Reviewerly/exHarmony huggingface dataset:
Lightweight Convolutional Neural Network Model for Human Face Detection in Risk Situations

In this article, we propose a model of face detection in risk situations to help rescue teams speed up the search of people who might need help. The proposed lightweight convolutional neural network (CNN) architecture is designed to detect faces of people in mines, avalanches, under water, or other dangerous situations when their face might not be very visible over surrounding background. We have designed a novel light architecture cooperating with the proposed sliding window procedure. The designed model works with maximum simplicity to support mobile devices. An output from processing presents a box on face location in the screen of device. The model was trained by using Adam and tested on various images. Results show that proposed lightweight CNN detects human faces over various textures with accuracy above 99% and precision above 98% what proves the efficiency of our proposed model.
A study of how immersion and interactivity drive VR learning

Even though learning refers to both a process and a product, the former tends to be overlooked in educational virtual reality (VR) research. This study examines the process of learning with VR technology using the Cognitive Affective Model of Immersive Learning (CAMIL) as its framework. The CAMIL theorizes that two technological features of VR, interactivity and immersion, influence a number of cognitive and affective variables that may facilitate or hinder learning. In addition, VR studies often involve media comparisons that make it difficult to disentangle the relative effects of technological features on learning. Therefore, this study also aims to provide insights concerning the unique and combined effects of interactivity and immersion on the cognitive and affective variables specified by CAMIL. We employed a 2 × 2 between-subjects design (N = 153) and manipulated the degree of interactivity and immersion during a virtual lesson on the topic of viral diseases. Analyses of variance (ANOVAs) were used to examine the effects of interactivity and immersion on our variables of interest, and structural equation modeling (SEM) was used to assess the process of learning as predicted by the CAMIL. The results indicated that the process of learning involves situational interest and embodied learning. Main effects of interactivity and/or immersion on cognitive load, situational interest, and physical presence are also reported in addition to interaction effects between immersion and interactivity on agency and embodied learning. The findings provide evidence for the CAMIL and suggest important additions to the model. These findings can be used to provide a better understanding of the process of learning in immersive VR and guide future immersive learning research.
Self‐Powered Gesture Recognition Wristband Enabled by Machine Learning for Full Keyboard and Multicommand Input

Virtual reality is a brand-new technology that can be applied extensively. To realize virtual reality, certain types of human-computer interaction equipment are necessary. Existing virtual reality technologies often rely on cameras, data gloves, game pads, and other equipment. These equipment are either bulky, inconvenient to carry and use, or expensive to popularize. Therefore, the development of a convenient and low-cost high-precision human-computer interaction device can contribute positively to the development of virtual reality technology. In this study, a gesture recognition wristband that can realize a full keyboard and multicommand input is developed. The wristband is convenient to wear, low in cost, and does not affect other daily operations of the hand. This wristband is based on physiological anatomy as well as aided by active sensor and machine learning technology; it can achieve a maximum accuracy of 92.6% in recognizing 26 letters. This wristband offers broad application prospects in the fields of gesture command recognition, assistive devices for the disabled, and wearable electronics.
Hidden coexisting hyperchaos of new memristive neuron model and its application in image encryption

The neuron models have been widely applied to neuromorphic computing systems and chaotic circuits. However, discrete neuron models and their application in image encryption have not gotten a lot of attention yet. This paper first presents a novel neuron model with significant chaotic characteristics, by coupling a memristor into the proposed neuron, a memristive neuron model is further obtained. Relevant control parameter-relied dynamical evolution is demonstrated using several numerical methods. The explorations manifest that memristor can boost chaos complexity of the discrete neuron, resulting in hyperchaos, infinite coexisting hidden attractors and attractor growing. Particularly, the NIST test verifies the generated hyperchaotic sequences exhibit high complexity, which makes them applicable to many applications based on chaos. Additionally, digital experiments based on developed hardware platform are designed to implement the memristive neuron model and get the hyperchaos. We also propose a new encryption scheme to apply the memristive neuron to the application of image encryption. The evaluation results show that the conceived algorithm appears excellent security characteristics and can effectively protect the information security of images.
MMRotate

We present an open-source toolbox, named MMRotate, which provides a coherent algorithm framework of training, inferring, and evaluation for the popular rotated object detection algorithm based on deep learning. MMRotate implements 18 state-of-the-art algorithms and supports the three most frequently used angle definition methods. To facilitate future research and industrial applications of rotated object detection-related problems, we also provide a large number of trained models and detailed benchmarks to give insights into the performance of rotated object detection. MMRotate is publicly released at https://github.com/open-mmlab/mmrotate.
FallDroid: An Automated Smart-Phone-Based Fall Detection System Using Multiple Kernel Learning

Common fall occurrences in the elderly population pose dramatic challenges in public healthcare domain. Adoption of an efficient and yet highly reliable automatic fall detection system may not only mitigate the adverse effects of falls through immediate medical assistance, but also profoundly improve the functional ability and confidence level of elder people. This paper presents a pervasive fall detection system developed on smart phones, namely, FallDroid that exploits a two-step algorithm proposed to monitor and detect fall events using the embedded accelerometer signals. Comprising of the threshold-based method and multiple kernel learning support vector machine, the proposed algorithm uses novel techniques to effectively identify fall-like events (such as lying on a bed or sudden stop after running) and reduce false alarms. In addition to user convenience and low power consumption, experimental results reveal that the system detects falls with high accuracy (97.8% and 91.7%), sensitivity (99.5% and 95.8%), and specificity (95.2% and 88.0%) when placed around the waist and thigh, respectively. The system also achieves the lowest false alarm rate of 1 alarm per 59 h of usage, which is best till date.
Using Data Augmentation in Continuous Authentication on Smartphones

As personal computing platforms, smartphones are commonly used to store private, sensitive, and security information, such as photographs, emails, and Android Pay. To protect such information from adversaries, continuous authentication on smartphone users becomes more and more important. In this paper, we present a novel authentication system, SensorAuth, for continuous authentication of users based on their behavioral patterns, by leveraging the accelerometer and gyroscope ubiquitously built into smartphones. We are among the first to exploit five data augmentation approaches including permutation, sampling, scaling, cropping, and jittering to create additional data by applying them on training data. With the augmented data, SensorAuth extracts sensor-based features in both time and frequency domains within a time window, then utilizes the one-class support vector machine to train the classifier, and finally authenticates users. We evaluate the authentication performance of SensorAuth in terms of the impact of window size, accuracy on each of and combinations of data augmentation approaches, time efficiency, energy consumption, and comparisons with the representative classifiers and with the existing approaches, respectively. The experimental results show that SensorAuth performs highly accurate and time-efficient continuous authentication, by reaching the lowest median equal error rate of 4.66%, and consuming a short authentication time of approximately 5 s.
Profit Maximization for Cloud Brokers in Cloud Computing

Along with the development of cloud computing, more and more applications are migrated into the cloud. An important feature of cloud computing is pay-as-you-go. However, most users always should pay more than their actual usage due to the one-hour billing cycle. In addition, most cloud service providers provide a certain discount for long-term users, but short-term users with small computing demands cannot enjoy this discount. To reduce the cost of cloud users, we introduce a new role, which is cloud broker. A cloud broker is an intermediary agent between cloud providers and cloud users. It rents a number of reserved VMs from cloud providers with a good price and offers them to users on an on-demand basis at a cheaper price than that provided by cloud providers. Besides, the cloud broker adopts a shorter billing cycle compared with cloud providers. By doing this, the cloud broker can reduce a great amount of cost for user. In addition to reduce the user cost, the cloud broker also could earn the difference in prices between on-demand and reserved VMs. In this paper, we focus on how to configure a cloud broker and how to price its VMs such that its profit can be maximized on the premise of saving costs for users. Profit of a cloud broker is affected by many factors such as the user demands, the purchase price and the sales price of VMs, the scale of the cloud broker, etc. Moreover, these factors are affected mutually, which makes the analysis on profit more complicated. In this paper, we first give a synthetically analysis on all the affecting factors, and define an optimal multiserver configuration and VM pricing problem which is modeled as a profit maximization problem. Second, combining the partial derivative and bisection search method, we propose a heuristic method to solve the optimization problem. The near-optimal solutions can be used to guide the configuration and VM pricing of the cloud broker. Moreover, a series of comparisons are given which show that a cloud broker can save a considerable cost for users.
Gated Conditional Displacement Readout of Superconducting Qubits

We have realized a new interaction between superconducting qubits and a readout cavity that results in the displacement of a coherent state in the cavity, conditioned on the state of the qubit. This conditional state, when it reaches the cavity-following, phase-sensitive amplifier, matches its measured observable, namely, the in phase quadrature. In a setup where several qubits are coupled to the same readout resonator, we show it is possible to measure the state of a target qubit with minimal dephasing of the other qubits. Our results suggest novel directions for faster readout of superconducting qubits and implementations of bosonic quantum error-correcting codes.
Efficient feature selection method using real-valued grasshopper optimization algorithm

Feature selection is the problem of finding the minimum number of features among a redundant feature space which leads to the maximum classification performance. In this paper, we have proposed a novel feature selection method based on mathematical model of interaction between grasshoppers in finding food sources. Some modifications were applied to the grasshopper optimization algorithm (GOA) to make it suitable for a feature selection problem. The method, abbreviated as GOFS is supplemented by statistical measures during iterations to replace the duplicate features with the most promising features. Several publicly available datasets with various dimensionalities, number of instances, and target classes were considered to evaluate the performance of the GOFS algorithm. The results of implementing twelve well-known and recent feature selection methods were presented and compared with GOFS algorithm. Comparative experiments indicate the significance of the proposed method in comparison with other feature selection methods.
Knowledge-enhanced document embeddings for text classification

Accurate semantic representation models are essential in text mining applications. For a successful application of the text mining process, the text representation adopted must keep the interesting patterns to be discovered. Although competitive results for automatic text classification may be achieved with traditional bag of words, such representation model cannot provide satisfactory classification performances on hard settings where richer text representations are required. In this paper, we present an approach to represent document collections based on embedded representations of words and word senses. We bring together the power of word sense disambiguation and the semantic richness of word- and word-sense embedded vectors to construct embedded representations of document collections. Our approach results in semantically enhanced and low-dimensional representations. We overcome the lack of interpretability of embedded vectors, which is a drawback of this kind of representation, with the use of word sense embedded vectors. Moreover, the experimental evaluation indicates that the use of the proposed representations provides stable classifiers with strong quantitative results, especially in semantically-complex classification scenarios.
Valence, arousal and sentiment meanings of 33 facial emoji: Insights for the use of emoji in consumer research

Emoji are increasingly used for emotional expression in computer-mediated communications and represent a relevant source of information for consumer companies about consumer's food-related attitudes and behaviours. However, given that emoji convey a wide range of meanings an in-depth understanding of how consumers interpret them is necessary to enable their use in food-related consumer research. The purpose of this research was to explore consumers' interpretations of facial emoji. In an online survey with 1084 adults from the USA, an open-ended question was used to uncover meanings associated to 33 common facial emoji (in Apple platform rendition iOS 6.0). Participants also evaluated the emotional valence and arousal of the emoji using Self-Assessment Manikins (SAM). The latter confirmed that facial emoji can span broad ranges of emotional valence and arousal and convey a corresponding range of different emotions. Although emoji conveyed their unique and intended meaning, semantic meanings were multiple and bestowed by associations of varying strength depending on the specific emoji being considered. This emphasised that emoji interpretation and use can be flexible, as also seen in internet resources dedicated to emoji meaning. Implications for food-related consumer research are discussed.
Scalable Access Management in IoT Using Blockchain: A Performance Evaluation

The rise of the Internet of Things (IoT) implies new technical challenges such as managing a universally vast number of IoT devices. Despite the fact that there are already a variety of secure management frameworks for IoT, they are based on centralized models, which limits their applicability in scenarios with a large number of IoT devices. In order to overcome those limitations, we have developed a distributed IoT management system based on blockchain. In this paper, we compare the performance of our solution with the existing access management solutions in IoT. We study the delays and the throughput rate associated with the systems and analyze different configurations of our solution to maximize its scalability. The objective of this paper is to find out whether our solution can scale as well as the existing management systems in IoT.
Semi-supervised person re-identification using multi-view clustering

Person Re-Identification (Re-Id) is a challenging task focusing on identifying the same person among disjoint camera views. A number of deep learning algorithms have been reported for this task in fully-supervised fashion which requires a large amount of labeled training data, while obtaining high quality labels for Re-Id is extremely time consuming. To address this problem, we propose a semi-supervised Re-Id framework by using only a small portion of labeled data and some additional unlabeled samples. This paper approaches the problem by constructing a set of heterogeneous Convolutional Neural Networks (CNNs) fine-tuned using the labeled portion, and then propagating the labels to the unlabeled portion for further fine-tuning the overall system. In this work, label estimation is a key component during the propagation process. We propose a novel multi-view clustering method, which integrates features of multiple heterogeneous CNNs to cluster and generate pseudo labels for unlabeled samples. Then we fine-tune each of the multiple heterogeneous CNNs by minimizing an identification loss and a verification loss simultaneously, using training data with both true labels and pseudo labels. The procedure is iterated until the estimation of pseudo labels no longer changes. Extensive experiments on three large-scale person Re-Id datasets demonstrate the effectiveness of the proposed method.
Sphericity and roundness computation for particles using the extreme vertices model

Shape is a property studied for many kinds of particles. Among shape parameters, sphericity and roundness indices had been largely studied to understand several processes. Some of these indices are based on length measurements of the particle obtained from its oriented bounding box (OBB). In this paper we follow a discrete approach based on Extreme Vertices Model and devise new methods to compute the OBB and the mentioned indices. We apply these methods to synthetic sedimentary rocks and to a real dataset of silicon nanocrystals (Si NC) to analyze the obtained results and compare them with those obtained with a classical voxel model.
Metrological and Critical Characterization of the Intel D415 Stereo Depth Camera

Low-cost RGB-D cameras are increasingly being used in several research fields, including human⁻machine interaction, safety, robotics, biomedical engineering and even reverse engineering applications. Among the plethora of commercial devices, the Intel RealSense cameras have proven to be among the most suitable devices, providing a good compromise between cost, ease of use, compactness and precision. Released on the market in January 2018, the new Intel model RealSense D415 has a wide acquisition range (i.e., ~160⁻10,000 mm) and a narrow field of view to capture objects in rapid motion. Given the unexplored potential of this new device, especially when used as a 3D scanner, the present work aims to characterize and to provide metrological considerations for the RealSense D415. In particular, tests are carried out to assess the device performance in the near range (i.e., 100⁻1000 mm). Characterization is performed by integrating the guidelines of the existing standard (i.e., the German VDI/VDE 2634 Part 2) with a number of literature-based strategies. Performance analysis is finally compared against the latest close-range sensors, thus providing a useful guidance for researchers and practitioners aiming to use RGB-D cameras in reverse engineering applications.
Recent Advances in Supervised Dimension Reduction: A Survey

Recently, we have witnessed an explosive growth in both the quantity and dimension of data generated, which aggravates the high dimensionality challenge in tasks such as predictive modeling and decision support. Up to now, a large amount of unsupervised dimension reduction methods have been proposed and studied. However, there is no specific review focusing on the supervised dimension reduction problem. Most studies performed classification or regression after unsupervised dimension reduction methods. However, we recognize the following advantages if learning the low-dimensional representation and the classification/regression model simultaneously: high accuracy and effective representation. Considering classification or regression as being the main goal of dimension reduction, the purpose of this paper is to summarize and organize the current developments in the field into three main classes: PCA-based, Non-negative Matrix Factorization (NMF)-based, and manifold-based supervised dimension reduction methods, as well as provide elaborated discussions on their advantages and disadvantages. Moreover, we outline a dozen open problems that can be further explored to advance the development of this topic.
A machine learning based robust prediction model for real-life mobile phone data

Real-life mobile phone data may contain noisy instances, which is a fundamental issue for building a prediction model with many potential negative consequences. The complexity of the inferred model may increase, may arise over-fitting problem, and thereby the overall prediction accuracy of the model may decrease. In this paper, we address these issues and present a robust prediction model for real-life mobile phone data of individual users, in order to improve the prediction accuracy of the model. In our robust model, we first effectively identify and eliminate the noisy instances from the training dataset by determining a dynamic noise threshold using naive Bayes classifier and laplace estimator, which may differ from user-to-user according to their unique behavioral patterns. After that, we employ the most popular rule-based machine learning classification technique, i.e., decision tree, on the noise-free quality dataset to build the prediction model. Experimental results on the real-life mobile phone datasets (e.g., phone call log) of individual mobile phone users, show the effectiveness of our robust model in terms of precision, recall and f-measure.
Design of a New Vision-Based Method for the Bolts Looseness Detection in Flange Connections

As regular inspection for the bolt connection in inaccessible areas is difficult and costly, computer vision technology provides a suitable noncontact approach for real-time bolt looseness detection as an alternative to inspection approaches. However, computer vision still suffers from various impracticalities. In this paper, a new vision-based bolt looseness detection method is designed and implemented with the bolt images acquired by a camera at arbitrary positions around the bolts. The new method includes the perspective transformation of original images acquired, identification of bolt positioning with the convolutional neural network digit recognition, detection of bolt rotation angles using Hough transform line detection, and density-based spatial clustering of applications with noise. To demonstrate the effectiveness of the new method, an experiment with bolted connections is setup. The experimental results demonstrate that the new method can accurately detect the looseness of the bolts in the bolted connection.