Reviewerly/exHarmony huggingface dataset:
Generation and Validation of Broadband Synthetic P Waves in Semistochastic Models of Large Earthquakes

ABSTRACT We present an approach for generating stochastic scenario rupture models and semistochastic broadband seismic waveforms that include validated P waves, an important feature for application to early warning systems testing. There are few observations of large magnitude earthquakes available for development and refinement of early warning procedures; thus, simulated data are a valuable supplement. We demonstrate the advantage of using the Karhunen–Loève expansion method for generating stochastic scenario rupture models, as it allows the user to build in desired spatial qualities, such as a slip inversion, as a mean background slip model. For waveform computation, we employ a deterministic approach at low frequencies (&amp;lt;1 Hz) and a semistochastic approach at high frequencies (&amp;gt;1 Hz). Our approach follows Graves and Pitarka (2010) and extends to model P waves. We present the first validation of semistochastic broadband P waves, comparing our waveforms against observations of the 2014 Mw 8.1 Iquique, Chile, earthquake in the time domain and across frequencies of interest. We then consider the P waves in greater detail, using a set of synthetic waveforms generated for scenario ruptures in the Cascadia subduction zone. We confirm that the time-dependent synthetic P-wave amplitude growth is consistent with previous analyses and demonstrate how the data could be used to simulate earthquake early warning procedures.
Mechanism analysis of non-inertial particle swarm optimization for Internet of Things in edge computing

This paper carries out the mechanism analysis of non-inertial particle swarm optimization. It is a modified algorithm based on a new kinetic equation which is applied to the prediction of non-stationary time series for the internet of things in the edge computing. In order to avoid premature convergence and accelerate convergence rate, different from standard particle swarm optimization, the modified algorithm uses a new kinetic equation to lead particles motion direction, besides generalized opposition-based learning (GOBL) and adaptive elite mutation (AEM) strategies. The work presents the second order standardized recurrence equation for the new kinetic equation whose corresponding characteristic equations can be analyzed via the difference functions to obtain the boundaries of coefficients and its convergence region. Besides, GOBL and AEM strategies are also analyzed to boost global and local convergence of the algorithm as an interpretation. It is illustrated that performance analysis of the algorithm with two well-known test functions. The good performance is further validated from application in edge computing.
Watermarking-Based Secure Plaintext Image Protocols for Storage, Show, Deletion and Retrieval in the Cloud

In this article, we propose secure plaintext image storage protocols in the cloud environment for image owners managing and controlling their outsourced images. To solve control and privacy issues, conventional schemes suggest outsourcing images in the encrypted form. However, encrypted images lose their usability and visibility. For example, image owners cannot quickly find their stored images in the cloud. The proposed protocols encourage plaintext image storage in the cloud with copyright protection of images, visible display, lossless retrieval and controlled deletion via techniques of homomorphic encryption and digital watermarking. We allow the image owner to embed and remove the watermark in a privacy-preserving way without compromising the security of the original data and the computed results. Moreover, the image owner can securely detect the cloud's dishonest act of not completely deleting an image as required or leaking an image without permission. Compared with existing works, our work achieves more functions. We prove that the proposed work achieves the controllable management of the outsourced plaintext images without privacy leakage to unauthorized parties and demonstrate the utility and the efficiency of our protocols through experimental evaluation.
Personalised Chats with Voice Assistants

Recent research suggests that adapting a voice assistant's personality to the user can improve the interaction experience. We present a pragmatic and practical approach to adapting voice assistant personality. We asked users to take the voice assistant's perspective and write their "ideal" voice assistant-user dialogue in different scenarios in an automotive context. Our results indicate individual differences in participants' preference for social or purely functional conversations with the voice assistant.
Integrating a cognitive assistant within a critique-based recommender system

Recommender systems are cognitive computing systems designed to support humans in their decision-making processes through convincing, timely product suggestions. In the field of recommender systems, critique-based recommenders have been widely applied as an effective approach for guiding users through a product space in pursuit of suitable products. To date, no critique-based approach has included an assistant that support users in their search in a pleasant way. In this paper, we describe how we integrate an assistant within a critique-based recommender. We consider the proposed assistant to be cognitive because its reasoning process when recommending products is based on a cognitively-inspired clustering algorithm. The proposal is evaluated by users and compared with a non-assistant approach. The results of this research demonstrate that the integration of a cognitive assistant within the recommender improves the user experience and increases the performance of the recommendation process, i.e., users need fewer cycles to achieve the desired product or service.
MPTCP Meets Big Data: Customizing Transmission Strategy for Various Data Flows

In recent years, network transmission carries big data of various types and sizes, which often have different transmission requirements. Transmission of data brings challenges to traditional transport layer protocols; meanwhile, performance improvements at endpoints and networks provide opportunities for new transport protocol design. Among the new designed transport layer protocols, multipath TCP is a promising one that provides better performance with multiple paths transmitting data simultaneously. However, when dealing with different types and sizes of data, MPTCP does not always work well, as all data streams are processed based on the same transmission strategy, which is simple but not always optimal. To fill this gap, in this article, we first give optimal strategies for different data flows, and propose a cross-layer solution for MPTCP, named FSA-MPTCP, to automatically provide flexible and optimal transmission strategies for different data flows. Finally, we conduct some experiments, and the results show that FSAMPTCP adaptively provides better performance for different kinds of flows.
Prediction of Chronic Diseases With Multi-Label Neural Network

Chronic diseases have seriously affected human activities, especially in many developing countries and underdeveloped countries. The long duration of chronic diseases and the high cost of medical care have placed a huge economic burden on society and families. Meanwhile, chronic patients tend to have a variety of complications over time. So, it is difficult for doctors to find effective diagnosis and appropriate treatment. Machine learning techniques can integrate their heterogeneous data of various body indicators. Meanwhile, for chronic patients, multi-label learning methods can be used to help doctors identify the types of the chronic diseases. This paper proposes a novel multi-label neural network method (ML-NN) to predict the chronic diseases combining neural network and multi-label learning technology based on cross entropy lost function and backward propagation algorithm. Compared with 14 traditional multi-label learning methods on 10 chronic diseases and 19733 patients, the proposed method achieved a consistently best in 5 performance measurements. The results demonstrate the proposed method can effectively predict chronic diseases and assist doctors to diagnose and treat patients.
Adversarial attacks on text classification models using layer‐wise relevance propagation

Due to the nested nonlinear structure inside neural networks, most existing deep learning models are treated as black boxes, and they are highly vulnerable to adversarial attacks. On the one hand, adversarial examples shed light on the decision-making process of these opaque models to interrogate the interpretability. On the other hand, interpretability can be used as a powerful tool to assist in the generation of adversarial examples by affording transparency on the relative contribution of each input feature to the final prediction. Recently, a post-hoc explanatory method, layer-wise relevance propagation (LRP), shows significant value in instance-wise explanations. In this paper, we attempt to optimize the recently proposed explanation-based attack algorithms (EAAs) on text classification models with LRP. We empirically show that LRP provides good explanations and benefits existing EAAs notably. Apart from that, we propose a LRP-based simple but effective EAA, LRPTricker. LRPTricker uses LRP to identify important words and subsequently performs typo-based perturbations on these words to generate the adversarial texts. The extensive experiments show that LRPTricker is able to reduce the performance of text classification models significantly with infinitesimal perturbations as well as lead to high scalability.
The Comparison of Color Texture Features Extraction based on 1D GLCM with Deep Learning Methods

In this paper, the comparison between deep learning methods and feature extraction algorithms is presented. The principle of Grey-Level Co-occurrence Matrix (GLCM) and its modifications are used for our research. The main idea was to design a method for the description of combined features and textures. The texture classification process is carried out with the robust support vector machine classifier (SVM). We compare these feature extraction methods with proposed Convolutional Neural Networks (CNN). This proposed network contains 25 layers. Finally, the all evaluation and comparison of color texture retrieval results for all used methods are presented. The all feature extraction algorithms and proposed CNN have been tested on two different color texture datasets (Outex and Vistex datasets).
Learning Efficient Representations of Mouse Movements to Predict User Attention

Tracking mouse cursor movements can be used to predict user attention on heterogeneous page layouts like SERPs. So far, previous work has relied heavily on handcrafted features, which is a time-consuming approach that often requires domain expertise. We investigate different representations of mouse cursor movements, including time series, heatmaps, and trajectory-based images, to build and contrast both recurrent and convolutional neural networks that can predict user attention to direct displays, such as SERP advertisements. Our models are trained over raw mouse cursor data and achieve competitive performance. We conclude that neural network models should be adopted for downstream tasks involving mouse cursor movements, since they can provide an invaluable implicit feedback signal for re-ranking and evaluation.
Body Movement-Based Controlling Through Passive RFID Integrated Into Clothing

We present a passive ultra-high frequency (UHF) radiofrequency identification (RFID)-based strain sensor, which is designed for simple and efficient body movement monitoring. This RFID platform is fabricated from electro-textile materials and can thus be seamlessly integrated into our everyday clothing. The sensor platform has an integrated reference tag, in order to avoid the effects of reflections or external disturbances on the sensor tag performance. This sensor platform prototype has an on-body read range of 1 meter in a normal office environment with an off-the-shelf RFID reader. The wireless performance of the sensor tag has a significant change caused by arm elongation. Thus, the sensor functionality can be based on variation of the sensor tag's backscattered power percentage (ΔP%). Based on the preliminary results achieved in this study, this passive and cost-effective sensor platform could be an efficient future way to turn human gestures into inputs for digital devices.
A Survey of Languages for Formalizing Mathematics

In order to work with mathematical content in computer systems, it is necessary to represent it in formal languages. Ideally, these are supported by tools that verify the correctness of the content, allow computing with it, and produce human-readable documents. These goals are challenging to combine and state-of-the-art tools typically have to make difficult compromises. In this paper we discuss languages that have been created for this purpose, including logical languages of proof assistants and other formal systems, semi-formal languages, intermediate languages for exchanging mathematical knowledge, and language frameworks that allow building customized languages. We evaluate their advantages based on our experience in designing and applying languages and tools for formalizing mathematics. We reach the conclusion that no existing language is truly good enough yet and derive ideas for possible future improvements.
Multi-task learning based on question–answering style reviews for aspect category classification and aspect term extraction on GPU clusters

Abstract Cluster computing technologies are rapidly advancing and user-generated online reviews are booming in the current Internet and e-commerce environment. The latest question–answering (Q&amp;A)-style reviews are novel, abundant and easily digestible product reviews that also contain massive valuable information for customers. In this paper, we mine valuable aspect information of products contained in these reviews on GPU clusters. To achieve this goal, we utilize two subtasks of aspect-based sentiment analysis: aspect term extraction (ATE) and aspect category classification (ACC). Most previous works focused on only one task or solved these two tasks separately, even though they are highly interrelated, and they do not make full use of abundant training resources. To address this problem, we propose a novel multi-task neural learning model to jointly handle these two tasks and explore the performance of our model on GPU clusters. We conducted extensive comparative experiments on an annotated corpus and found that our proposed model outperforms several baseline models in ATE and ACC tasks on GPU clusters, yielding significant strides in data mining for these types of reviews.
Omni-Drone: on the Design of a Novel Aerial Manipulator with Omni-directional Workspace

Aerial manipulation is a nascent research area that offers major impact for infrastructure monitoring and repair. While several design and control methods have been presented, there is still a need for new mechatronic solutions that are structurally optimised for aerial manipulation tasks. In this paper we present a novel design for a manipulator tailored for aerial applications with a high level of morphological integration with the robot frame. A hybrid system is presented that comprises a 5-bar linkage parallel robot with an additional active joint for the swirling motion about a pivotal point. The design offers an omnidirectional workspace about the aerial vehicle, enhancing the versatility of the aerial system and the tasks that can be accomplished. The mechanical design of the proposed robot, the analysis of the kinematics and the study of the workspace are presented. The novel manipulator represents the first of its kind, enabling aerial interaction with ceilings, curved surfaces and side interaction with facades.
How to Reduce Computation Time While Sparing Performance During Robot Navigation? A Neuro-Inspired Architecture for Autonomous Shifting Between Model-Based and Model-Free Learning

Taking inspiration from how the brain coordinates multiple learning systems is an appealing strategy to endow robots with more flexibility. One of the expected advantages would be for robots to autonomously switch to the least costly system when its performance is satisfying. However, to our knowledge no study on a real robot has yet shown that the measured computational cost is reduced while performance is maintained with such brain-inspired algorithms. We present navigation experiments involving paths of different lengths to the goal, dead-end, and non-stationarity (i.e., change in goal location and apparition of obstacles). We present a novel arbitration mechanism between learning systems that explicitly measures performance and cost. We find that the robot can adapt to environment changes by switching between learning systems so as to maintain a high performance. Moreover, when the task is stable, the robot also autonomously shifts to the least costly system, which leads to a drastic reduction in computation cost while keeping a high performance. Overall, these results illustrates the interest of using multiple learning systems.
Contribution of Motion Parallax and Stereopsis to the Sense of Presence in Virtual Reality

The sense of presence is defined as a subjective feeling of being situated in an environment and occupying a location therein. The sense of presence is a defining feature of virtual environments. In two experiments, we aimed at investigating the relative contribution of motion parallax and stereopsis to the sense of presence, using two versions of the classic pit room paradigm in virtual reality. In Experiment 1, participants were asked to cross a deep abyss between two platforms on a narrow plank. Participants completed the task under three experimental conditions: (1) when the lateral component of motion parallax was disabled, (2) when stereopsis was disabled, and (3) when both stereopsis and motion parallax were available. As a subjective measure of presence, participants completed a presence questionnaire after each condition. Additionally, electrodermal activity (EDA) was recorded as a measure of anxiety. In Experiment 1, EDA responses were significantly higher with restricted motion parallax as compared to the other two conditions. However, no difference was observed in terms of the subjective presence scores across the three conditions. To test whether these results were due to the nature of the environment, participants in Experiment 2 experienced a slightly less stressful environment, where they were asked to stand on a ledge and drop virtual balls to specified targets into the abyss. The same experimental manipulations were used as in Experiment 1. Again, the EDA responses were significantly higher when motion parallax was impaired as compared to when stereopsis was disabled. The results of the presence questionnaire revealed a reduced sense of presence with impaired motion parallax compared to the normal viewing condition. Across the two experiments, our results unexpectedly demonstrate that presence in the virtual environments is not necessarily linked to EDA responses elicited by affective situations as has been implied by earlier studies.
Convolutional Neural Network With Developmental Memory for Continual Learning

Convolutional neural networks (CNNs) are one of the most successful deep neural networks. Indeed, most of the recent applications related to computer vision are based on CNNs. However, when learning new tasks in a sequential manner, CNNs face catastrophic forgetting: they forget a considerable amount of previously learned tasks while adapting to novel tasks. To overcome this main barrier to continual learning with CNNs, we introduce developmental memory (DM) into a CNN, continually generating submemory networks to learn important features of individual tasks. A novel training method, referred to here as guided learning (GL), guides the newly generated submemory to become an expert on the new task, eventually improving the performance of the overall network. At the same time, the existing submemories attempt to preserve the knowledge of old tasks. Experiments on image classification tasks show that compared with the state-of-the-art algorithms, the proposed CNN with DM not only improves the classification performance on the new image task but also leads to less forgetting of previous image tasks to facilitate continual learning.
Masking Quantum Information Encoded in Pure and Mixed States

Masking of quantum information means that information is hidden from a subsystem and spread over a composite system. Modi et al. proved in [Phys. Rev. Lett. 120, 230501 (2018)] that this is true for some restricted sets of nonorthogonal quantum states and it is not possible for arbitrary quantum states. In this paper, we discuss the problem of masking quantum information encoded in pure and mixed states, respectively. Based on an established necessary and sufficient condition for a set of pure states to be masked by an operator, we find that there exists a set of four states that can not be masked, which implies that to mask unknown pure states is impossible. We construct a masker S♯ and obtain its maximal maskable set, leading to an affirmative answer to a conjecture proposed in Modi's paper mentioned above. We also prove that an orthogonal (resp. linearly independent) subset of pure states can be masked by an isometry (resp. injection). Generalizing the case of pure states, we introduce the maskability of a set of mixed states and prove that a commuting subset of mixed states can be masked by an isometry S◇ while it is impossible to mask all of mixed states by any operator. We also find the maximal maskable sets of mixed states of the isometries S♯ and S◇, respectively.
Training set selection and swarm intelligence for enhanced integration in multiple classifier systems

Multiple classifier systems (MCSs) constitute one of the most competitive paradigms for obtaining more accurate predictions in the field of machine learning. Systems of this type should be designed efficiently in all of their stages, from data preprocessing to multioutput decision fusion. In this article, we present a framework for utilizing the power of instance selection methods and the search capabilities of swarm intelligence to train learning models and to aggregate their decisions. The process consists of three steps: First, the essence of the complete training data set is captured in a reduced set via the application of intelligent data sampling. Second, the reduced set is used to train a group of heterogeneous classifiers using bagging and distance-based feature sampling. Finally, swarm intelligence techniques are applied to identify a pattern among multiple decisions to enhance the fusion process by assigning class-specific weights for each classifier. The proposed methodology yielded competitive results in experiments that were conducted on 25 benchmark datasets. The Matthews correlation coefficient (MCC) is regarded as the objective to be maximized by various nature-inspired metaheuristics, which include the moth-flame optimization algorithm (MFO), the grey wolf optimizer (GWO) and the whale optimization algorithm (WOA).