Reviewerly/exHarmony huggingface dataset:
3D Path Planning Method for UAV Based on Improved Artificial Potential Field

Path planning is the key technology for UAV to achieve autonomous flight. Considering the shortcomings of path planning based on the conventional potential field method, this paper proposes an improved optimization algorithm based on the artificial potential field method and extends it to three-dimensional space to better achieve flight constrained 3D online path planning for UAVs. The algorithm is improved and optimized aiming at the three problems of goal nonreachable with obstacle nearby (GNWON), easy to fall into local minimum, and path oscillation in traditional artificial potential field method. First, an improved potential field function with relative distance is used to solve the GNWON, and an optimized repulsive potential field calculation method based on different obstacles or threat models is proposed to optimize the planned path. Secondly, in order to make the drone jump out of the local minimum trap, a method of setting heuristic sub-target points is proposed. For local path oscillation, a method using memory sum force was proposed to improve the oscillation. The simulation results show that the improved optimization algorithm in this paper effectively makes up for the shortcomings of the traditional artificial potential field method, and the designed 3D online path planning algorithm for the UAV is practical and feasible.
GuessUNeed

Massive Open Online Courses, offering millions of high-quality courses from prestigious universities and prominent experts, are picking up momentum in popularity. Although users enrolling on MOOCs have free access to abundant knowledge, they may easily get overwhelmed by information overload. Therefore, there is a need of recommending technology as a fundamental and well-accepted effective solution. However, differing from many other online recommendations, recommending courses to users on MOOCs faces two challenges. First, users’ knowledge background differs, so does their purpose of learning. Second, online courses are not independent but intertwined with prerequisite relations. Therefore, it is necessary to take these two challenges into account when designing a recommending method. To tackle this issue, in this article, we first propose two algorithms for extracting concept-level and course-level prerequisite relations. We then present the recommending method GuessUNeed based on neural attention network and course prerequisite relation embeddings. The experimental results on real-world datasets demonstrate the superiority of the proposed GuessUNeed method.
Graphonomy: Universal Image Parsing via Graph Reasoning and Transfer

Prior highly-tuned image parsing models are usually studied in a certain domain with a specific set of semantic labels and can hardly be adapted into other scenarios (e.g.sharing discrepant label granularity) without extensive re-training. Learning a single universal parsing model by unifying label annotations from different domains or at various levels of granularity is a crucial but rarely addressed topic. This poses many fundamental learning challenges, e.g.discovering underlying semantic structures among different label granularity or mining label correlation across relevant tasks. To address these challenges, we propose a graph reasoning and transfer learning framework, named "Graphonomy," which incorporates human knowledge and label taxonomy into the intermediate graph representation learning beyond local convolutions. In particular, Graphonomy learns the global and structured semantic coherency in multiple domains via semantic-aware graph reasoning and transfer, enforcing the mutual benefits of the parsing across domains (e.g.different datasets or co-related tasks). The Graphonomy includes two iterated modules: Intra-Graph Reasoning and Inter-Graph Transfer modules. The former extracts the semantic graph in each domain to improve the feature representation learning by propagating information with the graph; the latter exploits the dependencies among the graphs from different domains for bidirectional knowledge transfer. We apply Graphonomy to two relevant but different image understanding research topics: human parsing and panoptic segmentation, and show Graphonomy can handle both of them well via a standard pipeline against current state-of-the-art approaches. Moreover, some extra benefit of our framework is demonstrated, e.g., generating the human parsing at various levels of granularity by unifying annotations across different datasets.
NSCR-Based DenseNet for Lung Tumor Recognition Using Chest CT Image

Nonnegative sparse representation has become a popular methodology in medical analysis and diagnosis in recent years. In order to resolve network degradation, higher dimensionality in feature extraction, data redundancy, and other issues faced when medical images parameters are trained using convolutional neural networks. Lung tumors in chest CT image based on nonnegative, sparse, and collaborative representation classification of DenseNet (DenseNet-NSCR) are proposed by this paper: firstly, initialization parameters of pretrained DenseNet model using transfer learning; secondly, training DenseNet using CT images to extract feature vectors for the full connectivity layer; thirdly, a nonnegative, sparse, and collaborative representation (NSCR) is used to represent the feature vector and solve the coding coefficient matrix; fourthly, the residual similarity is used for classification. The experimental results show that the DenseNet-NSCR classification is better than the other models, and the various evaluation indexes such as specificity and sensitivity are also high, and the method has better robustness and generalization ability through comparison experiment using AlexNet, GoogleNet, and DenseNet-201 models.
Spherical Formulation of Geometric Motion Segmentation Constraints in Fisheye Cameras

We introduce a visual motion segmentation method employing spherical geometry for fisheye cameras and automoated driving. Three commonly used geometric constraints in pin-hole imagery (the positive height, positive depth and epipolar constraints) are reformulated to spherical coordinates, making them invariant to specific camera configurations as long as the camera calibration is known. A fourth constraint, known as the anti-parallel constraint, is added to resolve motion-parallax ambiguity, to support the detection of moving objects undergoing parallel or near-parallel motion with respect to the host vehicle. A final constraint constraint is described, known as the spherical three-view constraint, is described though not employed in our proposed algorithm. Results are presented and analyzed that demonstrate that the proposal is an effective motion segmentation approach for direct employment on fisheye imagery.
Semi-supervised disentangled framework for transferable named entity recognition

Named entity recognition (NER) for identifying proper nouns in unstructured text is one of the most important and fundamental tasks in natural language processing. However, despite the widespread use of NER models, they still require a large-scale labeled data set, which incurs a heavy burden due to manual annotation. Domain adaptation is one of the most promising solutions to this problem, where rich labeled data from the relevant source domain are utilized to strengthen the generalizability of a model based on the target domain. However, the mainstream cross-domain NER models are still affected by the following two challenges (1) Extracting domain-invariant information such as syntactic information for cross-domain transfer. (2) Integrating domain-specific information such as semantic information into the model to improve the performance of NER. In this study, we present a semi-supervised framework for transferable NER, which disentangles the domain-invariant latent variables and domain-specific latent variables. In the proposed framework, the domain-specific information is integrated with the domain-specific latent variables by using a domain predictor. The domain-specific and domain-invariant latent variables are disentangled using three mutual information regularization terms, i.e., maximizing the mutual information between the domain-specific latent variables and the original embedding, maximizing the mutual information between the domain-invariant latent variables and the original embedding, and minimizing the mutual information between the domain-specific and domain-invariant latent variables. Extensive experiments demonstrated that our model can obtain state-of-the-art performance with cross-domain and cross-lingual NER benchmark data sets.
SAFE: A General Secure and Fair Auction Framework for Wireless Markets With Privacy Preservation

With the prosperity of wireless and mobile communications, the allocation of wireless resources, e.g., spectrum channels, femtocell access permissions, and resource blocks of D2D connections, has become a matter of great concern, which leads to the emergence and popularity of electronic auction. However, the lack of privacy protection and fairness guarantee in the existing market has posed great obstacles to user participation in electronic auctions. Without privacy protection, users will be worried about the unauthorized exposure of their commercial secrets, which may hurt their payoffs in the long run. Without fairness guarantee, selfish/malicious users may prematurely abort the auction to avoid unsatisfactory payment, thus causing loss to honest users and wasting system resources. Furthermore, the lack of fairness guarantee will incur an exchange dilemma, where mutually distrusting parties may refuse to initiate the goods/money exchange, hindering the market-clearing of auctions. Although extensive efforts have been made to develop privacy-preserving auction mechanisms for wireless markets, the fairness issue, to the best of our knowledge, has never been taken into account in the context of auctions. Moreover, a truly practical framework that can be applied to most mainstream auction formats is also missing in the literature. In this article, we present the first general framework, named SAFE, for representative auctions in (but not limited to) wireless markets. SAFE achieves a wide range of security goals with much higher efficiency than existing solutions. Furthermore, SAFE ensures auction fairness in a trust-free manner by developing a full set of carefully-designed modular protocols that can be easily adapted to any auction format. We implement the SAFE framework over a simulated Ethereum network. Extensive experimental results confirm that SAFE can indeed achieve high economic efficiency in terms of social welfare and participation satisfaction at very low computation and communication overheads.
Efficient Hardware Implementations for Elliptic Curve Cryptography over Curve448

In this paper, we present different implementations of point multiplication over Curve448. Curve448 has recently been recommended by NIST to provide 224-bit security over elliptic curve cryptography. Although implementing high-security cryptosystems should be considered due to recent improvements in cryptanalysis, hardware implementation of Curve488 has been investigated in a few studies. Hence, in this study, we propose three variable-base-point FPGA-based Curve448 implementations, i.e., lightweight, area-time efficient, and high-performance architectures, which aim to be used for different applications. Synthesized on a Xilinx Zynq 7020 FPGA, our proposed high-performance design increases 12% throughput with executing 1,219 point multiplication per second and increases 40% efficiency in terms of required clock cycles $$\times $$ utilized area compared to the best previous work. Furthermore, the proposed lightweight architecture works 250 MHz and saves 96% of resources with the same performance. Additionally, our area-time efficient design considers a trade-off between time and required resources, which shows a 48% efficiency improvement with 52% fewer resources. Finally, effective side-channel countermeasures are added to our proposed designs, which also outperform previous works.
Accelerated adiabatic quantum search algorithm via pulse control in a non-Markovian environment

Adiabatic quantum computation requires that the system remains in its ground state. However, an adiabatic process often loses partly or entirely its quantumness, such as entanglement in its long runtime, due to the system-environment interactions. Here we put forward an effective quantum control technique to realize the adiabatic quantum search algorithm in a nonadiabatic regime and in the presence of environment. Using the non-Markovian quantum state diffusion equation approach, we numerically study the system dynamics characterized by the success probability. The results show that the probability increases with decreasing coupling strength and temperature. In particular, non-Markovianity from the environment can help to enhance the success probability. By choosing a suitable pulse intensity and period, a high success probability can be obtained in a short runtime for weak system-bath coupling, low temperature, and strong non-Markovian heat baths.
Lifelong learning of interpretable image representations

Existing machine learning systems are trained to adapt to a single database and their ability to acquire additional information is limited. Catastrophic forgetting occurs in all deep learning systems when attempting to train them with additional databases. The information learnt previously is forgotten and no longer recognized when such a learning systems is trained using a new database. In this paper, we develop a new image generation approach defined under the lifelong learning framework which prevents forgetting. We employ the mutual information maximization between the latent variable space and the outputs of the generator network in order to learn interpretable representations, when learning using the data from a series of databases sequentially. We also provide the theoretical framework for the generative replay mechanism, under the lifelong learning setting. We perform a series of experiments showing that the proposed approach is able to learn a set of disjoint data distributions in a sequential manner while also capturing meaningful data representations across domains.
NUTA: Non-uniform Temporal Aggregation for Action Recognition

In the world of action recognition research, one primary focus has been on how to construct and train networks to model the spatial-temporal volume of an input video. These methods typically uniformly sample a segment of an input clip (along the temporal dimension). However, not all parts of a video are equally important to determine the action in the clip. In this work, we focus instead on learning where to extract features, so as to focus on the most informative parts of the video. We propose a method called the non-uniform temporal aggregation (NUTA), which aggregates features only from informative temporal segments. We also introduce a synchronization method that allows our NUTA features to be temporally aligned with traditional uniformly sampled video features, so that both local and clip-level features can be combined. Our model has achieved state-of-the-art performance on four widely used large-scale action-recognition datasets (Kinetics400, Kinetics700, Something-something V2 and Charades). In addition, we have created a visualization to illustrate how the proposed NUTA method selects only the most relevant parts of a video clip.
Probabilistic Naming of Functions in Stripped Binaries

Debugging symbols in binary executables carry the names of functions and global variables. When present, they greatly simplify the process of reverse engineering, but they are almost always removed (stripped) for deployment. We present the design and implementation of punstrip, a tool which combines a probabilistic fingerprint of binary code based on high-level features with a probabilistic graphical model to learn the relationship between function names and program structure. As there are many naming conventions and developer styles, functions from different applications do not necessarily have the exact same name, even if they implement the exact same functionality. We therefore evaluate punstrip across three levels of name matching: exact; an approach based on natural language processing of name components; and using Symbol2Vec, a new embedding of function names based on random walks of function call graphs. We show that our approach is able to recognize functions compiled across different compilers and optimization levels and then demonstrate that punstrip can predict semantically similar function names based on code structure. We evaluate our approach over open source C binaries from the Debian Linux distribution and compare against the state of the art.
Considering decoherence errors in the simulation of quantum circuits using decision diagrams

By using quantum mechanical effects, quantum computers promise significant speedups in solving problems intractable for conventional computers. However, despite recent progress they remain limited in scaling and availability---making quantum software and hardware development heavily reliant on quantum simulators running on conventional hardware. However, most of those simulators mimic perfect quantum computers and, hence, ignore the fragile nature of quantum mechanical effects which frequently yield to decoherence errors in real quantum devices. Considering those errors during the simulation is complex, but necessary in order to tailor quantum algorithms for specific devices. Thus far, most state-of-the-art simulators considering decoherence errors rely on (exponentially) large array representations. As an alternative, simulators based on decision diagrams have been shown very promising for simulation of quantum circuits in general, but have not supported decoherence errors yet. In this work, we are closing this gap. We investigate how the consideration of decoherence errors affects the simulation performance of approaches based on decision diagrams and propose advanced solutions to mitigate negative effects. Experiments confirm that this yields improvements of several orders of magnitudes compared to a naive consideration of errors.
CNN-Based Classification of Degraded Images With Awareness of Degradation Levels

Image classification needs to consider the existence of image degradations in practice. Although degraded images have various levels of degradation, the degradation levels are usually unknown. This paper proposes a convolutional neural network to classify degraded images by using a restoration network and an ensemble learning. The proposed network can automatically infer ensemble weights by using estimated degradation levels of degraded images and features of restored images, where the degradation levels are estimated internally. The proposed network is mainly discussed with JPEG distortion, while degradations of both Gaussian noise and blurring are also examined. We demonstrate that the proposed network can classify degraded images over various levels of degradation. This paper also reveals how the image-quality of training data for a classification network affects the classification performance of degraded images.
Robust Feature Matching for Remote Sensing Image Registration via Guided Hyperplane Fitting

Feature matching is a fundamental problem in feature-based remote sensing image registration. Due to the ground relief variations and imaging viewpoint changes, remote sensing images often involve local distortions, leading to difficulties in high-accuracy image registration. To address this issue, in this article, we propose a robust feature matching method called First Neighbor Relation Guided (FNRG) for remote sensing image registration via guided hyperplane fitting. The key idea of FNRG is to exploit the first neighbor relation of feature points between two images for seeking consistent seeds in a parameter-free manner. To boost more consistent matches based on the consistent seeds, we formulate the feature matching problem into an affine hyperplane fitting problem by imposing the motion consistency, and then we design a hyperplane updating strategy to refine the fitting model. We also introduce a locality preserving structure-based cost function to promote the matching performance of the hyperplane updating strategy. Our method can mine consistent matches from thousands of putative ones within only a few milliseconds, and it also can handle the data with a large-scale change, rotation, or severe nonrigid deformation. Extensive experiments on the remote sensing image data sets with different types of image transformations show that the proposed method achieves significant superiority over several state-of-the-art methods.
Observability-Constrained VINS for MAVs Using Interacting Multiple Model Algorithm

This article presents the design of an interacting multiple model (IMM) filter for visual-inertial navigation (VIN) of MAVs. VIN of MAVs in practice typically uses a single system model for its state estimator design. However, microaerial vehicles (MAVs) can operate in different scenarios such as aggressive flights, hovering flights, and under high external disturbance requiring changing constraints imposed on the estimator model. This article proposes the use of a conventional VIN and a drag force VIN in an error-state IMM filtering framework to address the need for multiple models in the estimator. The work uses an epipolar geometry constraint for the design of the measurement model for both filters to realize computationally efficient state updates. Observability of the proposed modifications to VIN filters (drag force model and epipolar measurement model) are analyzed, and observability-based consistency rules are derived for the two filters of the IMM. Monte Carlo numerical simulations validate the performance of the observability constrained IMM, which improved the accuracy and consistency of the visual-inertial navigation system (VINS) for changing flight conditions and external wind disturbance scenarios. Experimental validation is performed using the EuRoC dataset to evaluate the performance of the proposed IMM filter design. The results show that the IMM outperforms stand-alone filters used in the IMM filtering bank by switching between the filters based on the residual likelihood of the models.
PASTRAMI

Decentralised cloud computing platforms enable individuals to offer and rent resources in a peer-to-peer fashion. They must assign resources from multiple sellers to multiple buyers and derive prices that match the interests and capacities of both parties. The assignment process must be decentralised, fair and transparent, but also protect the privacy of buyers.