Reviewerly/exHarmony huggingface dataset:
Perturbation-Hidden: Enhancement of Vehicular Privacy for Location-Based Services in Internet of Vehicles

The great development of smart networks enables Internet of Vehicles (IoV) as a promising paradigm to provide pervasive services, where privacy issues for location-based services (LBSs) have attracted considerable attention. In terms of location privacy, inspired by differential privacy, geo-indistinguishability (Geo-Ind) has recently become a prevalent privacy model for LBSs. Although Geo-Ind guarantees the location privacy, users’ other privacy concerns are still at risk if the location perturbation behavior is exposed due to implausible reported locations. Through experiments we find the probability that the classical Geo-Ind mechanism perturbs the true location to implausible areas can be more than 50%. To address it, we first propose an enhanced privacy definition beyond Geo-Ind, called Perturbation-Hidden, to prevent location perturbation behaviors of users from being recognized by guaranteeing their pseudo-locations plausible. Then we design a mechanism to achieve this definition by transplanting the differential private exponential mechanism to our approach. Furthermore, we propose a method for determining the retrieval area utilizing dynamic programming to ensure the accuracy of LBSs. Finally, we theoretically prove that our mechanism satisfies the privacy definition. Extensive experiments on simulations and a real-world dataset show that our proposal achieves 100% plausible pseudo-locations while ensuring high query precision and recall.
A Convolutional Neural Network and Matrix Factorization-Based Travel Location Recommendation Method Using Community-Contributed Geotagged Photos

Travel location recommendation methods using community-contributed geotagged photos are based on past check-ins. Therefore, these methods cannot effectively work for new travel locations, i.e., they suffer from the travel location cold start problem. In this study, we propose a convolutional neural network and matrix factorization-based travel location recommendation method to address the problem. Specifically, a weighted matrix factorization method is used to obtain the latent factor representations of travel locations. The latent factor representation for a new travel location is estimated from its photos by using a convolutional neural network. Experimental results on a Flickr dataset demonstrate that the proposed method can provide better recommendations than existing methods.
Attack Selectivity of Adversarial Examples in Remote Sensing Image Scene Classification

Remote sensing image (RSI) scene classification is the foundation and important technology of ground object detection, land use management and geographic analysis. During recent years, convolutional neural networks (CNNs) have achieved significant success and are widely applied in RSI scene classification. However, crafted images that serve as adversarial examples can potentially fool CNNs with high confidence and are hard for human eyes to interpret. For the increasing security and robust requirements of RSI scene classification, the adversarial example problem poses a serious problem for the classification results derived from systems using CNN models, which has not been fully recognized by previous research. In this study, to explore the properties of adversarial examples of RSI scene classification, we create different scenarios by testing two major attack algorithms (i.e., the fast gradient sign method (FGSM) and basic iterative method (BIM)) trained on different RSI benchmark datasets to fool CNNs (i.e., InceptionV1, ResNet and a simple CNN). In the experiment, our results show that CNNs of RSI scene classification are also vulnerable to adversarial examples, and some of them have a fooling rate of over 80%. These adversarial examples are affected by the architecture of CNNs and the type of RSI dataset. InceptionV1 has a fooling rate of less than 5%, which is lower than the others. Adversarial examples generated on the UCM dataset are easier than other datasets. Importantly, we also find that the classes of adversarial examples have an attack selectivity property. Misclassifications of adversarial examples of RSIs are related to the similarity of the original classes in the CNN feature space. Attack selectivity reveals potential classes of adversarial examples and provides insights into the design of defensive algorithms in future research.
A New Framework for Automatic Modulation Classification using Deep Belief Networks

Automatic Modulation Classification (AMC) is the process of determining the modulation scheme of an intercepted signal with no a priori information about its characteristics. AMC's main advantage is that no communication overhead needs to be allocated for control information to inform the receiver about changes in a transmitted signal's modulation scheme. Proposed approaches for AMC traditionally suffer from inherent computational complexity that prevents real-time AMC applications. Several extensions for lowering the computational complexity have been developed, including Feature-based AMC with machine learning classifiers. For the contribution of this research, we propose a new classifier called Deep Belief Network (DBN) for AMC applications, which is an algorithm derived from the Restricted Boltzmann Machine (RBM). DBN is capable of learning the probability distribution over its set of inputs and is comprised of layers of RBM stacked together. Additionally, this composition of layers in DBN leads to a faster learning procedure compared to conventional algorithms, and can help this classifier operate in real-time. Essentially, High-Order Statistics-based (HoS) features are utilized in the feature extraction stage for which the authors also investigate the bias issue of the estimator. The standard RadioML dataset is utilized to assess the performance of the studied AMC platform. This research shows the comparison of high order modulation schemes results in lower-bound performance of the AMC classifier, which are classified with notable higher probability of correct classification compared to conventional deep learning classifiers especially for lower signal-to-noise (SNR) ratio scenarios.
MRP2Rec: Exploring Multiple-Step Relation Path Semantics for Knowledge Graph-Based Recommendations

Knowledge graphs (KGs) have been proven to be effective for improving the performance of recommender systems. KGs can store rich side information and relieve the data sparsity problem. There are many linked attributes between entity pairs (e.g., items and users) in KGs, which can be called multiple-step relation paths. Existing methods do not sufficiently exploit the information encoded in KGs. In this paper, we propose MRP2Rec to explore various semantic relations in multiple-step relation paths to improve recommendation performance. The knowledge representation learning approach is used in our method to learn and represent multiple-step relation paths, and they are further utilized to generate prediction lists by inner products in top- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> recommendations. Experiments on two real-world datasets demonstrate that our model achieves higher performance compared with many state-of-the-art baselines.
Interpretable policy derivation for reinforcement learning based on evolutionary feature synthesis

Abstract Reinforcement learning based on the deep neural network has attracted much attention and has been widely used in real-world applications. However, the black-box property limits its usage from applying in high-stake areas, such as manufacture and healthcare. To deal with this problem, some researchers resort to the interpretable control policy generation algorithm. The basic idea is to use an interpretable model, such as tree-based genetic programming, to extract policy from other black box modes, such as neural networks. Following this idea, in this paper, we try yet another form of the genetic programming technique, evolutionary feature synthesis, to extract control policy from the neural network. We also propose an evolutionary method to optimize the operator set of the control policy for each specific problem automatically. Moreover, a policy simplification strategy is also introduced. We conduct experiments on four reinforcement learning environments. The experiment results reveal that evolutionary feature synthesis can achieve better performance than tree-based genetic programming to extract policy from the neural network with comparable interpretability.
Is apparent fixational drift in eye-tracking data due to filters or eyeball rotation?

Abstract Eye trackers are sometimes used to study the miniature eye movements such as drift that occur while observers fixate a static location on a screen. Specifically, analysis of such eye-tracking data can be performed by examining the temporal spectrum composition of the recorded gaze position signal, allowing to assess its color. However, not only rotations of the eyeball but also filters in the eye tracker may affect the signal’s spectral color. Here, we therefore ask whether colored, as opposed to white, signal dynamics in eye-tracking recordings reflect fixational eye movements, or whether they are instead largely due to filters. We recorded gaze position data with five eye trackers from four pairs of human eyes performing fixation sequences, and also from artificial eyes. We examined the spectral color of the gaze position signals produced by the eye trackers, both with their filters switched on, and for unfiltered data. We found that while filtered data recorded from both human and artificial eyes were colored for all eye trackers, for most eye trackers the signal was white when examining both unfiltered human and unfiltered artificial eye data. These results suggest that color in the eye-movement recordings was due to filters for all eye trackers except the most precise eye tracker where it may partly reflect fixational eye movements. As such, researchers studying fixational eye movements should be careful to examine the properties of the filters in their eye tracker to ensure they are studying eyeball rotation and not filter properties.
A General Matrix Function Dimensionality Reduction Framework and Extension for Manifold Learning

Many dimensionality reduction methods in the manifold learning field have the so-called small-sample-size (SSS) problem. Starting from solving the SSS problem, we first summarize the existing dimensionality reduction methods and construct a unified criterion function of these methods. Then, combining the unified criterion with the matrix function, we propose a general matrix function dimensionality reduction framework. This framework is configurable, that is, one can select suitable functions to construct such a matrix transformation framework, and then a series of new dimensionality reduction methods can be derived from this framework. In this article, we discuss how to choose suitable functions from two aspects: 1) solving the SSS problem and 2) improving pattern classification ability. As an extension, with the inverse hyperbolic tangent function and linear function, we propose a new matrix function dimensionality reduction framework. Compared with the existing methods to solve the SSS problem, these new methods can obtain better pattern classification ability and have less computational complexity. The experimental results on handwritten digit, letters databases, and two face databases show the superiority of the new methods.
A Model Management Platform for Industry 4.0 – Enabling Management of Machine Learning Models in Manufacturing Environments

Industry 4.0 use cases such as predictive maintenance and product quality control make it necessary to create, use and maintain a multitude of different machine learning models. In this setting, model management systems help to organize models. However, concepts for model management systems currently focus on data scientists, but do not support non-expert users such as domain experts and business analysts. Thus, it is difficult for them to reuse existing models for their use cases. In this paper, we address these challenges and present an architecture, a metadata schema and a corresponding model management platform.
Applicability of WSN and Biometric Models in the Field of Healthcare

Health is considered as the most important ingredient in human life. Health is wealth is the most frequent used proverb. A healthy person can perform its entire task with full enthusiasm and great energy and can solve all problems as mind is a powerful weapon, which controls all our functioning. But now due to change in our lifestyles, we are becoming prone to all kinds of health hazards. Due to unhealthy mind, we are not able to perform any tasks. Humans are becoming victims of many diseases and one of the most common reason for our degradation in health is stress. In this chapter, the authors present role of WSN and biometric models such as two factor remote authentication, verifying fingerprint operations for enhancing security, privacy preserving in healthcare, healthcare data by cloud technology with biometric application, and validation built hybrid trust computing perspective for confirmation of contributor profiles in online healthcare data. A comparison table is formulated listing all the advantages and disadvantages of various biometric-based models used in healthcare.
WITHDRAWN: Towards efficient and reliable federated learning using Blockchain for autonomous vehicles

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Representational Capabilities and Learning of Bithreshold Neural Networks

The paper deals with questions related to the ability of real-weighted bithreshold neurons and neural networks to solve the classification tasks. We study how many partitions of a finite set in n-dimensional vector space can be computed by using bithreshold neurons. The upper and lower bounds on the number of bithreshold dichotomies are proved. Our main theoretical result states that there exists \(\mathrm {\Theta }\left( m^{n+1}\right) \) bithreshold dichotomies of the set of m points in general position in n-dimensional space. We also estimate the Vapnik-Chervonenkis dimension of the class of bithreshold neurons. The problem of the synthesis of 2-layer bithreshold neural network for binary classification is considered. We demonstrate that an arbitrary dichotomy of m points in general position in n-dimensional space can be realized by 2-layer neural network containing at most \(\left\lceil m/(2n)\right\rceil \) bithreshold neurons in hidden layer and a single output threshold neuron. The learning algorithm with computational complexity \(O\left( m{{n}^{2}}+{{m}^{2}} \right) \) is proposed for such networks. We give simulation results of the network performance on real data sets and discuss the influence of algorithm parameters on the quality of learning and the representational capabilities of bithreshold networks.
A folded model for compositional data analysis

Summary A folded type model is developed for analysing compositional data. The proposed model involves an extension of the α ‐transformation for compositional data and provides a new and flexible class of distributions for modelling data defined on the simplex sample space. Despite its rather seemingly complex structure, employment of the EM algorithm guarantees efficient parameter estimation. The model is validated through simulation studies and examples which illustrate that the proposed model performs better in terms of capturing the data structure, when compared to the popular logistic normal distribution, and can be advantageous over a similar model without folding.
New Approach for Fall Detection System Using Embedded Technology

Elderly Fall accidents are one of the main causes of deaths due to fractures caused by collision with the ground or hard obstacles. This research paper aims to develop an intelligent fall detection system to detect the user fall and alarm the caretaker for a fast response. The early response of the caretaker to the alert will keep a high chance to save the life of the victim. In this paper, wearable low-cost fall detection and alarming system for the elderly has been proposed. a novel approach has been used to reduce the false-positive fall alarm. The user motion tracked using a sophisticated orientation detection module to detect user orientation in different output forms. In case of any abnormal orientation data values of the user, the system will send an alarm message via Wi-Fi and GSM to the caretaker. The system design considers a simple and low-cost design with acceptable power consumption rate. The system consists of a Wi-Fi-based microcontroller, and two orientation detection modules, and a GSM module. The system is designed for the user to wear in the waistline by installing it with the waist belt. The system used a rechargeable pin-type lithium-ion battery as a power source.
A Blockchain-based Conditional Privacy-Preserving Traffic Data Sharing in Cloud

As a typical application scenarios of Internet of Things (IoT), Internet of Vehicles (IoV) is playing an important role in the field of intelligent transportation. In such a system, vehicles are allowed to collect ambient sensing data and communicate with other vehicles, roadside infrastructure, cloud server, even personal mobile smart devices in order to exchange the relevant traffic information that usually includes the owners' sensitive information, such as geographical location or personally identifiable information. However, in open network environment, different wireless communication technologies make all these information vulnerable to various attacks and compromises. The users' private information is put at the risk of leaking at any time. To address this issue, a blockchain-based privacy-preserving traffic data sharing protocol with cloud storage is proposed based on a novel heterogeneous signcryption scheme between Public Key Infrastructure (PKI) and Certificateless Cryptosystem (CLC). Through theoretical and simulation analysis in terms of security, complexity and time consumption, the proposed traffic data sharing protocol is proved to meet the security requirements of data tamper-proofing, anonymity and traceability with higher efficiency.
Open cross-domain visual search

This paper addresses cross-domain visual search, where visual queries retrieve category samples from a different domain. For example, we may want to sketch an airplane and retrieve photographs of airplanes. Despite considerable progress, the search occurs in a closed setting between two pre-defined domains. In this paper, we make the step towards an open setting where multiple visual domains are available. This notably translates into a search between any pair of domains, from a combination of domains or within multiple domains. We introduce a simple – yet effective – approach. We formulate the search as a mapping from every visual domain to a common semantic space, where categories are represented by hyperspherical prototypes. Open cross-domain visual search is then performed by searching in the common semantic space, regardless of which domains are used as source or target. Domains are combined in the common space to search from or within multiple domains simultaneously. A separate training of every domain-specific mapping function enables an efficient scaling to any number of domains without affecting the search performance. We empirically illustrate our capability to perform open cross-domain visual search in three different scenarios. Our approach is competitive with respect to existing closed settings, where we obtain state-of-the-art results on several benchmarks for three sketch-based search tasks.
Towards behaviour based testing to understand the black box of autonomous cars

Abstract Background Autonomous cars could make traffic safer, more convenient, efficient and sustainable. They promise the convenience of a personal taxi, without the need for a human driver. Artificial intelligence would operate the vehicle instead. Especially deep neural networks (DNNs) offer a way towards this vision due to their exceptional performance particularly in perception. DNNs excel in identifying objects in sensor data which is essential for autonomous driving. These networks build their decision logic through training instead of explicit programming. A drawback of this technology is that the source code cannot be reviewed to assess the safety of a system. This leads to a situation where currently used methods for regulatory approval do not work to validate a promising new piece of technology. Objective In this paper four approaches are highlighted that might help understanding black box technical systems for autonomous cars by focusing on its behaviour instead. The method of experimental psychology is proposed to model the inner workings of DNNs by observing its behaviour in specific situations. It is argued that penetration testing can be applied to identify weaknesses of the system. Both can be applied to improve autonomous driving systems. The shadowing method reveals behaviour in a naturalistic setting while ensuring safety. It can be seen as a theoretical driving exam. The supervised driving method can be utilised to decide if the technology is safe enough. It has potential to be developed into a practical driving exam.
A Lightweight Attribute Based Encryption Scheme with Constant Size Ciphertext for Internet of Things

The Internet of Things technology has been used in a wide range of fields, ranging from industrial applications to individual lives. As a result, a massive amount of sensitive data is generated and transmitted by IoT devices. Those data may be accessed by a large number of complex users. Therefore, it is necessary to adopt an encryption scheme with access control to achieve more flexible and secure access to sensitive data. The Ciphertext Policy Attribute-Based Encryption (CP-ABE) can achieve access control while encrypting data can match the requirements mentioned above. However, the long ciphertext and the slow decryption operation makes it difficult to be used in most IoT devices which have limited memory size and computing capability. This paper proposes a modified CP-ABE scheme, which can implement the full security (adaptive security) under the access structure of AND gate. Moreover, the decryption overhead and the length of ciphertext are constant. Finally, the analysis and experiments prove the feasibility of our scheme.