Reviewerly/exHarmony huggingface dataset:
Prediction‐discrepancy based on innovative particle filter for estimating UAV true position in the presence of the GPS spoofing attacks

In this paper, a novel prediction-discrepancy based on innovative particle filter (PDIPF) is proposed to solve the unmanned aerial vehicle (UAV) positioning problem in the presence of the global positioning system (GPS) spoofing attack, supposing that the GPS spoofing effects are in the form of unknown but bounded errors. To cope with the GPS spoofing attacks as unknown sudden changes of system state variables, the compensation of the GPS spoofing effects is adaptively done in two basic parts of PDIPF algorithm including particle weighting and covariance matrix adaption. In addition, a theorem is developed which verifies that the output estimation error is upper bounded by a given probability with the help of the adapted covariance matrix. Besides, the particle weight calculation in PDIPF is done with respect to the prediction discrepancy of generated particles from the GPS measurements. The proposed PDIPF is used to decrease the effects of any GPS spoofing errors with different probability density functions and estimate true position of UAV in the presence of the GPS spoofing attacks. The algorithm is applied to the inertial navigation system/GPS/Loran-C integration systems. Simulation results demonstrate the effectiveness of the proposed PDIPF algorithm in terms of accuracy and redundancy.
Continuous Control with Deep Reinforcement Learning for Mobile Robot Navigation

Autonomous navigation is one of the focuses in the field of mobile robot research. The traditional method usually consists of two parts: building the map of environment, localization of mobile robot and path planning. However, these traditional methods usually rely on high-precision sensor information. At the same time, mobile robots have no intelligent understanding of autonomous navigation. In this article, a deep reinforcement learning method, i.e. soft actor critic, is used to navigate in a mapless environment. It takes laser scanning data and information of the target as input, outputs linear velocity and angular velocity in continuous space. The simulation shows that this learning-based end-to-end autonomous navigation method can accomplish tasks as well as traditional methods.
Performance of the site-adapted CAMS database and locally adjusted cloud index models for estimating global solar horizontal irradiation over the Pampa Húmeda

CAMS provides global solar radiation estimates for clear-sky (McClear model) and all-sky (Heliosat-4 method) conditions, the latter based on MSG satellite information. A performance assessment of these estimates (with site-adaptation and spatial smoothing) is done, using hourly data from 10 sites in the Pampa Húmeda region of South America. Two locally adjusted Cloud Index Models (CIM) using GOES-East satellite information are also evaluated. One of them (CIM-ESRA) is based on the ESRA clear-sky model and the other (CIM-McClear) on the McClear clear-sky model. Under clear-sky conditions, the site-adapted McClear is found to perform best with a relative root mean square deviation (rRMSD) of 2.8%. However, in the presence of clouds in the real atmosphere, the model tends to provide lower clear-sky estimates than the ESRA model which, in our implementation, is only sensitive to average atmospheric trends. Under all-sky conditions, both CIMs show a small but consistent underestimation of -1.1% in the region and perform significantly better than the site-adapted Heliosat-4, with rRMSDs of 12.1% (CIM-McClear), 12.5% (CIM-ESRA) and 16.8% (site-adapted Heliosat-4). This performance difference is not a statement about the relative quality of the models, since it can be explained by the difference in satellite view angle (significantly higher for the MSG satellite than for the GOES-East satellite). The performance downgrade due to using MSG satellite images out of their recommended area is quantified. Both CIMs, based on using GOES-East imagery, provide accurate solar irradiation estimates over this region and can be extended to other areas of Latin America.
Opinion Spam Detection Based on Heterogeneous Information Network

User-generated online reviews can play a significant role in the success of retail products, hotels, restaurants, etc. However, opinion spam has become a widespread problem because often paid spam reviewers write fake reviews to unjustly promote or demote certain products or businesses. Existing approaches often utilize part of the clues within user-review-product Heterogeneous Information Network to detect spam or spammers, which cannot get satisfied performance. In this work, we use novel features like posted photos in reviews and user social networks, and propose a new approach called SkyNet that utilizes clues from all heterogeneous data including metadata (text, photos within reviews, and etc.) as well as relational data, and harness them collectively under a unified framework to spot suspicious users and reviews. The experiments showed that SkyNet significantly outperforms several baselines and state-of-the-art methods on real-world review dataset.
A Deep Neural Network Approach for Top View People Detection and Counting

People detection and counting is considered as one of the important application in video surveillance. Various computer vision and deep learning based methods have been developed which aim to provide efficient and accurate people detection/counting results using frontal view data sets. Furthermore, there are many challenges which occurs while detecting people including occlusion, perspective distortion, variations in human body pose, size and orientation, these challenges effect the results of developed detection and counting models. In this work, a deep neural network approach i.e. SSD (Single Shot multi-box Detector) is explored for people detection and counting. SSD model is used for detection and counting of people from significantly different viewpoint i.e. top view. To the extent of our knowledge, this is the first attempt to use deep neural network based model for top view people detection and counting. Furthermore, the impact of frontal view trained SSD model on top view test images is also discussed. The experimental results show the effectiveness of deep learning model by achieving promising results with average TPR of 95% and TPR 94.42% for indoor and outdoor environments respectively.
Data Augmentation using Generative Adversarial Networks for Pneumonia classification in chest Xrays

In medical images, data augmentation is essentially important for accurate classification of images especially when available data is limited. This paper proposes a noble data augmentation method of generating synthetic chest Xray images of patients with Pneumonia using Generative Adversarial Networks (GANs). The proposed model first uses conventional data augmentation techniques along with GANs to generate more training samples. A specific implementation of GANs allows us to produce unprecedented Chest X-Ray images of patients suffering from Pneumonia. The generated samples are then used to train a DCNN model to classify chest X-Ray images. The classifier significantly improves its accuracy after the introduction of synthetic data produced by the GAN model.
Immersive Process Model Exploration in Virtual Reality

In many professional domains, relevant processes are documented as abstract process models, such as event-driven process chains (EPCs). EPCs are traditionally visualized as 2D graphs and their size varies with the complexity of the process. While process modeling experts are used to interpreting complex 2D EPCs, in certain scenarios such as, for example, professional training or education, also novice users inexperienced in interpreting 2D EPC data are facing the challenge of learning and understanding complex process models. To communicate process knowledge in an effective yet motivating and interesting way, we propose a novel virtual reality (VR) interface for non-expert users. Our proposed system turns the exploration of arbitrarily complex EPCs into an interactive and multi-sensory VR experience. It automatically generates a virtual 3D environment from a process model and lets users explore processes through a combination of natural walking and teleportation. Our immersive interface leverages basic gamification in the form of a logical walkthrough mode to motivate users to interact with the virtual process. The generated user experience is entirely novel in the field of immersive data exploration and supported by a combination of visual, auditory, vibrotactile and passive haptic feedback. In a user study with $\mathrm{N}=27$ novice users, we evaluate the effect of our proposed system on process model understandability and user experience, while comparing it to a traditional 2D interface on a tablet device. The results indicate a tradeoff between efficiency and user interest as assessed by the UEQ novelty subscale, while no significant decrease in model understanding performance was found using the proposed VR interface. Our investigation highlights the potential of multi-sensory VR for less time-critical professional application domains, such as employee training, communication, education, and related scenarios focusing on user interest.
HiRec: API Recommendation using Hierarchical Context

Context-aware API recommendation techniques aim to generate a ranked list of candidate APIs on an editing position during development. The basic context used in traditional API recommendation mainly focuses on the APIs from third-party libraries, limit or even ignore the usage of project-specific code. The limited usage of project-specific code may result in the lack of context information, and degrade the effectiveness of API recommendation. To address this problem, we introduce a novel type of context, i.e., hierarchical context, which can leverage the hidden information of project-specific code by analyzing the call graph. In hierarchical context, a project-specific API is presented as a sequence of low-leveled APIs from third-party libraries. We propose an approach, i.e., HiRec, which builds on the basis of hierarchical context. HiRec is evaluated on 108 projects and the results show that HiRec can obtain much more accurate results than all the other selected approaches in terms of top-5 and top-10 accuracy due to the strong ability of context representation. And HiRec performs closely to the outstanding tools in terms of top-1 accuracy. The average time of recommending execution is less than 1 seconds in most cases, which is acceptable for interaction in an IDE. Unlike current approaches, the effectiveness of HiRec is not impacted much by editing positions. And we can obtain more accurate results from HiRec with larger sizes of training data and hierarchical context.
Development of a Model ofProject-Based Learning

Project-based learning as a principle of the educational process that is being implemented at the universities has a long history. The model of Aalborg University (Denmark) which was created in 1974 to change the traditional approach to teaching students in higher educational institutions of the country is the basic (archetypal) model of project-based learning. The School of Economics and Management of the Far Eastern Federal University is currently implementing its own model of project-based learning (SEM model). This model is developed on the basis of design thinking, which is a methodology for solving ill-structured problems in the face of uncertainty. The implementation of design thinking in the educational process should promote the development of creativity and critical thinking competencies among students, and the organization of project work should contribute to the development of communication skills necessary for interaction with internal and external stakeholders as well as teamwork skills. The purpose of the article is to compare this model with the base model of Aalborg university, to identify the general and the special features of both of these models, to outline prospects for the further development of the SEM model. Thesis: the experience gained at the SEM over the three years of the learning implementation provides an opportunity to create its own systemic model of project-based learning and to recommend its implementation in the other Russian universities.
Robust support vector data description for novelty detection with contaminated data

Support vector data description (SVDD) is a widely used novelty detection algorithm. It provides excellent predictions even in the absence of negative samples and retains the mathematical elegance of Support Vector Machines. The decision boundary can be very flexible due to the incorporation of kernel functions. However, SVDD can suffer a lot from contaminated data containing, for example, outliers or mislabeled observations. Although several weighting schemes have been proposed to find a more reliable description of the target data, the calculation of the weight are themselves affected by the outliers and does not provide much insight in the data. The masked outliers fail to receive lower weight values. The Stahel–Donoho (SD) outlyingness from multivariate statistics is a very robust measure to expose the outliers. To avoid the masking effect, we propose to assign weight to each observation based on the SD outlyingness in an arbitrary kernel space. A robust SVDD is defined down-weighting the samples with large outlyingness. The experimental results demonstrate superiority of the proposed method in terms of AUC for contaminated data.
Adversarial Reinforcement Learning Framework for Benchmarking Collision Avoidance Mechanisms in Autonomous Vehicles

With the rapidly growing interest in autonomous navigation, the body of research on motion planning and collision avoidance techniques has enjoyed an accelerating rate of novel proposals and developments. However, the complexity of new techniques and their safety requirements render the bulk of current benchmarking frameworks inadequate, thus leaving the need for efficient comparison techniques unanswered. This work proposes a novel framework based on deep reinforcement learning for benchmarking the behavior of collision avoidance mechanisms under the worst-case scenario of dealing with an optimal adversarial agent, trained to drive the system into unsafe states. We describe the architecture and flow of this framework as a benchmarking solution, and demonstrate its efficacy via a practical case study of comparing the reliability of two collision avoidance mechanisms in response to adversarial attempts to cause collisions.
Provably secure two-party authenticated key agreement protocol for post-quantum environments

A two-party authenticated key agreement (2PAKA) protocol is a cryptographic tool employed widely to allow two users to generate a shared and fresh session key between them in each session over an insecure network. The authenticated version of a two-party key agreement protocol is popular because it can easily withstand the impersonation of the user. In the literature, many 2PAKA protocols have been put forward with the intractability assumptions of the discrete logarithm (DLP) problem and integer factorization problem (IFP). Some recent studies showed that the 2PAKA protocols based on these assumptions are insecure in post-quantum environments. To resolve this issue, we have designed a lattice-based 2PAKA (LB-2PAKA) protocol with the intractability of the ring-learning-with-errors (RLWE) problem. The proposed LB-2PAKA protocol is also analyzed in the random oracle model to measure provable security and to estimate the breaching time. To evaluate the performance, we used the LatticeCrypto Library and estimated the running time of our LB-2PAKA protocol. Besides, we analyzed the communication cost requirement of our LB-2PAKA protocol.
Knowledge transfer by domain-independent user latent factor for cross-domain recommender systems

Collaborative filtering is one of the most promising used techniques in recommender systems to provide personalized recommendations. However, it suffers from the data sparsity problem due to insufficient user preference data. The promising solution to this problem is knowledge exploitation from other related source domains through transfer learning mechanism. Although several works have been proposed to enhance prediction performance of the target domain by leveraging the knowledge of source domains, extracting domain-independent knowledge from source domains and preventing the negative knowledge both are challenging and open problems for cross-domain recommender systems framework. In this paper, we propose a method namely Knowledge Transfer by Domain-independent User Latent Factor for Cross-domain Recommender Systems. The propose method uses matrix tr-factorization by which a rating matrix decomposes into three sub-matrices. Advantage of tri-factorization is to be able to prevent domain effect knowledge transfer. This allows to enhance the overall performance of prediction in the target domain. We collectively factorize both domain rating matrices by matrix tri-factorization with the constraint of shared user latent factor across domains. The constraint holds to be true due to users fully overlap across domains. The loss functions of matrix tri-factorization are combined with a tuning parameter that is able to prevent negative transferring of a source domain. The combined loss function is optimized by stochastic gradient descent algorithm. The superiority of our proposed method over existing state-of-the-art methods is demonstrated by experimental results which have been done on real-world Amazon co-purchasing metadata.
Posture Recognition Using Ensemble Deep Models under Various Home Environments

This paper is concerned with posture recognition using ensemble convolutional neural networks (CNNs) in home environments. With the increasing number of elderly people living alone at home, posture recognition is very important for helping elderly people cope with sudden danger. Traditionally, to recognize posture, it was necessary to obtain the coordinates of the body points, depth, frame information of video, and so on. In conventional machine learning, there is a limitation in recognizing posture directly using only an image. However, with advancements in the latest deep learning, it is possible to achieve good performance in posture recognition using only an image. Thus, we performed experiments based on VGGNet, ResNet, DenseNet, InceptionResNet, and Xception as pre-trained CNNs using five types of preprocessing. On the basis of these deep learning methods, we finally present the ensemble deep model combined by majority and average methods. The experiments were performed by a posture database constructed at the Electronics and Telecommunications Research Institute (ETRI), Korea. This database consists of 51,000 images with 10 postures from 51 home environments. The experimental results reveal that the ensemble system by InceptionResNetV2s with five types of preprocessing shows good performance in comparison to other combination methods and the pre-trained CNN itself.
Corner detection based on shearlet transform and multi-directional structure tensor

Image corners have been widely used in various computer vision tasks. Current multi-scale analysis based corner detectors do not make full use of the multi-scale and multi-directional structural information. This degrades their detection accuracy and capability of refining corners. In this work, an improved shearlet transform with a flexible number of directions and a reasonable support is proposed to extract accurate multi-scale and multi-directional structural information from images. To make full use of the structural information from the improved shearlets, a novel multi-directional structure tensor is constructed for corner detection, and a multi-scale corner measurement function is proposed to remove false candidate corners. Experimental results demonstrate that the proposed corner detector performs better than existing corner and interest point detectors in terms of detection accuracy, localization accuracy, and robustness to affine transformations, illumination changes, noise, viewpoint changes, etc. It has a great potential for extension as a descriptor and for applications in computer vision tasks.
(I can’t get no) antisatisfaction

Substructural approaches to paradoxes have attracted much attention from the philosophical community in the last decade. In this paper we focus on two substructural logics, named $${\mathsf {ST}}$$ and $${\mathsf {TS}}$$ , along with two structural cousins, $${\mathsf {LP}}$$ and $${\mathsf {K3}}$$ . It is well known that $${\mathsf {LP}}$$ and $${\mathsf {K3}}$$ are duals in the sense that an inference is valid in one logic just in case the contrapositive is valid in the other logic. As a consequence of this duality, theories based on either logic are tightly connected since many of the arguments for and objections against one theory reappear in the other theory in dual form. The target of the paper is making explicit in exactly what way, if any, $${\mathsf {ST}}$$ and $${\mathsf {TS}}$$ are dual to one another. The connection will allow us to gain a more fine-grained understanding of these logics and of the theories based on them. In particular, we will obtain new insights on two questions concerning $${\mathsf {ST}}$$ which are being intensively discussed in the current literature: whether $${\mathsf {ST}}$$ preserves classical logic and whether it is $${\mathsf {LP}}$$ in sheep’s clothing. Explaining in what way $${\mathsf {ST}}$$ and $${\mathsf {TS}}$$ are duals requires comparing these logics at a metainferential level. We provide to this end a uniform proof theory to decide on valid metainferences for each of the four logics. This proof procedure allows us to show in a very simple way how different properties of inferences (unsatisfiability, supersatisfiability and antivalidity) that behave in very different ways for each logic can be captured in terms of the validity of a metainference.
Abnormal energy consumption detection for GSHP system based on ensemble deep learning and statistical modeling method

Energy consumption of heat pump system accounts for a large part of the total building energy consumption, and the energy-saving operation of heat pump system has always been the focus of researchers. A promising solution to tackling energy wastes during system operations is anomaly detection. In this study, we propose an anomaly detection method for GSHP system in a public building based on mode decomposition based LSTM and statistical modeling method Grubbs’ test. The system energy consumption is predicted using mode decomposition based LSTM algorithm, and the difference between predicted value and actual value is used to detect the abnormal system energy consumption by Grubbs’ test. Results show that detected anomalies can be summarily divided into three categories (parabola anomaly, abrupt anomaly and time related anomaly) depending on their characteristics, and the rationality of detected anomalies are evaluated through field investigation and expert knowledge. This work is enlightening and indicates that the proposed method would efficiently detect the abnormal performance of GSHP system, and find out unreasonable operating patterns.