Reviewerly/exHarmony huggingface dataset:
Enabling Sophisticated Lifecycle Support for Mobile Healthcare Data Collection Applications

The widespread dissemination of smart mobile devices enables new ways of collecting longitudinal data sets in a multitude of healthcare scenarios.On the one hand, mobile data collection can be accomplished more effectively and quicker compared with validated paper-based instruments.On the other hand, it can increase the data quality significantly and enable data collection in scenarios not covered by existing approaches so far.Previous attempts to utilize smart mobile devices for collecting data in these scenarios, however, often struggle with high costs for developing and maintaining mobile applications, which need to run on a multitude of mobile operating systems.Therefore, in the QuestionSys project, we are developing a generic (i.e., platform-independent) framework for enabling mobile data collection and sensor data integration in healthcare scenarios.The latter, in turn, is addressed by a model-driven approach, which is shown this paper along with the core components of the QuestionSys framework.In particular, it is shown how healthcare experts are empowered to create mobile data collection and sensing applications on their own and with reasonable efforts.
An Application of Object Detection Based on YOLOv3 in Traffic

With the progress of scientific-technology, automatic equipment has become a prevailing trend in our society. Especially in transportation, unmanned vehicle system is a significant research project that can greatly benefit us. In traffic scene, to achieve high detection accuracy in real-time, a robust algorithm is needed. In this paper, we present a detection method based on YOLOv3 which preprocesses the data set before training. We divide the original images into equal parts with k-fold cross validation. Each time when we train the model, we choose one part as testing set and remaining parts as training set to make full use of our data. The experiment results show that our method performs better than YOLOv3 in the case of class-imbalance. We also propose a new network structure, Dense-YOLOv3 which replaces some residual modules with dense modules. Through this method, overfitting is effectively alleviated.
Smart Home Technology Diffusion in a Living Laboratory

Smart home products continue to rise in popularity but have yet to achieve widespread adoption. There is little research on how the general population perceives benefits of different smart home devices beyond general surveys. Using a living laboratory of five solar houses that we equipped with a range of smart home devices, we assessed how university student residents learn about, use, and gain interest in adopting this smart home technology. Analysis of data confirms that users find lifestyle benefits to be the most important motivators for adopting smart home technology. Yet without training in using that technology, these benefits do not outweigh the risks associated with learning to operate that technology.
Twitter culture: irony comes faster than tourist mobility

Recent developments in the research field of the tourism sciences show a growing interest in customer satisfaction. Increasingly mediated communications develop new signification practices within social networks, as Twitter, where tourism companies discover what users think of the service offered, through methodologies such as Sentiment Analysis [Pang, B., & Lee, L. (2008). Opinion mining and sentiment analysis. Foundations and Trends® in Information Retrieval, 2(1–2), 1–135] and digital natives [Prensky, M. (2001). Digital natives, digital immigrants. On the Horizon, 9(5), 1–6] produce short textual content regarding the service of tourist companies, as in the case of Trenitalia. From the analysis of sentiment on tweets about Trenitalia, it emerged that Internet users tend to employ ironic devices, difficult to recognize by a software tool. Starting from this limit, the main intent is to examine how social networks offer a new context to the enunciative practice of irony. To detect these aspects, the thinking aloud technique is used, aiming to understand how digital actors re-build signification practices of tourist mobility and to demonstrate how online reputation and customer satisfaction detection systems are not always effective in understanding why users, when they review a tourist service, use veiled communication methods.
Analysis of the effects of imperfections in an optical heterodyne quantum random-number generator

Quantum random numbers generators (QRNGs) rely on quantum systems to produce sequences of random numbers with an overall lower level of predictability than classical algorithmic systems. Over the past two decades, phase randomizations of coherent sources from quantum spontaneous emission effects have gained a lot of interest due to their operational simplicity, cost-contained components, and ability to generate random numbers at high rates. However, many QRNGs require optimal calibration and alignment to ensure efficient and effective random-number generation. This work demonstrates a detailed analysis of a heterodyne measurement based QRNG, which implements phase randomization from two independent laser sources. The analysis also quantifies the effects of setup misalignments using the Kullback-Leibler divergence as a benchmark to assess the limiting conditions of secure random-number generation.
Quality Prediction Model Based on Novel Elman Neural Network Ensemble

In this paper, we propose a novel prediction algorithm based on an improved Elman neural network (NN) ensemble for quality prediction, thus achieving the quality control of designed products at the product design stage. First, the Elman NN parameters are optimized using the grasshopper optimization (GRO) method, and then the weighted average method is improved to combine the outputs of the individual NNs, where the weights are determined by the training errors. Simulations were conducted to compare the proposed method with other NN methods and evaluate its performance. The results demonstrated that the proposed algorithm for quality prediction obtained better accuracy than other NN methods. In this paper, we propose a novel Elman NN ensemble model for quality prediction during product design. Elman NN is combined with GRO to yield an optimized Elman network ensemble model with high generalization ability and prediction accuracy.
An image understanding based model with ion current signals for predicting combustion information

With the gradually stringent emission regulation, it is in needs for new methods to optimize conventional combustion engines in terms of exhaust emission, working performance, and abnormal combustion. In response, cheaper, more reliable, and more responsive engine control schemes based on the ion current detection method appear, and a challenge in the method is to retrieve combustion information involving max-pressure and knock condition from ion current signals. To cope with the challenge, we develop an image understanding based FusionNet model that transforms ion current signals to spectrograms and takes the spectrograms to predict max-pressure and knock condition simultaneously. As a result, FusionNet can predict the crank angle and the numerical value of the max-pressure of samples in the test set, with an average of the Mean Squared Error valuing 6.802 and 7.142 respectively. Moreover, FusionNet can predict pressure oscillation related to the knock condition with an average of the Cosine Similarity valuing 0.00932, and apply the detection result of the oscillation to predict the knock condition of samples in the test set, with an average of the F1-Score valuing 0.92684.
A Method Toward Privacy Protection in Context-Aware Environment

Current demands for flexible services based on context-aware applications are mounting. The efficiency of such services mainly depends on acquiring, organizing, accessing, processing and sharing the user’s context. This context usage yield to more concerns and even more worries about the user’s data privacy. Hence, in this work we propose a privacy-aware method to address users privacy protection concerns in context-aware environments through automated decision-making processes by monitoring their privacy behavior and personal data usage.
GrowBit: Incremental Hashing for Cross-Modal Retrieval

Cross-modal retrieval using hashing techniques is gaining increasing importance due to its efficient storage, scalability and fast query processing speeds. In this work, we address a related and relatively unexplored problem: given a set of cross-modal data with their already learned hash codes, can we increase the number of bits to better represent the data without relearning everything? This scenario is especially important when the number of tags describing the data increases, necessitating longer hash codes for better representation. To tackle this problem, we propose a novel approach called GrowBit, which incrementally learns the bits in the hash code and thus utilizes all the bits learned so far. We develop a two-stage approach for learning the hash codes and hash functions separately, utilizing a recent formulation which decouples over the bits so that it can incorporate the incremental approach. Experiments on MirFlickr, IAPR-TC-12 and NUS-WIDE datasets show the usefulness of the proposed approach.
Gradient Correlation Functions in Digital Image Correlation

The performance of seven different correlation functions applied in Digital Image Correlation has been investigated using simulated and experimentally acquired laser speckle patterns. The correlation functions were constructed as combinations of the pure intensity correlation function, the gradient correlation function and the Hessian correlation function, respectively. It was found that the correlation function that was constructed as the product of all three pure correlation functions performed best for the small speckle sizes and large correlation values, respectively. The difference between the different functions disappeared as the speckle size increased and the correlation value dropped. On average, the random error of the combined correlation function was half that of the traditional intensity correlation function within the optimum region.
Small High-Resolution Angular Displacement Measurement Technology Based on Near-Field Image Acquisition

Advances in fine photoelectric displacement measurement technology have made image angular displacement measurement technology via imaging method a popular research topic; it is now easier than ever to realize small-scale high-resolution angular displacement measurement. This paper proposes a small-sized high-resolution image angular displacement measurement technique based on near-field image acquisition. A measuring mechanism based on the lensless near-field image acquisition is first established. Next, based on the error code problem, a space-value-based code value correction algorithm is proposed. Finally, the proposed device is validated on an experimental system. The proposed technology can achieve 21-bit resolution and 20.14” angle measurement accuracy. The results presented here may provide a workable foundation for further research on image-type fine photoelectric displacement measurement technology.
Single-Shot Colored Speckle Pattern for High Accuracy Depth Sensing

Structured light techniques are widely used in the depth measurement. In this paper, we propose a colored speckle method for high-accuracy depth sensing. The projected pattern is a composite of the discrete phase information and the colored De Bruijn sequence information, which can prove the feasibility, in practice, and the robustness to the environment. A Gabor filter is used to estimate the wrapped phase. To alleviate the phase ambiguity of conventional measurement methods, the period of the phase is distributed in two channels of the colored pattern by the De Bruijn sequence decoding. A morphological closure is adopted to determine the De Bruijn code by the phase neighborhoods back and forth. The proposed method is suitable for a dynamic scene. The quantitative and qualitative experiments are conducted, in practice, to evaluate the performance of the proposed method. The results of the experiments prove that not only can our proposed method achieve high accuracy and dense depth map, but it is also suitable for color object and dynamic scenes indoors.
A Network Method for Identifying the Root Cause of High-Speed Rail Faults Based on Text Data

Root cause identification is an important task in providing prompt assistance for diagnosis, security monitoring and guidance for specific routine maintenance measures in the field of railway transportation. However, most of the methods addressing rail faults are based on state detection, which involves structured data. Manual cause identification from railway equipment maintenance and management text records is undoubtedly a time-consuming and laborious task. To quickly obtain the root cause text from unstructured data, this paper proposes an approach for root cause factor identification by using a root cause identification-new word sentence (RCI-NWS) keyword extraction method. The experimental results demonstrate that the extraction of railway fault text data can be performed using the keyword extraction method and the highest values are obtained using RCI-NWS.
A Bayes Risk Minimization Machine for Example-Dependent Cost Classification

A new method for example-dependent cost (EDC) classification is proposed. The method constitutes an extension of a recently introduced training algorithm for neural networks. The surrogate cost function is an estimate of the Bayesian risk, where the estimates of the conditional probabilities for each class are defined in terms of a 1-D Parzen window estimator of the output of (discriminative) neural networks. This probability density is modeled with the objective of allowing an easy minimization of a sampled version of the Bayes risk. The conditional probabilities included in the definition of the risk are not explicitly estimated, but the risk is minimized by a gradient-descent algorithm. The proposed method has been evaluated using linear classifiers and neural networks, with both shallow (a single hidden layer) and deep (multiple hidden layers) architectures. The experimental results show the potential and flexibility of the proposed method, which can handle EDC classification under imbalanced data situations that commonly appear in this kind of problems.
Low-Complexity Dynamic Channel Scaling of Noise-Resilient CNN for Intelligent Edge Devices

In this paper, we present a novel channel scaling scheme for convolutional neural networks (CNNs), which can improve the recognition accuracy for the practical distorted images without increasing the network complexity. During the training phase, the proposed work first prepares multiple filters under the same CNN architecture by taking account of different noise models and strengths. We then newly introduce an FFT-based noise classifier, which determines the noise property in the received input image by calculating the partial sum of the frequency-domain values. Based on the detected noise class, we dynamically change the filters of each CNN layer to provide the dedicated recognition. Furthermore, we propose a channel scaling technique to reduce the number of active filter parameters if the input data is relatively clean. Experimental results show that the proposed dynamic channel scaling reduces the computational complexity as well as the energy consumption, still providing the acceptable accuracy for intelligent edge devices.
What Is Abusive Language?

Abusive language has been corrupting online conversations since the inception of the internet. Substantial research efforts have been put into the investigation and algorithmic resolution of the problem. Different aspects such as "cyberbullying", "hate speech" or "profanity" have undergone ample amounts of investigation, however, often using inconsistent vocabulary such as "offensive language" or "harassment". This led to a state of confusion within the research community. The inconsistency can be considered an inhibitor for the domain: It increases the risk of unintentional redundant work and leads to undifferentiated and thus hard to use and justifiable machine learning classifiers. To remedy this effect, this paper introduces a novel configurable, multi-view approach to define abusive language concepts.
An image representation of skeletal data for action recognition using convolutional neural networks

In this paper we present preliminary results of an approach for understanding human actions, based on a novel 2D image representation for 3D skeletal data. More specifically, motion information for human skeletal joints is transformed to a pseudo-colored image. A Convolutional Neural Network is then used for classification. Our approach is evaluated for actions that may be used in an ambient assisted living scenario.
Efficient implementation of lightweight block ciphers on volta and pascal architecture

Lightweight block ciphers based on symmetric cipher are tailored to addressing security issues for highly constrained Internet of things devices. In this article, we explore general software implementations of lightweight ciphers on GPU architectures, with a special focus on LED, Piccolo and PRESENT. First, we implement the lightweight block ciphers using lookup table based technique. Then we analyze the effect of different factors on the encryption performance, such as the size of lookup table, the parallelism level, the use of different type of memory and the idle state caused by data transmission. We evaluated the three lightweight block ciphers on Nvidia Tesla V100 and Nvidia GTX1060 GPUs. Finally, the experimental result shows a great improvement on all the investigated ciphers compared to the unoptimized implementations.
An Energy-Based SAR Image Segmentation Method with Weighted Feature

To extract more structural features, which can contribute to segment a synthetic aperture radar (SAR) image accurately, and explore their roles in the segmentation procedure, this paper presents an energy-based SAR image segmentation method with weighted features. To precisely segment a SAR image, multiple structural features are incorporated into a block- and energy-based segmentation model in weighted way. In this paper, the multiple features of a pixel, involving spectral feature obtained from original SAR image, texture and boundary features extracted by a curvelet transform, form a feature vector. All the pixels’ feature vectors form a feature set of a SAR image. To automatically determine the roles of the multiple features in the segmentation procedure, weight variables are assigned to them. All the weight variables form a weight set. Then the image domain is partitioned into a set of blocks by regular tessellation. Afterwards, an energy function and a non-constrained Gibbs probability distribution are used to combine the feature and weight sets to build a block-based energy segmentation model with feature weighted on the partitioned image domain. Further, a reversible jump Markov Chain Monte Carlo (RJMCMC) algorithm is designed to simulate from the segmentation model. In the RJMCMC algorithm, three move types were designed according to the segmentation model. Finally, the proposed method was tested on the SAR images, and the quantitative and qualitative results demonstrated its effectiveness.
Near-Lossless ℓ∞-Constrained Image Decompression via Deep Neural Network

Recently a number of CNN-based techniques were proposed to remove image compression artifacts. As in other restoration applications, these techniques all learn a mapping from decompressed patches to the original counterparts under the ubiquitous L2 metric. However, this approach is incapable of restoring distinctive image details which may be statistical outliers but have high semantic importance (e.g., tiny lesions in medical images). To overcome this weakness, we propose to incorporate an ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> fidelity criterion in the design of neural network so that no small, distinctive structures of the original image can be dropped or distorted. Experimental results demonstrate that the proposed method outperforms the state-of-the-art methods in ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> error metric and perceptual quality, while being competitive in L2 error metric as well. It can restore subtle image details that are otherwise destroyed or missed by other algorithms. Our research suggests a new machine learning paradigm of ultra high fidelity image compression that is ideally suited for applications in medicine, space, and sciences.
Compromised account detection in online social networks: A survey

Summary Online Social Networks are an integral part of the modern society. Social networking sites are at the top rank in terms of usage out of the billions of websites hosted in the internet. The way in which criminal activities are interleaved to these networks are beyond our imagination. Securing these networks from cyber criminals and malicious attackers are a major concern nowadays. There are various security threats happening to the Online Social Network accounts, and account compromise is one among it. Account compromise can cause collateral damage to the user and to a mass community. Misusing the personal information of the users and exploiting their trust relationships within the network lead to serious problems. These compromised accounts are becoming a safe zone for criminals to launch attacks. The threats caused by these compromised accounts include spreading malware, spy advertisements, and espionage. In most cases, it becomes too late to detect that an account is compromised. Detection of these accounts is important in order to reduce the impact of damage. The detection of compromised Online Social Network account is the process of confirming whether the account used by an individual or by an organization has lost its control or not. This survey reviews the various methods that are found in the literature toward detecting compromised Online Social Network Accounts.
Learning and Annotating Activities for Home Automation using LSTM

Driven by the growth in ubiquitous sensor networks and IoT, Human Activity Recognition (HAR) has emerged to be an exciting challenge. Popular machine learning approaches proposed in this direction have shown promise with modest performance. Treatment of HAR data in a recurrent form, and subsequent analysis using deep networks such as RNNs/LSTM is largely unexplored. In this work, we treat HAR data stream, generated from a smart home setup, as a series of events and apply LSTM to perform feature extraction. We use the Aruba dataset from the CASAS project [4], where we apply LSTM to extract features and perform annotations. We then apply standard classification techniques to recognize the different activities in the newly annotated data. We observe that, using our method we can achieve annotation accuracies of up to 79.5% which is 13.6% better than other state-of-the-art solutions.
Fuzzy Clustering Method for Spherical Data Based on <i>q</i>-Divergence

This study presents a fuzzy clustering algorithm for classifying spherical data based on q -divergence. First, it is shown that a conventional method for vectorial data is equivalent to the regularization of another conventional method using q -divergence. Next, based on the knowledge that q -divergence is a generalization of Kullback-Leibler (KL)-divergence and that there is a conventional fuzzy clustering method for classifying spherical data based on KL-divergence, a fuzzy clustering algorithm for spherical data is derived based on q -divergence. This algorithm uses an optimization problem built by extending KL-divergence in the conventional method to q -divergence. Finally, some numerical experiments are conducted to verify the proposed methods.