BeSafe B2.0 Smart Multisensory Platform for Safety in Workplaces

Wearable technologies are becoming a profitable means of monitoring a person’s health state, such as heart rate and physical activity. The use of the smartwatch is becoming consolidated, not only as a novelty but also as a very useful tool for daily use. In addition, other devices, such as helmets or belts, are beneficial for monitoring workers and the early detection of any anomaly. They can provide valuable information, especially in work environments, where they help reduce the rate of accidents and occupational diseases, which makes them powerful Personal Protective Equipment (PPE). The constant monitoring of the worker’s health can be done in real-time, through temperature, falls, noise, impacts, or heart rate meters, activating an audible and vibrating alarm when an anomaly is detected. The gathered information is transmitted to a server in charge of collecting and processing it. In the first place, this paper provides an exhaustive review of the state of the art on works related to electronics for human activity behavior. After that, a smart multisensory bracelet, combined with other devices, developed a control platform that can improve operators’ security in the working environment. Artificial Intelligence and the Internet of Things (AIoT) bring together the information to improve safety on construction sites, power stations, power lines, etc. Real-time and historic data is used to monitor operators’ health and a hybrid system between Gaussian Mixture Model and Human Activity Classification. That is, our contribution is also founded on the use of two machine learning models, one based on unsupervised learning and the other one supervised. Where the GMM gave us a performance of 80%, 85%, 70%, and 80% for the 4 classes classified in real time, the LSTM obtained a result under the confusion matrix of 0.769, 0.892, and 0.921 for the carrying-displacing, falls, and walking-standing activities, respectively. This information was sent in real time through the platform that has been used to analyze and process the data in an alarm system.
CakeVR: A Social Virtual Reality (VR) Tool for Co-designing Cakes

Cake customization services allow clients to collaboratively personalize cakes with pastry chefs. However, remote (e.g., email) and in-person co-design sessions are prone to miscommunication, due to natural restrictions in visualizing cake size, decoration, and celebration context. This paper presents the design, implementation, and expert evaluation of a social VR application (CakeVR) that allows a client to remotely co-design cakes with a pastry chef, through real-time realistic 3D visualizations. Drawing on expert semi-structured interviews (4 clients, 5 pastry chefs), we distill and incorporate 8 design requirements into our CakeVR prototype. We evaluate CakeVR with 10 experts (6 clients, 4 pastry chefs) using cognitive walkthroughs, and find that it supports ideation and decision making through intuitive size manipulation, color/flavor selection, decoration design, and custom celebration theme fitting. Our findings provide recommendations for enabling co-design in social VR and highlight CakeVR's potential to transform product design communication through remote interactive and immersive co-design.
A Comparison of Java, Flutter and Kotlin/Native Technologies for Sensor Data-Driven Applications

As a result of the continuous progress and fast-growing popularity of mobile technologies in recent years, the demand for mobile applications has increased rapidly. One of the most important decisions that its developers have to make is the choice of technology on which their application will be based. This article is devoted to the comparison of Java, Flutter, and Kotlin/Native technologies for applications based on processing and analyzing data from sensors. The main elements of the comparison are the efficiency and resource utilization of mobile applications for Android OS implemented in each of the aforementioned technologies.
Margin-Based Adversarial Joint Alignment Domain Adaptation

Domain adaptation aims to transfer the knowledge learned from a labeled source domain to an unlabeled target domain, which has different data distribution with the source domain. Most of the existing methods focus on aligning the data distribution between the source and target domains but ignore the discrimination of the feature space among categories, leading the samples close to the decision boundary to be misclassified easily. To address the above issue, we propose a Margin-based Adversarial Joint Alignment (MAJA) to constrain the feature spaces of source and target domains to be aligned and discriminative. The proposed MAJA consists of two components: joint alignment module and margin-based generative module. The joint alignment module is proposed to align the source and target feature spaces by considering the joint distribution of features and labels. Therefore, the embedding features and the corresponding labels treated as pair data are applied for domain alignment. Furthermore, the margin-based generative module is proposed to boost the discrimination of the feature space, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.,</i> make all samples as far away from the decision boundary as possible. The margin-based generative module first employs the Generative Adversarial Networks (GAN) to generate a lot of fake images for each category, then applies the adversarial learning to enlarge and reduce the category margin for the true images and generated fake images, respectively. The evaluations on three benchmarks, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g.,</i> small image datasets, VisDA-2017, and Office-31, verify the effectiveness of the proposed method.
Augmenting Transfer Learning with Feature Extraction Techniques for Limited Breast Imaging Datasets

Computer aided detection (CADe) and computer aided diagnostic (CADx) systems are ongoing research areas for identifying lesions among complex inner structures with different pixel intensities, and for medical image classification. There are several techniques available for breast cancer detection and diagnosis using CADe and CADx systems. However, some of these systems are not accurate enough or suffer from lack of sufficient data. For example, mammography is the most commonly used breast cancer detection technique, and there are several CADe and CADx systems based on mammography, because of the huge dataset that is publicly available. But, the number of cancers escaping detection with mammography is substantial, particularly in dense-breasted women. On the other hand, digital breast tomosynthesis (DBT) is a new imaging technique, which alleviates the limitations of the mammography technique. However, the collections of huge amounts of the DBT images are difficult as it is not publicly available. In such cases, the concept of transfer learning can be employed. The knowledge learned from a trained source domain task, whose dataset is readily available, is transferred to improve the learning in the target domain task, whose dataset may be scarce. In this paper, a two-level framework is developed for the classification of the DBT datasets. A basic multilevel transfer learning (MLTL) based framework is proposed to use the knowledge learned from general non-medical image datasets and the mammography dataset, to train and classify the target DBT dataset. A feature extraction based transfer learning (FETL) framework is proposed to further improve the classification performance of the MLTL based framework. The FETL framework looks at three different feature extraction techniques to augment the MLTL based framework performance. The area under receiver operating characteristic (ROC) curve of value 0.89 is obtained, with just 2.08% of the source domain (non-medical) dataset, 5.09% of the intermediate domain (mammography) dataset, and 3.94% of the target domain (DBT) dataset, when compared to the dataset reported in literature.
Dynamic Point Cloud Compression Using A Cuboid Oriented Discrete Cosine Based Motion Model

Immersive media representation format based on point clouds has underpinned significant opportunities for extended reality applications. Point cloud in its uncompressed format require very high data rate for storage and transmission. The video based point cloud compression technique projects a dynamic point cloud into geometry and texture video sequences. The projected texture video is then coded using modern video coding standard like HEVC. Since the properties of projected texture video frames are different from traditional video frames, HEVC-based commonality modeling can be inefficient. An improved commonality modeling technique is proposed that employs discrete cosine basis oriented motion models and the domains of such models are approximated by homogeneous regions called cuboids. Experimental results show that the proposed commonality modeling technique can yield savings in bit rate of up to 4.17%.
Glinda: Supporting Data Science with Live Programming, GUIs and a Domain-specific Language

Researchers have explored several avenues to mitigate data scientists' frustrations with computational notebooks, including: (1) live programming, to keep notebook results consistent and up to date; (2) supplementing scripting with graphical user interfaces (GUIs), to improve ease of use; and (3) providing domain-specific languages (DSLs), to raise a script's level of abstraction. This paper introduces Glinda, which combines these three approaches by providing a live programming experience, with interactive results, for a domain-specific language for data science. The language's compiler uses an open-ended set of "recipes" to execute steps in the user's data science workflow. Each recipe is intended to combine the expressiveness of a written notation with the ease-of-use of a GUI. Live programming provides immediate feedback to a user's input, whether in the form of program edits or GUI gestures. In a qualitative evaluation with 12 professional data scientists, participants highly rated the live programming and interactive results. They found the language productive and sufficiently expressive and suggested opportunities to extend it.
Three-dimensional reconstruction of super-resolved white-light interferograms based on deep learning

White-light scanning interferometry is an effective and widely used technology for measuring the microscopic three-dimensional morphology of an object. Its vertical resolution can reach the sub-nanometer level, and its lateral resolution reaches submicron level. However, for the samples containing complex structure or high-density periodic distribution structural units, the measurement results are strongly restricted by magnification and numerical aperture (NA) of the microscopic objective. In this paper, we proposed a three-dimensional reconstruction algorithm for white-light interferograms after super resolution processing, using fast super-resolution convolutional neural networks (FSRCNN) to improve the detailed information of the interferograms, and then we used centroid method combined with the five-step phase-shift method to extract the zero optical path difference (ZOPD) position of the interference signal after super resolution processing. After processed by the proposed method, the interferograms collected by the 10X microscope objective (NA=0.3) recovered the 3D surface is the same as that measured by the 100X microscope objective (NA=0.7), which is proved by the experiment results.
Enhancing sentiment classification performance using hybrid Query Expansion Ranking and Binary Particle Swarm Optimization with Adaptive Inertia Weights

Machine learning-based sentiment classification is the best-performing method to understand public sentiment. However, the method has some problems, such as noisy features and high-dimensional feature space which affect the sentiment classification performance. To address the problems, this paper proposes a new feature selection using hybrid Query Expansion Ranking and Binary Particle Swarm Optimization with Adaptive Inertia Weights. The proposed method was validated using five tweet datasets on different topics both in Indonesian and English, and compared with state-of-the-art of filter and wrapper-based feature selection methods. Experimental results show the proposed method significantly improves sentiment classification performance and decrease computational time.
RNN Transducer Models for Spoken Language Understanding

We present a comprehensive study on building and adapting RNN transducer (RNN-T) models for spoken language understanding (SLU). These end-to-end (E2E) models are constructed in three practical settings: a case where verbatim transcripts are available, a constrained case where the only available annotations are SLU labels and their values, and a more restrictive case where transcripts are available but not corresponding audio. We show how RNN-T SLU models can be developed starting from pre-trained automatic speech recognition (ASR) systems, followed by an SLU adaptation step. In settings where real audio data is not available, artificially synthesized speech is used to successfully adapt various SLU models. When evaluated on two SLU data sets, the ATIS corpus and a customer call center data set, the proposed models closely track the performance of other E2E models and achieve state-of-the-art results.
Development of the bodily self: Effects of visuomotor synchrony and visual appearance on virtual embodiment in children and adults

The sense of a bodily self is thought to depend on adaptive weighting and integration of bodily afferents and prior beliefs. Evidence from studies using paradigms such as the rubber hand illusion and full body illusion suggests changes in the integration of visuotactile bodily signals throughout childhood. Here, we extended this line of research by assessing how bottom-up visuomotor synchrony and expectancy, modulated by visual appearance of virtual avatars, contribute to embodiment in children. We compared responses to a first-person perspective virtual full body illusion from 8- to 12-year-old children and adults while manipulating synchrony of the avatar's movements (synchronous, 0.5-s delay, or 1-s delay compared with the participant's movements) and appearance of the avatar (human or skeleton). We measured embodiment with both subjective questionnaires and objective skin conductance responses to virtual threat. Results showed that children experienced ownership for the virtual avatar in a similar way as adults, which was reduced with increasing asynchrony, and for the skeleton avatar as compared with the human avatar. This modulation of ownership was not reflected in the skin conductance responses, which were equally high in all experimental conditions and only showed a modulation of repetition by age. In contrast, in children the subjective experience of agency was less affected by the dampening effects of visuomotor asynchrony or reduced human likeness and was overall higher. These findings suggest that children can easily embody a virtual avatar but that different aspects of embodiment develop at different rates, which could have important implications for applications of embodied virtual reality.
The use of autonomous vehicles in transportation

The objective of this paper is to examine the types of autonomous vehicles already employed in transportation. It exemplifies the segments of transportation which already use autonomous vehicles. This paper gives an insight of the places in the world where autonomous vehicles are put in use. Furthermore, companies such as Volvo, Volkswagen, Airbus and WAYMO have showed their models of driverless vehicles ranging from trucks, buses, shuttles and helicopters. Moreover, the paper provides a description, testing methods, use and features of the each vehicle presented. In short, this paper aims to raise consciousness about the future of not just public transportation but also transportation of any other type such as good's transportation, delivery, vehicles provided for sightseeing, aircrafts etc.
Gospels of Modernity: Digital Cattle Markets, Urban Religiosity, and Secular Computing in the Global South

This paper joins the growing body of critical HCI work that studies the digitization of the Global South and reports the elements of ‘secularization’ in it. Based on a year-long ethnography on the contemporary transformations in religious practices in Dhaka, Bangladesh, this paper presents how the emerging “digital” cattle marketplaces subdue various forms of traditional manifestations of urban religiosity during Eid-ul-Adha, the second-largest Islamic festival in the city. This paper further depicts how such secularization contributes to diminishing rural-urban linkages, affecting electoral politics, and reducing the tolerance to religious celebrations in a city. Drawing from a rich body of work in critical urban studies, postcolonial computing, and sociology of religions, we explain how such oft-overlooked embedding of secularization in computing affects the religious fabrics in the urban regions of the Global South, and discuss its implication for HCI scholarship in diversity, inclusion, and development.
D2MIF: A Malicious Model Detection Mechanism for Federated-Learning-Empowered Artificial Intelligence of Things

Artificial Intelligence of Things (AIoT), as a fusion of artificial intelligence (AI) and Internet of Things (IoT), has become a new trend to realize the intelligentization of industry 4.0 and the data privacy and security is the key to its successful implementation. To enhance data privacy protection, the federated learning has been introduced in AIoT, which allows participants to jointly train AI models without sharing private data. However, in federated learning, malicious participants might provide malicious models by launching the poisoning attack, which will jeopardize the convergence and accuracy of the global model. To solve this problem, we propose a malicious model detection mechanism based on the isolation forest (iforest), named D2MIF, for the federated learning-empowered AIoT. In D2MIF, an iforest is constructed to compute the malicious score for each model uploaded by the corresponding participant, and then, the models will be filtered if their malicious scores are higher than the threshold, which is dynamically adjusted using reinforcement learning (RL). The validation experiment is conducted on two public data sets Mnist and Fashion_Mnist. The experimental results show that the proposed D2MIF can effectively detect malicious models and significantly improve the global model accuracy in federated learning-empowered AIoT.
A User-Oriented Approach to Space-Adaptive Augmentation

Space-adaptive algorithms aim to effectively align the virtual with the real to provide immersive user experiences for Augmented Reality(AR) content across various physical spaces. While such measures are reliant on real spatial features, efforts to understand those features from the user's perspective and reflect them in designing adaptive augmented spaces have been lacking. For this, we compared factors of narrative experience in six spatial conditions during the gameplay of Fragments, a space-adaptive AR detective game. Configured by size and furniture layout, each condition afforded disparate degrees of traversability and visibility. Results show that whereas centered furniture clusters are suitable for higher presence in sufficiently large rooms, the same layout leads to lower narrative engagement. Based on our findings, we suggest guidelines that can enhance the effects of space adaptivity by considering how users perceive and navigate augmented space generated from different physical environments.