Reviewerly/exHarmony huggingface dataset:
UprightNet: Geometry-Aware Camera Orientation Estimation From Single Images

We introduce UprightNet, a learning-based approach for estimating 2DoF camera orientation from a single RGB image of an indoor scene. Unlike recent methods that leverage deep learning to perform black-box regression from image to orientation parameters, we propose an end-to-end framework that incorporates explicit geometric reasoning. In particular, we design a network that predicts two representations of scene geometry, in both the local camera and global reference coordinate systems, and solves for the camera orientation as the rotation that best aligns these two predictions via a differentiable least squares module. This network can be trained end-to-end, and can be supervised with both ground truth camera poses and intermediate representations of surface geometry. We evaluate UprightNet on the single-image camera orientation task on synthetic and real datasets, and show significant improvements over prior state-of-the-art approaches.
Primal–dual accelerated gradient methods with small-dimensional relaxation oracle

In this paper, a new variant of accelerated gradient descent is proposed. The proposed method does not require any information about the objective function, uses exact line search for the practical accelerations of convergence, converges according to the well-known lower bounds for both convex and non-convex objective functions, possesses primal–dual properties and can be applied in the non-euclidian set-up. As far as we know this is the first such method possessing all of the above properties at the same time. We also present a universal version of the method which is applicable to non-smooth problems. We demonstrate how in practice one can efficiently use the combination of line-search and primal-duality by considering a convex optimization problem with a simple structure (for example, linearly constrained).
Attentional Kernel Encoding Networks for Fine-Grained Visual Categorization

Fine-grained visual categorization aims to recognize objects from different sub-ordinate categories, which is a challenging task due to subtle visual differences between images. It is highly desired to identify discriminative regions while achieving highly non-linear compact representation for fine-grained visual categorization. However, existing methods either rely on manually defined part-based annotations to indicate the distinctive regions or operate on longitudinal vectors to capture the non-linear information, which may lose important spatial layout information. In this paper, we propose the Attentional Kernel Encoding Networks (AKEN) for fine-grained visual categorization. Specifically, the AKEN aggregates feature maps from the last convolutional layer of ConvNets to obtain a holistic feature representation. By Fourier embedding, it encodes features from both the longitudinal and transverse directions, which largely retains the spatial layout information. Moreover, we incorporate a Cascaded Attention (Cas-Attention) module to highlight local regions that distinguish among subordinate categories, enabling the AKEN to extract the most discriminative features. Working in conjunction with the attention mechanism, the proposed AKEN combines the strengths of ConvNets and kernels for non-linear feature learning, which can establish discriminative and descriptive feature representations for fine-grained image categorization. Experiments on three benchmark datasets show that the proposed AKEN delivers highly competitive performance, surpassing most existed methods and achieving state-of-the-art results.
Hailstorm

Distributed LSM-based databases face throughput and latency issues due to load imbalance across instances and interference from background tasks such as flushing, compaction, and data migration. Hailstorm addresses these problems by deploying the database storage engines over a distributed filesystem that disaggregates storage from processing, enabling storage pooling and compaction offloading. Hailstorm pools storage devices within a rack, allowing each storage engine to fully utilize the aggregate rack storage capacity and bandwidth. Storage pooling successfully handles load imbalance without the need for resharding. Hailstorm offloads compaction tasks to remote nodes, distributing their impact, and improving overall system throughput and response time. We show that Hailstorm achieves load balance in many MongoDB deployments with skewed workloads, improving the average throughput by 60%, while decreasing tail latency by as much as 5X. In workloads with range queries, Hailstorm provides up to 22X throughput improvements. Hailstorm also enables cost savings of 47-56% in OLTP workloads.
Improvement of oral cancer screening quality and reach: The promise of artificial intelligence

Abstract Oral cancer is easily detectable by physical (self) examination. However, many cases of oral cancer are detected late, which causes unnecessary morbidity and mortality. Screening of high‐risk populations seems beneficial, but these populations are commonly located in regions with limited access to health care. The advent of information technology and its modern derivative artificial intelligence (AI) promises to improve oral cancer screening but to date, few efforts have been made to apply these techniques and relatively little research has been conducted to retrieve meaningful information from AI data. In this paper, we discuss the promise of AI to improve the quality and reach of oral cancer screening and its potential effect on improving mortality and unequal access to health care around the world.
Diversified Third-Party Library Prediction for Mobile App Development

The rapid growth of mobile apps has significantly promoted the use of third-party libraries in mobile app development. However, mobile app developers are now facing the challenge of finding useful third-party libraries for improving their apps, e.g., to enhance user interfaces, to add social features, etc. An effective approach is to leverage collaborative filtering (CF) to predict useful third-party libraries for developers. We employed Matrix Factorization (MF) approaches - the classic CF-based prediction approaches - to make the predictions based on a total of 31,432 Android apps from Google Play. However, our investigation shows that there is a significant lack of diversity in the prediction results - a small fraction of popular third-party libraries dominate the prediction results while most other libraries are ill-served. The low diversity in the prediction results limits the usefulness of the prediction because it lacks novelty and serendipity which are much appreciated by mobile app developers. In order to increase the diversity in the prediction results, we designed an innovative MF-based approach, namely LibSeek, specifically for predicting useful third-party libraries for mobile apps. It employs an adaptive weighting mechanism to neutralize the bias caused by the popularity of third-party libraries. In addition, it introduces neighborhood information, i.e., information about similar apps and similar third-party libraries, to personalize the predictions for individual apps. The experimental results show that LibSeek can significantly diversify the prediction results, and in the meantime, increase the prediction accuracy.
A Fuzzy Adaptive Dynamic NSGA-II With Fuzzy-Based Borda Ranking Method and its Application to Multimedia Data Analysis

In this article, a novel fuzzy-based dynamic multiobjective evolutionary algorithm is presented. In this article, giving a valid and true response to the change is an essential task to improve the diversity of solutions when an environmental change occurs. The basic idea is to randomly remove some solutions and replace by newly created solutions. However, the random selection detours the algorithm's trajectory and deteriorates the performance of the optimization algorithm. Recently, the Borda method has been deployed to find the best candidates to be removed from the solutions list. Although the Borda method outperforms the random strategy, it suffers from some drawbacks. In this article, we propose an improved Borda count method incorporated with fuzzy tuned parameters so that its parameters are adjusted by Mamdani fuzzy rules. Our new Borda method can distinguish the information before and after change with different fuzzy weights. In addition to the fuzzy-based Borda, we employ an improved evolutionary algorithm based on fuzzy logic. We propose a novel nondominated sorting genetic algorithm with its parameters tuned with fuzzy rules so that it is adapted to the new environment. Experiments are conducted on standard benchmarks and the results are compared with recent algorithms. Then, multimedia data analysis, such as segmentation of moving objects, is experimented as a dynamic multiobjective problem and solved by the proposed algorithm.
Continual Multiview Task Learning via Deep Matrix Factorization

The state-of-the-art multitask multiview (MTMV) learning tackles a scenario where multiple tasks are related to each other via multiple shared feature views. However, in many real-world scenarios where a sequence of the multiview task comes, the higher storage requirement and computational cost of retraining previous tasks with MTMV models have presented a formidable challenge for this lifelong learning scenario. To address this challenge, in this article, we propose a new continual multiview task learning model that integrates deep matrix factorization and sparse subspace learning in a unified framework, which is termed deep continual multiview task learning (DCMvTL). More specifically, as a new multiview task arrives, DCMvTL first adopts a deep matrix factorization technique to capture hidden and hierarchical representations for this new coming multiview task while accumulating the fresh multiview knowledge in a layerwise manner. Then, a sparse subspace learning model is employed for the extracted factors at each layer and further reveals cross-view correlations via a self-expressive constraint. For model optimization, we derive a general multiview learning formulation when a new multiview task comes and apply an alternating minimization strategy to achieve lifelong learning. Extensive experiments on benchmark data sets demonstrate the effectiveness of our proposed DCMvTL model compared with the existing state-of-the-art MTMV and lifelong multiview task learning models.
Semi-Supervised Clustering With Constraints of Different Types From Multiple Information Sources

Semi-supervised clustering is one of important research topics in cluster analysis, which uses pre-given knowledge as constraints to improve the clustering performance. While clustering a data set, people often get prior constraints from different information sources, which may have different representations and contents, to guide clustering process. However, most of existing semi-supervised clustering algorithms are based on single-source constraints and rarely consider to integrate multi-source constraints to enhance the clustering quality. To solve the problem, we analyze the relations among different types of constraints and propose an uniform representation for them. Based it, we propose a new semi-supervised clustering algorithm to find out a clustering that has good cluster structure and high consensus of all the sources of constraints. In the algorithm, we construct an optimization objective model and its solution method to achieve the aim. This algorithm can integrate multi-source constraints well to reduce the effect of incorrect constraints from single sources and find out a high-quality clustering. By the experimental studies on several benchmark data sets, we illustrate the effectiveness of the proposed algorithm, compared to other semi-supervised clustering algorithms.
Attendance System Based on Face Recognition System Using CNN-PCA Method and Real-time Camera

One of the developments in computer vision is the research on human face recognition. One of the implementations of the human face recognition system is used as an attendance system. The attendance system uses faces as objects to be detected and recognized as a person's identity and then stored as a face database. The process of matching face image data captured by the camera with face images that have been stored in the face database will result in face identification of the object faces captured by the camera. The face recognition-based attendance system in this study uses a hybrid feature extraction method using CNN-PCA (Convolutional Neural Network - Principal Component Analysis). This combination of methods is intended to produce a more accurate feature extraction method. The face recognition-based attendance system using this camera is very effective and efficient to further improve the accuracy of user data. This face recognition-based attendance system using this camera has very accurate data processing and high accuracy so that it can produce a system that is reliable and powerful to identify human faces in real-time.
Unsupervised Learning in Reservoir Computing for EEG-Based Emotion Recognition

In real-world applications such as emotion recognition from recorded brain activity, data are captured from electrodes over time. These signals constitute a multidimensional time series. In this article, Echo State Network (ESN), a recurrent neural network with great success in time series prediction and classification, is optimized with different neural plasticity rules for classification of emotions based on electroencephalogram (EEG) time series. The developed network could automatically extract valid features from EEG signals. We use the filtered signals as the network input and do not take any feature extraction methods. Evaluated on two well-known benchmarks, the DEAP dataset, and the SEED dataset, the performance of the ESN with intrinsic plasticity greatly outperforms the feature-based methods and shows certain advantages compared with other existing methods. Thus, the proposed network can form a more complete and efficient representation, whilst retaining the advantages such as faster learning speed and more reliable performance.
Detection method of manufacturing defects on aircraft surface based on fringe projection

For high-value aircraft, in order to ensure their stealth, pneumatic and safety characteristics, surface geometric defects such as unacceptable rivet height and seam width must be accurately detected during the manufacturing process. These defects need to be controlled within a very small scope of error. Traditional defect detection methods are difficult to meet the actual requirements of manufacturing of advanced aircrafts in terms of information dimension, detection accuracy and efficiency. In this paper, a new method of high-precision 3D defect detection for controlling riveting and seam quality is proposed. For the complexity of illumination distribution on aircraft surface, first, an accurate surface measurement method based on fringe projection is proposed. Then, based on two-dimensional and three-dimensional information, a method of automatic identification and location of rivets and seams is developed. Finally, a visualization method based on augmented reality is proposed to help improve the operator's work efficiency. The experimental results show that the method has high detection accuracy and practicability.
Identifying and Predicting the States of Complex Search Tasks

Complex search tasks that involve uncertain solution space and multi-round search iterations are integral to everyday life and information-intensive workplace practices, affecting how people learn, work, and resolve problematic situations. However, current search systems still face plenty of challenges when applied in supporting users engaging in complex search tasks. To address this issue, we seek to explore the dynamic nature of complex search tasks from process-oriented perspective by identifying and predicting implicit task states. Specifically, based upon the Web search logs and user annotation data (regarding information seeking intentions in local search steps, in-situ search problems, and help needed) collected from 132 search sessions in two controlled lab studies, we developed two task state frameworks based on intention state and problem-help state respectively and examined the connection between task states and search behaviors. We report that (1) complex search tasks of different types can be deconstructed and disambiguated based on the associated nonlinear state transition patterns; and (2) the identified task states that cover multiple subtle factors of user cognition can be predicted from search behavioral signals using supervised learning algorithms. This study reveals the way in which complex search tasks are unfolded and manifested in users' search interactions and paves the way for developing state-aware adaptive search supports and system evaluation frameworks.
Evaluation of the Spatio-Temporal Features and GAN for Micro-Expression Recognition System

Owing to the development and advancement of artificial intelligence, numerous works have been established in the human facial expression recognition system. Meanwhile, the detection and classification of micro-expressions have been attracting attention from various research communities in the recent few years. In this paper, we first review the processes of a conventional optical-flow-based recognition system. Concisely, it comprises four basic steps: facial landmarks annotations (to detect the face and locate the landmark coordinates), optical flow guided images computation (to describe the dynamic changes on the face), feature extraction (to summarize the features encoded) and emotion class categorization (to build a classification model based on the given training data). Secondly, a few approaches have been implemented to improve the feature extraction part, such as exploiting GAN to generate more image samples. Particularly, several variations of optical flow are computed in order to generate optimal images, which lead to high recognition accuracies. Next, GAN, a combination of Generator and Discriminator, is utilized to generate new “fake” images to increase the sample size. Thirdly, a modified state-of-the-art convolutional neural networks is proposed. In brief, multiple optical flow derived components are adopted in the OFF-ApexNet structure to better represent the facial subtle motion changes. From the experiment results obtained, the additional optical flow information computed does not complement the feature extraction stage, and thus leading to poorer recognition performance. On the other hand, the implementation of GAN to the input data improves the performance in SMIC dataset, by achieving the accuracy of 61.80%, 62.20% and 60.98% for AC-GAN, SAGAN and without GAN images, respectively.
PADL: Privacy-Aware and Asynchronous Deep Learning for IoT Applications

As a promising data-driven technology, deep learning has been widely employed in a variety of Internet-of-Things (IoT) applications. Examples include automated navigation, telemedicine, and smart home. To protect the data privacy of deep-learning-based IoT applications, a few privacy-preserving approaches have also been exploited, designed, and implemented in various scenarios. However, state-of-the-art works are still defective in accuracy, efficiency, and functionality. In this article, we propose the privacy-aware and asynchronous deep-learning-assisted IoT applications (PADL), a privacy-aware and asynchronous deep learning framework that enables multiple data collecting sites to collaboratively train deep neural networks (DNNs), while keeping the confidentiality of private data to each other. Specifically, we first design a layerwise importance propagation (LIP) algorithm to quantify the importance of the model's weights held by each site. Then, we present the customized perturbation mechanism, a precise combination of the LIP algorithm and differential privacy mechanism, which helps to make optimal tradeoffs between the availability and privacy of local models. Furthermore, to fully use the computing resources of all sites, for the first time, we propose an advanced asynchronous optimization (AAO) protocol to perform global updates without waiting. Theoretical analysis shows that the PADL is robust to extreme collusion even with only one reliable site while supporting lock-free optimization. Finally, extensive experiments conducted on real-world data sets using TensorFlow library show that the PADL outperforms the existing systems in terms of efficiency and prediction accuracy.
Stress generation and non-intrusive measurement in virtual environments using eye tracking

Abstract In real life, it is well understood how stress can be induced and how it is measured. While virtual reality (VR) applications can resemble such stress inducers, it is still an open question if and how stress can be measured in a non-intrusive way during VR exposure. Usually, the quality of VR applications is estimated by user acceptance in the form of presence. Presence itself describes the individual’s acceptance of a virtual environment as real and is measured by specific questionnaires. Accordingly, it is expected that stress strongly affects this presence and thus also the quality assessment. Consequently, identifying the stress level of a VR user may enable content creators to engage users more immersively by adjusting the virtual environment to the measured stress. In this paper, we thus propose to use a commercially available eye tracking device to detect stress while users are exploring a virtual environment. We describe a user study in which a VR task was implemented to induce stress, while users’ pupil diameter and pulse were measured and evaluated against a self-reported stress level. The results show a statistically significant correlation between self-reported stress and users’ pupil dilation and pulse, indicating that stress measurements can indeed be conducted during the use of a head-mounted display. If this indication can be successfully proven in a larger scope, it will open up a new era of affective VR applications using individual and dynamic adjustments in the virtual environment.
Disha: An Implementation of Machine Learning Based Bangla Healthcare Chatbot

While humans as a whole do live longer nowadays than ever before, we now suffer certain illnesses to a degree never seen in the past - including rising rates of Diabetes, Hypertension, Hypotension, Cholesterol imbalance, obesity and, ailments such as fever. People around the world are preemptively seeking medical advices on how to live a healthy lifestyle. They are looking to lower their risk of various diseases. A healthcare chatbot can play a significant role to monitor a person's health status. A healthcare chatbot is a computer program designed to simulate conversation with human users as a virtual medical assistant. Our study shows, there are some healthcare chatbots available in English and other languages but not in Bangla. In this paper, we have demonstrated a machine learning-based closed domain Bangla healthcare chatbot `Disha (Direction)' which can converse in Bangla with the user with the help of its knowledge base and through learning from interactions with the user. It helps a user to diagnose potential diseases based on inputted symptoms, keep track of a user's health status, and alert a user from potential health hazards. This paper explores the use of six supervised machine learning approaches and showed significant efficiency.