Reviewerly/exHarmony huggingface dataset:
Hybrid Beta-KDE Model for Solar Irradiance Probability Density Estimation

This letter proposes a hybrid Beta-kernel density estimation (KDE) model for solar irradiance probability density estimation. The model combines parametric and nonparametric approaches to avoid KDE boundary bias and obtain a more reliable statistical model of solar irradiance. The performance of the hybrid model is assessed via comparisons with the Beta distribution and two KDE models that employ different bandwidth selection methods. The assessment is carried out using the Kolmogorov-Smirnov goodness-of-fit test, and four measures of error: root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and mean bias error (MBE). Results confirm the accuracy of the hybrid model for solar irradiance modeling with percentage improvements over the Beta distribution of up to 13.8% (RMSE), 11.7% (MAE), 19.3% (MAPE), and 72.5% (MBE). The K-S test results show that the proposed Beta-KDE hybrid is the only model for which the null hypothesis is not rejected for any of the eight datasets considered in this study.
An adaptive steganographic scheme for H.264/AVC video with distortion optimization

With the popularity of streaming media, data hiding in H.264/AVC video streams has attracted more and more attention. However, how to achieve high secure video steganography while preventing intra-frame distortion drift is a challenging task. In this paper, based on Syndrome–Trellis Code(STC), we propose a secure steganographic scheme using Quantized Discrete Cosine Transform (QDCT) coefficients of I-frames. We first present a theoretical analysis of the block distortion, which consists of inter-block distortion caused by data embedding in reference pixels of adjacent blocks and inner-block distortion caused by data embedding in local pixels of current block. Based on block distortion analysis, we propose a novel distortion optimization strategy by avoiding inter-block distortion and decreasing inner-block distortion. The cost function is constructed based on block distortion, bit-rate change and texture complexity of DCT block. Compared with distortion compensation methods, the proposed method not only considers the distortion optimization but also introduces secure steganographic scheme. Experimental results show that the proposed scheme can resist the state-of-the-art steganalytic method while achieving high visual quality.
Question-Led object attention for visual question answering

Question plays a leading role for Visual Question Answering (VQA) because it specifies the particular visual objects or conjures vivid visual that the machine should attend. However, existing approaches predominantly predict the answer by utilizing the question and the whole image without considering the leading role of the question. Also, recent object spatial inference is usually conducted on pixel level instead of object level. Therefore, we propose a novel but simple framework, namely Question-Led Object Attention (QLOB), to improve the VQA performance by exploring question semantics, fine-grained object information, and the relationship between those two modalities. First, we extract sentence semantics by a question model, and utilize the efficient object detection network to obtain a global visual feature and local features from top r object region proposals. Second, our QLOB attention mechanism selects those question-related object regions. Third, we optimize question model and QLOB attention by a softmax classifier to predict the final answer. Extensive experimental results on three public VQA datasets demonstrate that our QLOB outperforms the state-of-the-arts.
Intelligent Micro Energy Grid in 5G Era: Platforms, Business Cases, Testbeds, and Next Generation Applications

As fifth-generation mobile communication systems give rise to new smart grid technologies, such as distributed energy resources, advanced communication systems, the Internet of Things, and big data analytics, the development of novel platforms and business models that ensure reliability and profitability of microgrid operations become increasingly important. In this study, we introduce an open micro energy grid platform to operate the widely distributed microgrids in Korea. Subsequently, we present commercial microgrid business models supported by the open micro energy grid platform equipped with an artificial intelligence engine and provide test results from testbeds connected to the platform. In contrast to the existing microgrid business models in the market, we propose a universal architecture and business model of the future microgrid, comprising (i) an energy robot-management operation business model, (ii) electric vehicle-based demand response, (iii) blockchain technology for energy trading, and (iv) a service-oriented business model. Finally, we propose a new business model for an intelligent virtual power plant (VPP) operator along with the architecture of the VPP and its proof of concept (PoC). We expect the proposed business model to provide energy solution providers with guidelines to develop various VPP services.
QAnalysis: a question-answer driven analytic tool on knowledge graphs for leveraging electronic medical records for clinical research

While doctors should analyze a large amount of electronic medical record (EMR) data to conduct clinical research, the analyzing process requires information technology (IT) skills, which is difficult for most doctors in China.In this paper, we build a novel tool QAnalysis, where doctors enter their analytic requirements in their natural language and then the tool returns charts and tables to the doctors. For a given question from a user, we first segment the sentence, and then we use grammar parser to analyze the structure of the sentence. After linking the segmentations to concepts and predicates in knowledge graphs, we convert the question into a set of triples connected with different kinds of operators. These triples are converted to queries in Cypher, the query language for Neo4j. Finally, the query is executed on Neo4j, and the results shown in terms of tables and charts are returned to the user.The tool supports top 50 questions we gathered from two hospital departments with the Delphi method. We also gathered 161 questions from clinical research papers with statistical requirements on EMR data. Experimental results show that our tool can directly cover 78.20% of these statistical questions and the precision is as high as 96.36%. Such extension is easy to achieve with the help of knowledge-graph technology we have adopted. The recorded demo can be accessed from https://github.com/NLP-BigDataLab/QAnalysis-project .Our tool shows great flexibility in processing different kinds of statistic questions, which provides a convenient way for doctors to get statistical results directly in natural language.
A variational level set model for multiscale image segmentation

In this paper, we propose a variational level set model for multiscale image segmentation that can extract desired object from the background under various scales. The new model consists of three terms: A boundary extraction term, a regularization term and a multiscale representation term. The boundary extraction term is based on the Chan–Vese (CV) functional, which drives the active contour toward the boundaries of the objects. The regularization term penalizes the length of the active contour and keeps the level set function close to a signed distance function, which enables the active contour to maintain its stability during the evolution process. The multiscale representation term is based on the total variation (TV) energy that can provide a multiscale representation of the input image by tuning the scale parameter. We use an alternating iterative algorithm that combines the gradient descent algorithm and the alternating direction method of multipliers (ADMM) to numerically solve the model. The experimental results for both synthetic and real images demonstrate the robustness and effectiveness of the proposed model for multiscale image segmentation. In addition, the proposed model is a promising model for image denoising applications.
Secure EEG Signal Transmission for Remote Health Monitoring Using Optical Chaos

For the very first time, we present the use of optical chaos for the secure transmission of electroencephalogram (EEG) signals through optical fiber medium in remote health monitoring systems. In our proposed scheme, a semiconductor laser source is used to generate optical chaos, which hides EEG signal before its transmission over the optical fiber medium. The EEG signals are acquired by using a 14-channel Emotiv headset device, which are then processed and rescaled to be compatible with the experimental environment (Optisystem). The mixing of EEG signals and chaos is performed using additive chaos masking scheme, which exhibits certain useful properties such as simplicity and easy recovery of the message. Chaotic data (a combination of EEG signals and chaos) is sent over the optical fiber medium to investigate propagation issues associated with secure EEG signal transmission. The scheme is implemented for long haul communication in which the linear impairments of optical fiber are controlled for successful transmission of the secure signal. The parameters at transmitting and receiving sides are selected to achieve synchronization, such that the transmitted signal could be subtracted from identical chaos to restore the original EEG signal at the receiving side. The scheme is tested for different lengths of the optical fiber cable in which the quality of the received signal is determined by obtaining Q-factors. This scheme could also be used with medical signals such as electrocardiography and electromyography.
The discursive construction of mobile chronotopes in mobile-phone messaging

Abstract This article draws on data from an ethnographic project to explore the ways in which migrant micro-entrepreneurs exploit mobile messaging apps to co-construct mobile chronotopes: dynamic configurations of time and space negotiated by geographically separated participants, who draw on different contexts and frames of understanding. Analysis of mobile messages by two couples—Chinese butchers in Birmingham and Polish shop-owners in London—informed by interview and interactional data collected at work and home, suggests they discursively negotiate and exploit multiple chronotopic layers, creating complex intersections between virtual and physical spaces in everyday interactions. We focus on the role that multilingual and multimodal semiotic resources play in co-constructing mobile chronotopes. In particular, we explore critical junctures at which communicative expectations are challenged, rendering mobile chronotope negotiation visible. Our concept of the mobile chronotope has implications for both the theorisation of mobile phone communication and understanding how chronotopes function in contemporary transnational migrant discourse. (Chronotope, migrant entrepreneur, mobile messaging, transnational migration)*
Experimenting with prequential variations for data stream learning evaluation

Abstract Processing data streams requires new demands not existent on static environments. In online learning, the probability distribution of the data can often change over time (concept drift). The prequential assessment methodology is commonly used to evaluate the performance of classifiers in data streams with stationary and non‐stationary distributions. It is based on the premise that the purpose of statistical inference is to make sequential probability forecasts for future observations, rather than to express information about the past accuracy achieved. This article empirically evaluates the prequential methodology considering its three common strategies used to update the prediction model, namely, Basic Window, Sliding Window, and Fading Factors. Specifically, it aims to identify which of these variations is the most accurate for the experimental evaluation of the past results in scenarios where concept drifts occur, with greater interest in the accuracy observed within the total data flow. The prequential accuracy of the three variations and the real accuracy obtained in the learning process of each dataset are the basis for this evaluation. The results of the carried‐out experiments suggest that the use of Prequential with the Sliding Window variation is the best alternative.
Triple-level cryptosystem using deterministic masks and modified gerchberg-saxton iterative algorithm in fractional Hartley domain by positioning singular value decomposition

We present a novel method for image encryption by Modified Gerchberg–Saxton Iterative Algorithm (MGSIA) using deterministic masks (DMKs) and singular value decomposition (SVD) in fractional Hartley transform (FrHT) domain. This proposal delivers triple-level encryption (L1, L2 and L3) and all these three levels can be iterated number of times. First and third level of encryption routines MGSIA, DMK and FrHT, whereas mid-level encryption uses SVD. Since L1 delivers single cipher image and L3 yields double cipher images, this technique is also known as multiple image encryption system. Each MGSIA generates private keys and these keys are engendered in such a way that decryption is to be expected with traditional DRPE system. Because of the nature of these two asymmetric keys, the proposed system is non-linear and by the way, it boosts security. In the place of traditional random phase masks, the proposed cryptosystem uses statistically independent deterministic masks in L1 and L3. DMKs are nothing but the class of structured phase masks. DMKs are generated and used as the phase for MGSIA in L1 and L3. These DMKs are formed by the deviation from conventional rectangular function and limited range values which delivers key components with reduced size, better performance and lower complexity. Deployment of SVD on L2 decomposes the cipher image1 into three components and out of these three components, due to the nature of diagonality, S components plays a major role in proposed work. Numerical simulations have been achieved to prove the strength and feasibility of the proposed scheme.
Wizard of Oz Prototyping for Machine Learning Experiences

Machine learning is being adopted in a wide range of products and services. Despite its adoption, design and research processes for machine learning experiences have yet to be cemented in the user experience community. Prototyping machine learning experiences is noted to be particularly challenging. This paper suggests Wizard of Oz prototyping to help designers incorporate human-centered design processes into the development of machine learning experiences. This paper also surfaces a set of topics to consider in evaluating Wizard of Oz machine learning prototypes.
A human-like description of scene events for a proper UAV-based video content analysis

In Video Surveillance age, the monitoring activity, especially from unmanned vehicles, needs some degree of autonomy in the scenario interpretation. Video Analysis tasks are crucial for the target tracking and recognition; anyway, it would be desirable if a further level of understanding could provide a comprehensive, high-level scene description, by reflecting that human cognitive capability of providing a concise scene description that comes from the analysis of involved objects relationships and actions. This paper presents a smart system to identify mobile scene objects, such as people, vehicles, automatically, by analyzing the videos acquired by drones in flight, along with the activities they carried out, so as to depict what it happens in the scene from a high-level perspective. The system uses Artificial Vision methods to detect and track the mobile objects and the area where they move, and Semantic Web technologies to provide a high-level description of the scenario. Spatio/temporal relations among the tracked objects as well as simple object activities (events) are described. By semantic reasoning, the system is able to connect the simple activities into more complex activities, that better reflect a human-like description of a scenario portion. Tests conducted on several videos, showing scenarios set in different environments, return convincing results which affirm the effectiveness of the proposed approach.
Measuring Saccade Latency Using Smartphone Cameras

Objective: Accurate quantification of neurodegenerative disease progression is an ongoing challenge that complicates efforts to understand and treat these conditions. Clinical studies have shown that eye movement features may serve as objective biomarkers to support diagnosis and tracking of disease progression. Here, we demonstrate that saccade latency-an eye movement measure of reaction time-can be measured robustly outside of the clinical environment with a smartphone camera. Methods: To enable tracking of saccade latency in large cohorts of patients and control subjects, we combined a deep convolutional neural network for gaze estimation with a model-based approach for saccade onset determination that provides automated signal-quality quantification and artifact rejection. Results: Simultaneous recordings with a smartphone and a high-speed camera resulted in negligible differences in saccade latency distributions. Furthermore, we demonstrated that the constraint of chinrest support can be removed when recording healthy subjects. Repeat smartphone-based measurements of saccade latency in 11 self-reported healthy subjects resulted in an intraclass correlation coefficient of 0.76, showing our approach has good to excellent test-retest reliability. Additionally, we conducted more than 19000 saccade latency measurements in 29 self-reported healthy subjects and observed significant intra- and inter-subject variability, which highlights the importance of individualized tracking. Lastly, we showed that with around 65 measurements we can estimate mean saccade latency to within less-than-10-ms precision, which takes within 4 min with our setup. Conclusion and Significance: By enabling repeat measurements of saccade latency and its distribution in individual subjects, our framework opens the possibility of quantifying patient state on a finer timescale in a broader population than previously possible.
Privacy-Preserving Analysis of Distributed Biomedical Data: Designing Efficient and Secure Multiparty Computations Using Distributed Statistical Learning Theory

Biomedical research often requires large cohorts and necessitates the sharing of biomedical data with researchers around the world, which raises many privacy, ethical, and legal concerns. In the face of these concerns, privacy experts are trying to explore approaches to analyzing the distributed data while protecting its privacy. Many of these approaches are based on secure multiparty computations (SMCs). SMC is an attractive approach allowing multiple parties to collectively carry out calculations on their datasets without having to reveal their own raw data; however, it incurs heavy computation time and requires extensive communication between the involved parties.This study aimed to develop usable and efficient SMC applications that meet the needs of the potential end-users and to raise general awareness about SMC as a tool that supports data sharing.We have introduced distributed statistical computing (DSC) into the design of secure multiparty protocols, which allows us to conduct computations on each of the parties' sites independently and then combine these computations to form 1 estimator for the collective dataset, thus limiting communication to the final step and reducing complexity. The effectiveness of our privacy-preserving model is demonstrated through a linear regression application.Our secure linear regression algorithm was tested for accuracy and performance using real and synthetic datasets. The results showed no loss of accuracy (over nonsecure regression) and very good performance (20 min for 100 million records).We used DSC to securely calculate a linear regression model over multiple datasets. Our experiments showed very good performance (in terms of the number of records it can handle). We plan to extend our method to other estimators such as logistic regression.
Flaws in Flows: Unveiling Design Flaws via Information Flow Analysis

This paper presents a practical and formal approach to analyze security-centric information flow policies at the level of the design model. Specifically, we focus on data confidentiality and data integrity objectives. In its guiding principles, the approach is meant to be amenable for designers (e.g., software architects) that have very limited or no background in formal models, logics, and the like. To this aim, we provide an intuitive graphical notation, which is based on the familiar Data Flow Diagrams, and which requires as little effort as possible in terms of extra security-centric information the designer has to provide. The result of the analysis algorithm is the early discovery of design flaws in the form of violations of the intended security properties. The approach is implemented as a publicly available plugin for Eclipse and evaluated with four real-world case studies from publicly available literature.
An anomaly detection technique for business processes based on extended dynamic bayesian networks

Checking and analyzing various executions of different Business Processes can be a tedious task as the logs from these executions may contain lots of events, each with a (possibly large) number of attributes. We developed a way to automatically model the behavior captured in log files with dozens of attributes. The advantage of our method is that we do not need any prior knowledge about the data and the attributes. The learned model can then be used to detect anomalous executions in the data. To achieve this we extend the existing Dynamic Bayesian Networks with other (existing) techniques to better model the normal behavior found in log files. We introduce a new algorithm that is able to learn a model of a log file starting from the data itself. The model is capable of scoring events and cases, even when new values or new combinations of values appear in the log file, and has the ability to give a decomposition of the given score, indicating the root cause for the anomalies. Furthermore we show that our model can be used in a more general way for detecting Concept Drift.
Recurrent Neural Networks With External Addressable Long-Term and Working Memory for Learning Long-Term Dependences

Learning long-term dependences (LTDs) with recurrent neural networks (RNNs) is challenging due to their limited internal memories. In this paper, we propose a new external memory architecture for RNNs called an external addressable long-term and working memory (EALWM)-augmented RNN. This architecture has two distinct advantages over existing neural external memory architectures, namely the division of the external memory into two parts-long-term memory and working memory-with both addressable and the capability to learn LTDs without suffering from vanishing gradients with necessary assumptions. The experimental results on algorithm learning, language modeling, and question answering demonstrate that the proposed neural memory architecture is promising for practical applications.