Reviewerly/exHarmony huggingface dataset:
NASM: Nonlinearly Attentive Similarity Model for Recommendation System via Locally Attentive Embedding

Recommendation system, as a core service for many customer-oriented online services, is employed to predict the personalized rating of users on their potentially preferable items. In modern industrial settings, an item-based collaborative filtering (item-based CF) method has been long popular owing to its excellent interpretability and high efficiency in the real-time personalized recommendation. In this model, the current target item is recommended according to the interacted similarity from the user's profile, which implies that the key of item-based CF is in the estimation of historical item similarity. Early studies usually utilize statistical measures including cosine similarity and Pearson correlation coefficient to estimate similarity with low accuracy caused by lacking optimization tailed. Recently, there are some learning-based models attempting to learn item similarity by optimizing a recommendation-aware loss function. However, these efforts are mainly concentrated on the application of the shallow linear model, and relative works that deploy some deep learning components for item-based CF are scarce. In this paper, we propose a nonlinearly attentive similarity model (NASM) for item-based CF via locally attentive embedding by introducing local attention and novel nonlinear attention to capture local and global item information, simultaneously. The NASM is based on a neural attentive item similarity (NAIS) model and further achieves significantly superior performance. The experimental results demonstrate that the NASM achieves more competitive recommendation performance in terms of hit ration (HR) and the normalized discounted cumulative gain (NDGC) in comparison with other state-of-the-art recommendation models.
A Proposed Virtual Private Cloud-Based Disaster Recovery Strategy

Disaster is an unexpected event in a system lifetime, which can be made by nature or even human errors. Disaster recovery of information technology is an area of information security for protecting data against unsatisfactory events. It involves a set of procedures and tools for returning an organization to a state of normality after an occurrence of a disastrous event. So the organizations need to have a good plan in place for disaster recovery. There are many strategies for traditional disaster recovery and also for cloud-based disaster recovery. This paper focuses on using cloud-based disaster recovery strategies instead of the traditional techniques, since the cloud-based disaster recovery has proved its efficiency in providing the continuity of services faster and in less cost than the traditional ones. The paper introduces a proposed model for virtual private disaster recovery on cloud by using two metrics, which comprise a recovery time objective and a recovery point objective. The proposed model has been evaluated by experts in the field of information technology and the results show that the model has ensured the security and business continuity issues, as well as the faster recovery of a disaster that could face an organization. The paper also highlights the cloud computing services and illustrates the most benefits of cloud-based disaster recovery.
Research on inheritance and innovation mode of erhu art development based on intelligent algorithm

In order to further promote the development of erhu, an inheritance and innovation model research in the development of erhu art is proposed based on intelligent algorithm. The development of erhu is expounded, with the intelligent algorithm used to construct the erhu learning model, which can sim plify the skills in erhu learning, make it more easy to understand, and get better inheritance and development. An optimization and updating scheme is proposed from the algorithm flow and evaluation model. By effectively evaluating the data connection state of the erhu art model, an effective identification model is established, and the corresponding optimization results are given. In the test for the erhu learning model, the efficiency of the erhu teaching and the data accuracy of the computer algorithm are tested. The test results show that the addition of artificial intelligence makes the erhu teaching more simple, and the teaching content is more accurate, which is worth further promotion and development.
Supernumerary Body Schema Extension to Non-Corporeal Object by Adding Artificial Tactile Feedback using Electrical Stimulation

Technological advancement could increase the degree of freedom in devices and tools, which often results in complex control interface. To decrease the learning time and increase usability of complex control interface, intuitiveness becomes an important issue. One interesting and powerful solution to increase intuitiveness in controlling non-corporeal objects is integrating them to the existing body schema. The hypothesis here is that this can be done by establishing a bi-directional communication interface between the body and the non-corporeal object (i.e., establishing control and sensory feedback, along with congruent visual feedback). In this study, a table tennis racket was modified to provide a bi-directional communication interface between the table tennis racket and the peripheral nervous system on the right palm. Two types of experiments were conducted on a single subject to test the hypothesis of body schema extension. The first experiment was designed to test congruent mapping of racket locations to the hand. The second experiment was designed to test the accuracy of perceived location along with the ownership and agency. We measured the error between the location of local pressure on the table tennis racket and the perceived location on the hand, along with a subjective questionnaire on ownership and agency of the table tennis racket. The first experiment confirmed an accurate and congruent mapping of the racket onto the hand. The second experiment showed a significant decrease in error in perceived location, with t-tests displaying statistical difference, after the subject was trained with the bi-directional interface. This study suggests that a bi-directional interface may extend the body schema to include non-corporeal objects.
Discriminative Multi-View Subspace Feature Learning for Action Recognition

Although deep features have achieved the state-of-the-art performance in action recognition recently, the hand-crafted shallow features still play a critical role in characterizing human actions for taking advantage of visual contents in an intuitive way such as edge features. Therefore, the shallow features can serve as auxiliary visual cues supplementary to deep representations. In this paper, we propose a discriminative subspace learning model (DSLM) to explore the complementary properties between the hand-crafted shallow feature representations and the deep features. As for the RGB action recognition, this is the first work attempting to mine multi-level feature complementaries by the multi-view subspace learning scheme. To sufficiently capture the complementary information among heterogeneous features, we construct the DSLM by integrating the multi-view reconstruction error and classification error into an unified objective function. To be specific, we first use Fisher Vector to encode improved dense trajectories (iDT+FV) for shallow representations and two-stream convolutional neural network models (T-CNN) for generating deep features. Moreover, the presented DSLM algorithm projects multi-level features onto a shared discriminative subspace with the complementary information and discriminating capacity simultaneously incorporated. Finally, the action types of test samples are identified by the margins from the learned compact representations to the decision boundary. The experimental results on three datasets demonstrate the effectiveness of the proposed method.
A simple kernel co‐occurrence‐based enhancement for pseudo‐relevance feedback

Pseudo‐relevance feedback is a well‐studied query expansion technique in which it is assumed that the top‐ranked documents in an initial set of retrieval results are relevant and expansion terms are then extracted from those documents. When selecting expansion terms, most traditional models do not simultaneously consider term frequency and the co‐occurrence relationships between candidate terms and query terms. Intuitively, however, a term that has a higher co‐occurrence with a query term is more likely to be related to the query topic. In this article, we propose a kernel co‐occurrence‐based framework to enhance retrieval performance by integrating term co‐occurrence information into the Rocchio model and a relevance language model (RM3). Specifically, a kernel co‐occurrence‐based Rocchio method (KRoc) and a kernel co‐occurrence‐based RM3 method (KRM3) are proposed. In our framework, co‐occurrence information is incorporated into both the factor of the term discrimination power and the factor of the within‐document term weight to boost retrieval performance. The results of a series of experiments show that our proposed methods significantly outperform the corresponding strong baselines over all data sets in terms of the mean average precision and over most data sets in terms of P@10. A direct comparison of standard Text Retrieval Conference data sets indicates that our proposed methods are at least comparable to state‐of‐the‐art approaches.
Rubber tail illusion is weakened in Ca2+-dependent activator protein for secretion 2 (Caps2)-knockout mice

Abstract Body ownership is a fundamental aspect of self-consciousness. Illusion of body ownership is caused by updating body representation through multisensory integration. Synchronous visuotactile stimulation of a hand and rubber hand leads to illusory changes in body ownership in humans, but this is impaired in individuals with autism spectrum disorder (ASD). We previously reported that mice also exhibit body ownership illusion. With synchronous stroking of a tail and rubber tail, mice responded as if their own tails were being touched when the rubber tails were grasped (‘rubber tail illusion’). However, it remains unknown whether deficits in illusion of body ownership occur in mouse models of autism. Here, we examined whether the ‘rubber tail illusion’ occurred in Ca 2+ -dependent activator protein for secretion 2 - knockout ( Caps2 -KO) mice, which exhibit autistic-like phenotypes. During the synchronous stroking, response rates were significantly lower in Caps2 -KO mice than in wild-type mice. There were no significant differences between the response rates of wild-type and Caps2 -KO mice during the asynchronous stroking. The ‘rubber tail illusion’ was weak in Caps2 -KO mice, suggesting that Caps2 -KO mice experienced weaker visuotactile integration during the task. The rubber tail task will be a useful tool in mouse models of autism to evaluate atypical sensory processing.
Unified Cloud Access Control Model for Cloud Storage Broker

Cloud Storage Broker (CSB) provides value-added cloud storage service for enterprise usage by leveraging multi-cloud storage architecture. However, it raises several challenges for managing resources and its access control in multiple Cloud Service Providers (CSPs) for authorized CSB stakeholders. In this paper we propose unified cloud access control model that provides the abstraction of CSP's services for centralized and automated cloud resource and access control management in multiple CSPs. Our proposal offers role-based access control for CSB stakeholders to access cloud resources by assigning necessary privileges and access control list for cloud resources and CSB stakeholders, respectively, following privilege separation concept and least privilege principle. We implement our unified model in a CSB system called CloudRAID for Business (CfB) with the evaluation result shows it provides system-and-cloud level security service for cfB and centralized resource and access control management in multiple CSPs.
Determining the Neural Network Topology

One of the challenges in the successful implementation of deep neural network (DNN) is setting the value for various hyper-parameters, one of which is the network topology, which is closely related to the number of hidden layers and the number of hidden neurons. Determining the number of hidden layers and the number of neurons is very important and has a large influence on DNN performance. Determining these two numbers manually (usually through trial and error methods) to find fairly optimal arrangement is a time-consuming process, while the automatic approach is divided into two, they are a model-based approach and a non-model based approach. The non-model-based approach, for example, is grid search and random search, whereas model-based approaches, for example, are using particle swarm optimization (PSO) algorithms. In some researches, how to determine the number of hidden layers or number of neurons, often the guidelines are unclear, even the roles and functions of both are explained minimally. Although it is still a difficult area of research, research to determine the number of hidden layers and the number of neurons must continue to be carried out, because these two numbers will greatly determine the performance of DNN.
Facial Expression Recognition Algorithm Based on the Combination of CNN and K-Means

Aiming at the problems of low recognition rate and slow training speed of facial expression recognition method in the background of complex images, an improved facial expression recognition algorithm based on convolutional neural networks is proposed. The proposed algorithm introduces K-Means clustering idea and SVM classifier in the framework of convolutional neural network. Firstly, the algorithm trains the K-Means clustering model by using the label-free expression images, and selects the K-means clustering centers with good data characteristics, which are used as the initial value of the convolution kernel of the CNN model to extract features. Secondly, using the feature extraction processing of the convolutional neural network, the extracted features are fed to the multi-class SVM classifier. The experimental results show that the proposed method reduces the training time of the model overall, improves the accuracies of facial expression recognition under the background of complex images, and has a certain robustness.
Human Eye Movements Reveal Video Frame Importance

Human eye movements indicate important spatial information in static images and videos. However, videos contain additional temporal information and convey a storyline. We explore whether eye movement patterns reflect frame importance during video viewing and facilitate video summarization.
Circular fringe projection technique for out-of-plane deformation measurements

Fringe projection is a widely used technique for measurement of out-of-plane deformation of diffusely reflecting objects. Among the various fringe patterns employed for projection, usage of linear fringes is most common. However, the phase function of linear fringes has 2π periodicity, thereby leading to ambiguity in determining whole-field out-of-plane deformation of dynamic objects. This paper presents a new method of measurement of whole-field out-of-plane deformation of targets by projecting a circular fringe pattern. New technique of fringe analysis is introduced to estimate the underlying phase distribution of the fringe pattern from a single image. In this technique, the fringe pattern is coordinate transformed into polar coordinates, and then Fourier fringe analysis technique is employed to obtain its phase. Subsequent to unwrapping, the phase map is re-transformed to Cartesian coordinates. The phase map obtained after coordinate transformation is converted to out-of-plane deformation map using camera calibration parameters. Simulations are carried out to establish the analysis method. Experimental validation is performed with the development of circular grating based fringe projection system.
CRLB for Estimating Time-Varying Rotational Biases in Passive Sensors

In target tracking systems involving data fusion it is common to encounter sensor measurement biases that contribute to the tracking errors. There is extensive research into estimating sensor biases, but very little research into bias estimation in the dynamic case, meaning that biases that change over time are addressed. This paper investigates the means for and necessity of estimating bias rates of change in addition to constant sensor biases to reduce the errors in the state estimates. This is explored by comparing the Cramer–Rao lower bound and root-mean-square error of simultaneous target state and bias estimates for rotational biases with three-dimensional passive sensors with roll, pitch, and yaw biases. The present work models the dynamic biases as linearly varying over time. The iterated least squares method is used for the search of the maximum likelihood estimate, and is shown to be statistically efficient via hypothesis testing.
On the Nature of Automotive Service Architectures

Context: Current trends in the automotive industry like automated driving, machine intelligence, and digitisation introduce more and more complex functions and their interplay in in-vehicle architectures. To cope with this complexity, car makers are introducing particularly tailored service-oriented architectures. Aim: We aim at developing a formal and methodological approach to model and structure automotive service architectures. Method: We present a formal model to specify and group automotive service-oriented architectures following a classification scheme. Furthermore, a well-defined property model is introduced facilitating the specification and verification of deployment and run-time requirements. The approach is illustrated using a realistic case example. Conclusion: The presented approach works well for the used case example but needs to be further evaluated in depth in the future. However, we strongly believe that the framework is promising and will receive further attention in future work.
Why Should I Trust a Blockchain Platform? Designing for Trust in the Digital Car Dossier

Trust is a crucial component for successful transactions regardless of whether they are executed in physical or virtual spaces. Blockchain technology is often discussed in the context of trust and referred to as a trust-free, trustless, or trustworthy technology. However, the question of how the trustworthiness of blockchain platforms should be demonstrated and proven to end users still remains open. While there may be some genuine trust in the blockchain technology itself, on an application level trust in an IT artifact needs to be established. In this study, we examine how trust-supporting design elements may be implemented to foster an end user’s trust in a blockchain platform. We follow the design science paradigm and suggest a practically useful set of design elements that can help designers of blockchain platforms to build more trustworthy systems.
Radio frequency classification toolbox for drone detection

There is a need for Radio Frequency Signal Classification (RF-Class) toolbox which can monitor, detect, and classify wireless signals. Rapid developments in the unmanned aerial systems (UAS) have made its usage in a variety of offensive as well as defensive applications especially in military, high priority and sensitive government sites. The ability to accurately classify over-the-air radio signals will provide insights into spectrum utilization, device fingerprinting and protocol identification. These insights can help the Warfighter to constantly be informed about adversarys transmitters capabilities without their knowledge. Recently, few researches have proposed feature-based machine learning techniques to classify RF signals. However, these researches are mostly evaluated on simulated environments, less accurate, and failed to explore advance machine learning techniques. In this research, we proposed a feature-engineering based signal classification (RF-class) toolbox which implements RF signal detection, Cyclostationary Features Extraction and Feature engineering, Automatic Modulation Recognition to automatically recognize modulation as well as sub-modulation types of the received signal. To demonstrate the feasibility and accuracy of our approach, we have evaluated the performance on a real environment with an UAS (Drone DJI Phantom 4). Our initial experimental result showed that we were able to detect presence of drone signal successfully when power on and transmitting. And further experiments are under progress.
‘I gave someone a good death’: Anonymity in a community of Reddit’s medical professionals

Anonymity is often regarded as a negative influence on the quality of online communication, but can anonymity serve to bridge communication gaps under specific conditions? This study explores this question by examining key affordances of Reddit using a quasi-ethnographic method. Here, anonymity is combined with active norm enforcement by self-appointed moderators of subReddits. This study develops the concept of actively moderated anonymity and assesses its ability to mend gaps in communication through an online ethnography of Medicine, a Reddit community of self-identified medical professionals to answer the following: How does anonymity work as an affordance in shaping online discussions, such as those in r/medicine, and what types of exchanges are occurring between medical professionals in the r/medicine community that may not occur elsewhere? r/medicine’s community provided a place for comfort and a place to freely exchange ideas including sensitive topics and opposing viewpoints. Findings conclude that medical professionals often sought to validate decisions they had made in the workplace and attempted to resolve concerns about particular institutional or systemic issues, suggesting actively moderated anonymity may afford an additional level of support that medical professionals may not find in the workplace or within an identifiable setting that could potentially be invaluable to the services provided to patients.
Medical Self-Diagnostic System Using Artificial Neural Networks

Recently, intelligent applications become part of our lives. In healthcare, there exist several medical self-diagnostic systems. However, only a few of them are available to the public. That is, these systems are attached to certain hospitals and only the patients of these hospitals can use such systems. In addition, some of these systems are not free. This paper proposes a medical-self diagnostic for cervical cancer disease. The system takes the disease symptoms and then predicts the availability of the disease using artificial neural network method. It also directs the patients to doctors in their area. In addition, it provides the patients with more information about the disease by directing them to some related medical websites. As future work, it is planned to extend the system to include more diseases.
Simulated Kalman Filter Optimization Algorithm for Maximization of Wireless Sensor Networks Coverage

Simulated Kalman Filter (SKF) is a population based optimization algorithm inspired by the Kalman filtering method. It had been successfully used for optimization of many engineering problems. In this work SKF is applied for wireless sensor networks (WSN) coverage optimization problem, where the objective is to maximize the area covered by the sensors in a region of interest. Coverage is an important issue in WSN. It is used as one of the measurement metric for a WSN's quality of service. Many metaheuristics algorithms had been applied to solve this problem. Here, SKF is tested over several WSN and found to be able to perform better than particle swarm optimization (PSO) and genetic algorithm (GA) in improving WSN coverage.