Reviewerly/exHarmony huggingface dataset:
REBAL: Channel Balancing for Payment Channel Networks

Cryptocurrency networks are a promising infrastructure for pseudonymous online payments. However, low throughput has prevented their widespread acceptance. A promising solution to scale throughput is the Payment channel network (PCN), exemplified by the Lightning Network (LN), that uses a network of off-chain bidirectional payment channels between parties that wish to transact often. Since payments use the shortest paths with sufficient funds over this network, channel balances get exhausted in the direction transactions flow and eventually become unidirectional. This results in transactions failing and consequently a lower transaction success ratio. Our observations on the production LN show that over 63% of the channels lose over 80% of the channel balance in one direction over time, which makes the success ratio of a real-world workload drop from 71% to 29%. A unidirectional channel along a path results in a failure message back to the source that recomputes the path, excluding the failed channel and reattempts the transaction, thus adding to the completion latency even for those transactions that do complete.We propose REBAL, a distributed re-balancing mechanism, and a new routing scheme to address the above issues. REBAL maximizes the extent to which channels can be re-balanced across the entire network. REBAL addresses the completion latency issue by re-routing transactions from intermediate nodes around a unidirectional channel rather than propagating the failure back to the source.Our comprehensive evaluation of REBAL shows that the success ratio improves from 30.18% to 79.54% and success volume from 3.98% to 29.99% for a real-world workload derived from the Ripple network, without adversely impacting the transaction latency. Even at very high transaction rates, REBAL outperforms Lightning Network Daemon (LND- a Golang implementation of LN) (12%) with a success ratio of 43.76%.
Genetic testing and insurance implications: Surveying the US general population about discrimination concerns and knowledge of the Genetic Information Nondiscrimination Act (GINA)

Globally, due to public concerns of genetic discrimination, some countries and insurance industries have adopted policies restricting insurer use of genetic information, such as the US Genetic Information Nondiscrimination Act (GINA). This study reports on combined analysis of two surveys assessing public knowledge of GINA and concerns of genetic discrimination in a diverse U.S. sample (N=1616). We focus on whether occupation, genetic testing history, and insurance status are correlated with knowledge of GINA or concerns of discrimination. While bivariate analysis identified some populations with higher subjective/objective knowledge and concern relative to counterparts, multivariable regression identified very few significant associations with outcomes of interest. Overall, this study highlights lack of awareness and understanding of GINA, even among subpopulations hypothesized to have greater knowledge of the law. These findings have implications for the broader debate around insurer use of genetic information.
Stochastic Multiple Chaotic Local Search-Incorporated Gradient-Based Optimizer

In this study, a hybrid metaheuristic algorithm chaotic gradient-based optimizer (CGBO) is proposed. The gradient-based optimizer (GBO) is a novel metaheuristic inspired by Newton’s method which has two search strategies to ensure excellent performance. One is the gradient search rule (GSR), and the other is local escaping operation (LEO). GSR utilizes the gradient method to enhance ability of exploitation and convergence rate, and LEO employs random operators to escape the local optima. It is verified that gradient-based metaheuristic algorithms have obvious shortcomings in exploration. Meanwhile, chaotic local search (CLS) is an efficient search strategy with randomicity and ergodicity, which is usually used to improve global optimization algorithms. Accordingly, we incorporate GBO with CLS to strengthen the ability of exploration and keep high-level population diversity for original GBO. In this study, CGBO is tested with over 30 CEC2017 benchmark functions and a parameter optimization problem of the dendritic neuron model (DNM). Experimental results indicate that CGBO performs better than other state-of-the-art algorithms in terms of effectiveness and robustness.
Mesozoic dike swarms in Borborema Province (NE Brazil): A structural analysis based on airborne geophysical data and field work

Mapping and analysis of dike swarms support a detailed understanding of the orientation, geometry, spatial distribution, geochronology, emplacement mechanism, structural control, and environments at the time of the magmatic events. Therefore, dike swarms have become a key tool for studying mantle plume activity, large igneous provinces, and continental breakup. Before the Pangea breakup, the Neoproterozoic Borborema Province (NE Brazil) was intensively intruded by sets of dike swarms, whose precise mapping and classification are essential for understanding the tectonic plate movements and evolution of the South American and African continents. In this sense, we promoted a regional mapping of dike swarms and their relationships between orientation, emplacement mechanism and structural control to provide a better understanding of the role of Mesozoic and Cenozoic large magmatic events in their tectonic evolution. We apply anomaly enhancement techniques to high-resolution airborne magnetic data to obtain the geometry and estimate the depth of causative sources. A structural analysis was carried out integrating magnetic patterns, field data and a compilation of previous geological maps to describe the detailed distribution of dike swarms in Borborema Province. Our study demonstrated that dike swarms extend over 5.4 × 105 km2. The 1388 mapped mafic dikes are grouped into three distinct dike swarms (DS): 1135 – Rio Ceará-Mirim DS; 168 – Canindé DS; and 85 – Riacho do Cordeiro DS. The dike swarms show three preferential trends: NE-SW, E-W and NW-SE. The geometry and structural analysis of the dikes allowed us to establish the paleostress field active at the time of intrusion. For the Rio Ceará-Mirim DS, the paleostress trajectories represent an N–S extension, rotating to NW-SE in the western portion of the swarm. For the Canindé DS, the paleostress orientation shows a NW-SE extension rotating to NE-SW. For the Riacho do Cordeiro DS, the trajectories are similar to those traced for the Rio Ceará-Mirim DS, showing a predominant NW-SE trend.
Long-Term Target Tracking of UAVs Based on Kernelized Correlation Filter

During the target tracking process of unmanned aerial vehicles (UAVs), the target may disappear from view or be fully occluded by other objects, resulting in tracking failure. Therefore, determining how to identify tracking failure and re-detect the target is the key to the long-term target tracking of UAVs. Kernelized correlation filter (KCF) has been very popular for its satisfactory speed and accuracy since it was proposed. It is very suitable for UAV target tracking systems with high real-time requirements. However, it cannot detect tracking failure, so it is not suitable for long-term target tracking. Based on the above research, we propose an improved KCF to match long-term target tracking requirements. Firstly, we introduce a confidence mechanism to evaluate the target tracking results to determine the status of target tracking. Secondly, the tracking model update strategy is designed to make the model suffer from less background information interference, thereby improving the robustness of the algorithm. Finally, the Normalized Cross Correlation (NCC) template matching is used to make a regional proposal first, and then the tracking model is used for target re-detection. Then, we successfully apply the algorithm to the UAV system. The system uses binocular cameras to estimate the target position accurately, and we design a control method to keep the target in the UAV’s field of view. Our algorithm has achieved the best results in both short-term and long-term evaluations of experiments on tracking benchmarks, which proves that the algorithm is superior to the baseline algorithm and has quite good performance. Outdoor experiments show that the developed UAV system can achieve long-term, autonomous target tracking.
Adaptive Normalized LMP Estimation for Graph Signal Processing

We propose an adaptive normalized least mean pth power (GNLMP) algorithm for graph signal processing (GSP), which estimates sampled graph signals under impulsive noise. Compared to the recently introduced adaptive GSP least mean pth power (GLMP) algorithm, the GNLMP algorithm reduces the number of iterations to converge to a steady graph signal. Different from adaptive GSP normalized least mean square (GNLMS) algorithm, the GNLMP algorithm has the ability to reconstruct a graph signal that is corrupted by non-Gaussian noise with heavy-tailed characteristics. Simulations show the performance of the GNLMP algorithm in estimating steady-state and time-varying graph signals, utilizing spectral properties such as bandlimitedness and sampling, faster and more robust in comparison to GLMP and GNLMS.
A Randomized Filtering Strategy Against Inference Attacks on Active Steering Control Systems

In this paper, we develop a framework against inference attacks aimed at inferring the values of the controller gains of an active steering control system (ASCS). We first show that an adversary with access to the shared information by a vehicle, via a vehicular ad hoc network (VANET), can reliably infer the values of the controller gains of an ASCS. This vulnerability may expose the driver as well as the manufacturer of the ASCS to severe financial and safety risks. To protect controller gains of an ASCS against inference attacks, we propose a randomized filtering framework wherein the lateral velocity and yaw rate states of a vehicle are processed by a filter consisting of two components: a nonlinear mapping and a randomizer. The randomizer randomly generates a pair of pseudo gains which are different from the true gains of the ASCS. The nonlinear mapping performs a nonlinear transformation on the lateral velocity and yaw rate states. The nonlinear transformation is in the form of a dynamical system with a feedforward-feedback structure which allows real-time and causal implementation of the proposed privacy filter. The output of the filter is then shared via the VANET. The optimal design of randomizer is studied under a privacy constraint that determines the protection level of controller gains against inference attacks, and is in terms of mutual information. It is shown that the optimal randomizer is the solution of a convex optimization problem. By characterizing the distribution of the output of the filter, it is shown that the statistical distribution of the filter’s output depends on the pseudo gains rather than the true gains. Using information-theoretic inequalities, we analyze the inference ability of an adversary in estimating the control gains based on the output of the filter. Our analysis shows that the performance of any estimator in recovering the controller gains of an ASCS based on the output of the filter is limited by the privacy constraint. The performance of the proposed privacy filter is compared with that of an additive noise privacy mechanism. Our numerical results show that the proposed privacy filter significantly outperforms the additive noise mechanism, especially in the low distortion regime.
Visual programming language for data visualization based on visual grammar

This paper describes a visual programming language for data visualization. This language is designed for students and to prototyping different data visualizations. This language allows to create a graphs by connecting nodes with multiple typed inputs and outputs. Nowadays there are no tools for construction complex data visualizations with understandable user interface. The scope of this paper is to describe a visual programming language for complex data visualizations to address those goals.
2PPS — Publish/Subscribe with Provable Privacy

Publish/Subscribe systems like Twitter and Reddit let users communicate with many recipients without requiring prior personal connections. The content that participants of these systems publish and subscribe to is typically public, but they may nevertheless wish to remain anonymous. While many existing systems allow users to omit explicit identifiers, they do not address the obvious privacy risks of being associated with content that may contain a wide range of sensitive information. We present 2PPS (Twice-Private Publish-Subscribe), the first pub/sub protocol to deliver strong provable privacy protection for both publishers and subscribers, leveraging Distributed Point Function-based secret sharing for publishing and Private Information Retrieval for subscribing. 2PPS does not require trust in other clients and its privacy guarantees hold as long as even a single honest server participant remains. Furthermore, it is scalable and delivers latency suitable for microblogging applications. A prototype implementation of 2PPS can handle 100,000 concurrent active clients with 5 seconds end-to-end latency and significantly lower bandwidth requirements than comparable systems.
Self-Adaptive Approximate Mobile Deep Learning

Edge intelligence is currently facing several important challenges hindering its performance, with the major drawback being meeting the high resource requirements of deep learning by the resource-constrained edge computing devices. The most recent adaptive neural network compression techniques demonstrated, in theory, the potential to facilitate the flexible deployment of deep learning models in real-world applications. However, their actual suitability and performance in ubiquitous or edge computing applications has not, to this date, been evaluated. In this context, our work aims to bridge the gap between the theoretical resource savings promised by such approaches and the requirements of a real-world mobile application by introducing algorithms that dynamically guide the compression rate of a neural network according to the continuously changing context in which the mobile computation is taking place. Through an in-depth trace-based investigation, we confirm the feasibility of our adaptation algorithms in offering a scalable trade-off between the inference accuracy and resource usage. We then implement our approach on real-world edge devices and, through a human activity recognition application, confirm that it offers efficient neural network compression adaptation in highly dynamic environments. The results of our experiment with 21 participants show that, compared to using static network compression, our approach uses 2.18× less energy with only a 1.5% drop in the average accuracy of the classification.
A Novel UAV Aerial Vehicle Detection Method Based on Attention Mechanism and Multi-scale Feature Cross Fusion

With the rapid development of artificial intelligence science, more and more researchers try to use deep learning to train neural networks and have achieved great success in object detection. Vehicle detection based on UAV image is a special field of object detection. Due to the low resolution of the vehicle object, complex background, and less image information, it is challenging to extract robust visual and spatial features from the depth network and accurately locate the object in complex scenes. In this paper, combining the characteristics of vehicles in aerial images, we design a novel feature pyramid network called channel-spatial attention fused feature pyramid network (CSF-FPN) with Faster R-CNN as the basic framework. In CSF-FPN, a hybrid attention mechanism and feature cross-fusion module are introduced, so that feature maps can be generated with enhanced spatial and channel interdependence to extract richer semantic information. After our CSF-FPN is integrated into the Faster R-CNN network, the detection performance of small objects is greatly improved. The experimental results based on the VEDIA Dataset showed that the proposed framework could effectively detect the vehicle in large scene azimuth. Compared with the existing advanced methods, mAP and F1-score are improved.
Guaranteeing That Multilevel Prioritized DNN Models on an Embedded GPU Have Inference Performance Proportional to Respective Priorities

When multiple deep neural networks (DNNs) are using an embedded GPU as an accelerator, adjusting the CPU occupancy time of the process encompassing each DNN on a priority basis does not always guarantee that higher priority DNNs take priority over the GPU. To address this problem, we propose a methodology that basically uses the model from PyTorch without modification while providing additional advantages. First, the response performance of higher priority DNNs is improved by allowing DNNs to occupy the GPU in preference in proportion to the priority granted to the hosting processes. Second, it reduces the execution time of each DNN by removing the multicontext overhead that occurs under an environment with an independent process per DNN, executing DNNs in a multithreaded manner to overcome the limitation of pure Python and taking advantage of multistream for concurrent running of different DNN operations inside the GPU.
Semi-Automatic Generation of Training Samples for Detecting Renewable Energy Plants in High-Resolution Aerial Images

Deep learning (DL)—in particular convolutional neural networks (CNN)—methods are widely spread in object detection and recognition of remote sensing images. In the domain of DL, there is a need for large numbers of training samples. These samples are mostly generated based on manual identification. Identifying and labelling these objects is very time-consuming. The developed approach proposes a partially automated procedure for the sample creation and avoids manual labelling of rooftop photovoltaic (PV) systems. By combining address data of existing rooftop PV systems from the German Plant Register, the Georeferenced Address Data and the Official House Surroundings Germany, a partially automated generation of training samples is achieved. Using a selection of 100,000 automatically generated samples, a network using a RetinaNet-based architecture combining ResNet101, a feature pyramid network, a classification and a regression network is trained, applied on a large area and post-filtered by intersection with additional automatically identified locations of existing rooftop PV systems. Based on a proof-of-concept application, a second network is trained with the filtered selection of approximately 51,000 training samples. In two independent test applications using high-resolution aerial images of Saarland in Germany, buildings with PV systems are detected with a precision of at least 92.77 and a recall of 84.47.
Blockchain: Effects in Transactions Costs from Information Governance

This research aims to analyze the effects of the adoption of blockchain on information governance and transaction costs. We present an exploratory, quantitative, and qualitative study. In the first stage, quantitative, a theoretical model was developed and tested based on structural equation modeling (PLS-SEM). Data collection was carried out through an online survey with IT and management professionals involved in blockchain projects from different countries. In the second stage, qualitative, a multiple case study was carried out in order to illustrate how the relations of the developed model are established. In the quantitative phase, the study's hypotheses were supported and it was observed that the adoption of blockchain technology can be seen as a mechanism to reduce transaction costs, since it has a positive impact on information governance. In the qualitative phase, it was observed that, in the three cases analyzed, characteristics such as information security, transparency, and the possibility.
In-depth energy analysis of security algorithms and protocols for the Internet of Things

Devices that populate the Internet of Things (IoT) are typically constrained with respect to energy consumption. When the data that are processed, stored and/or communicated by these devices need to be secured, low-energy security mechanisms have to be designed and implemented. Related work mainly concentrates either on low-energy security algorithms and protocols, or on low-energy wireless communication. However, it is important for system developers to take into account the overall energy consumption of the IoT system when making design choices. Therefore, this work presents an in-depth analysis of the energy consumption of IoT devices that provide end-to-end secure communication and digital signatures. The paper follows a granular approach, profiling and measuring each individual contribution to the overall energy consumption, including the computation of cryptographic operations as well as the wireless transmission of messages in cryptographic protocols. The paper also calculates the minimal time period of a secure communication session in order to minimize the energy impact of the session's setup phase and thus minimize the overall average power consumption. The goal of this work is to provide assistance in the selection of a suitable wireless communication standard and cryptographic cipher suite for building end-to-end secure IoT applications.
Automated search space and search strategy selection for AutoML

Existing works on Automated Machine Learning (AutoML) are mainly based on predefined search space. This paper seeks synergetic automation of two ingredients, i.e., search space and search strategies. Specifically, we formulate the automation of search space and search strategies as a combinatorial optimization problem. Our empirical study on many architecture benchmarks shows that identifying the suitable search space exerts more effect than choosing a sophisticated search strategy. Motivated by this, we attempt to leverage a machine learning method to solve the discrete optimization problem, and thus develop a Layered Architecture Search Tree (LArST) approach to synergize these two components. In addition, we use a probe model-based method to extract dataset-wise features, i.e., meta-features, which is able to facilitate the estimation of proper search space and search strategy for a given task. Experimental results show the efficacy of our approach under different search mechanisms and various datasets and hardware platforms.
Lightweight Speaker Recognition in Poincaré Spaces

This letter proposes a lightweight model for speaker recognition by leveraging a hyperbolic space. The speaker recognition performance heavily depends on the distinctiveness of speaker embeddings induced by metric learning. However, most state-of-the-art embedding methods are typically based on the Euclidean metric space, which does not account for inherent hierarchical structures of speech voice characteristics. The recent development of the neural hyperbolic geometry has demonstrated its effectiveness to model continuous hierarchical structures, which have been typically cumbersome to model by standard deep neural networks. This facet provides an additional by-product of a compact representation. Inspired by the favorable geometry of the hyperbolic geometry, we developed a hyperbolic ResNet for speaker recognition. We found that in smaller dimension regimes than typical cases, the learned speaker embeddings are more discriminative; in other words, more compact at the same level of performance. Our experiments on the large-scale VoxCeleb datasets show that, given the limited channel dimensions of neural networks, our method consistently has favorable performance against the standard ResNet for both speaker recognition and verification tasks.