Reviewerly/exHarmony huggingface dataset:
Mask R-CNN: Detection Performance on SPEED Spacecraft With Image Degradation

Convolutional neural networks in the task of object detection and localization have been evolving in the last few years. Various convolutional network models have been proposed such as Faster Region-Based, Mask Region-Based, Single Shot Detection, and "You Only Look Once" models (with different versions). Although instance segmentation has been explored with many models, the Mask Region-Based Convolutional Neural Network has been one of the most competitive models in terms of overall object detection performance. Its widespread use in many different applications encouraged us to take a closer look at model performance in a unique object detection task, namely the detection of spacecraft images in the wild. The main research question in this paper, is whether this off-the-shelf proposed architecture can effectively detect and localize a spacecraft when using the Spacecraft Pose Estimation Dataset, under a variety of different image degradation factors and at various degradation levels. The inspiration for this investigation is the effect of deep space environments on charge-coupled device image sensors, and other imaging hardware. The capability of detection and localization to continue in the face of pixel loss and Gaussian noise are explored. The effects of training augmentation on object detection performance is another task that has also been studied. Some of our main findings include that supplementing training on degraded images improve significantly the detection results. In low degradation scenarios, the improvement is better than the baseline results. Also, the proposed models is able to detect and localize properly on spacecraft images degraded by pixel loss. The proposed model continues to perform close to baseline in conditions even up to 80% pixel loss for the black background experiments.
Using Particle Swarm Optimization with Gradient Descent for Parameter Learning in Convolutional Neural Networks

The use of gradient-based methods are ubiquitously used to update the internal parameters of neural networks. Problems commonly associated with gradient based methods are the tendency for the algorithms to get stuck in sub-optimal local minima, and their slow convergence rate. Efficacious solutions to these issues, such as the addition of “momentum” and adaptive learning rates, have been offered. In this paper, we investigate the efficacy of using particle swarm optimization (PSO) to help gradient-based methods search for the optimal internal parameters to minimize the loss function of a convolutional neural network (CNN). We compare the metric performance of traditional gradient-baseds method with and without the use of a PSO to either guide or refine the search for the optimal weights. The gradient-based methods we examine are stochastic gradient descent with and without a momentum term, as well as Adaptive Moment Estimation (Adam). We find that, with the exception of the Adam optimized networks, regular gradient-based methods achieve better metric scores than when used in conjunction with a PSO. We also observe that using a PSO to refine the solution found through a gradient-descent technique reduces loss better than when using a PSO to dictate that starting solution for gradient descent. Ultimately, the best solution on the MNIST dataset was achieved by the network optimized with stochastic gradient descent and momentum with an average loss score of 0.0092 when evaluated using k-fold cross validation.
Penetration Planning and Design Method of Unmanned Aerial Vehicle Inspired by Biological Swarm Intelligence Algorithm

Unmanned aerial vehicles (UAVs) are gradually used in logistics transportation. They are forbidden to fly in some airspace. To ensure the safety of UAVs, reasonable path planning and design is one of the key factors. Aiming at the problem of how to improve the success rate of unmanned aerial vehicle (UAV) maneuver penetration, a method of UAV penetration path planning and design is proposed. Ant colony algorithm has strong path planning ability in biological swarm intelligence algorithm. Based on the modeling of UAV planning and threat factors, improved ant colony algorithm is used for UAV penetration path planning and design. It is proposed that the path with the best pheromone content is used as the planning path. Some principles are given for using ant colony algorithm in UAV penetration path planning. By introducing heuristic information into the improved ant colony algorithm, the convergence is completed faster under the same number of iteratives. Compared with classical methods, the total steps reduced by 56% with 50 ant numbers and 200 iterations. 62% fewer steps to complete the first iteration. It is found that the optimal trajectory planned by the improved ant colony algorithm is smoother and the shortest path satisfying the constraints.
Augmented Reality Real-time Drawing Application with a Hand Gesture on a Handheld Interface

Augmented Reality (AR) is a technology that can combine real and virtual objects. Interaction is essential in AR, allowing the user to interact with a virtual object in the natural environment. There is various type of gesture interaction that can be applied in AR. Drawing space can also be related to AR technology with the use of provided gesture interaction. The use of gesture interaction in drawing space has become a popular topic among researchers to apply this concept to AR technology by using bare hands to interact directly with AR. However, the visual and spatial complexity of the canvas requires rethinking how to develop a real-time drawing canvas for AR using real hand gestures on a handheld device. The compatibility to convert from traditional drawing into digital drawing space and the use of hand gestures as input is challenging. Therefore, this paper will discuss the designing and development process to actualize the real-time drawing in handheld AR using the user's real hand. (Abstract)
Source of fund, financial constraints, political instability, and firm innovation: empirical evidence from Arab Spring countries

The purpose of this article is to examine the impact of financial constraints on innovation in countries experiencing the Arab Spring. It also looks at how the impact of financial constraints varies with the source of funding. The study uses 2013 BEEPS data. 2261 companies from 5 MENA countries were included in our sample. The recursive bivariate probit and extended probit models are used. Our findings show that financial constraints negatively affect the likelihood of enhancing a firm's innovative performance. Unlike the existing literature, the effect of financial barriers was not only directly examined, but also the region and fund structure of the company were examined together. Although bank loans directly negatively impact innovation performance, when this effect is considered together with financial barriers, it has been observed that the effects of financial barriers decrease. An even more significant decline is seen with the inclusion of the regional influence.
Research and exploration on the interpretability of deep learning model in radar image

Although deep learning technology has been partially applied in radar image processing, the lack of interpretability analysis and comprehensive performance evaluation of black-box models limits the application performance, credibility, and universality of this technology in the radar image field. Starting from the interpretability, this paper proposes the mechanism analysis idea of deep learning black-box model in the radar image field and carries out experimental verification on the open-source MSTAR radar image dataset. It analyzes the transfer and cognitive mechanisms of the deep learning model and draws significant findings on transfer learning, attribution method application, and model robustness evaluation, filling gaps in the existing literature.
Comparison of Deep Learning Models for Automatic Generation of Product Description on E-commerce site

People can readily post their products on e-commerce sites. When users present their product on an e-commerce site, they must create a document describing the item and encouraging its purchase. However, it is not easy for beginner users to create a sentence that describes a product. For this study, the sentence describing the product is called the product description. We propose a method for automatically generating the product description based on a comparison of LSTM and GPT-2. Specifically, we examine the data structure of the product included in the existing product description. Then we use data based on these data structures as input to compare these two methods. Furthermore, we conduct two experiments to measure the benefits of our proposed method based on our proposed evaluation index.
Adaptive Stress Testing of Collision Avoidance Systems for Small UASs with Deep Reinforcement Learning

The next-generation Airborne Collision Avoidance System for smaller UASs (ACAS sXu) is currently being developed and tested by the Federal Aviation Administration (FAA) to provide detect-and-avoid capability for small unmanned aircraft operating beyond line-of-sight. Due to the complexity and safety-critical nature of the system, safety validation is important not only for the certification of the final system, but also for informing changes during the iterative development process. In this paper, we analyze a prototype of ACAS sXu in simulated aircraft encounters to discover scenarios of small near mid-air collisions (sNMACs), an important safety event in which two aircraft come closer than 50 feet horizontally and 15 feet vertically. Due to the size and complexity of the system as well as rarity of sNMAC events, traditional methods such as Monte Carlo testing often require informed setup and targeting to elicit failures. However, such a dependence on domain knowledge can be incompatible with the independent verification and validation (IV&V) process, the aim of which is to discover unforeseen issues. To address these challenges, we apply an accelerated validation method called adaptive stress testing (AST) to find the most likely sNMAC scenarios without reliance on system introspection. AST uses reinforcement learning to adapt the search towards the most promising areas of the search space as it progresses. We use a state-of-the-art deep reinforcement learning algorithm, proximate policy optimization, to more efficiently search the large and continuous state space. We find that this approach significantly improves the performance of AST compared to a prior approach based on Monte Carlo tree search. We perform experiments using AST to find sNMAC events under various encounter configurations, varying parameters pertaining to dynamics and coordination. Our experiments show AST to be very effective at finding sNMAC scenarios. We summarize our findings, presenting high-level categories of discovered sNMACs and specific examples of encounters in each category.
Handling Concurrency in Behavior Trees

This paper addresses the concurrency issues affecting Behavior Trees (BTs), a popular tool to model the behaviors of autonomous agents in the video game and the robotics industry. BT designers can easily build complex behaviors composing simpler ones, which represents a key advantage of BTs. The parallel composition of BTs expresses a way to combine concurrent behaviors that has high potential, since composing pre-existing BTs in parallel results easier than composing in parallel classical control architectures, as finite state machines or teleo-reactive programs. However, BT designers rarely use such composition due to the underlying concurrency problems similar to the ones faced in concurrent programming. As a result, the parallel composition, despite its potential, finds application only in the composition of simple behaviors or where the designer can guarantee the absence of conflicts by design. In this paper, we define two new BT nodes to tackle the concurrency problems in BTs and we show how to exploit them to create predictable behaviors. In addition, we introduce measures to assess execution performance and show how different design choices affect them. We validate our approach in both simulations and the real world. Simulated experiments provide statistically significant data, whereas real-world experiments show the applicability of our method on real robots. We provided an open-source implementation of the novel BT formulation and published all the source code to reproduce the numerical examples and experiments.
Prioritized Experience Replay for Continual Learning

Humans can learn and accumulate knowledge throughout their lifespan. Similarly, the paradigm of continual learning in artificial intelligence requires that the machine learning model preserve consolidated knowledge if a new task is adapted. However, to overcome catastrophic forgetting, a destructive issue in continual learning, memory-based approaches, replaying old experiences with experience drawn from new tasks or constraining old experiences, need a considerable memory to prevent them from decreasing consolidated knowledge, which is inefficient. To improve the efficiency of old experiences and keep memory small, we introduce prioritized experience replay, which uses a feature margin and classification margin to prioritize representative experiences. The feature margin is a cosine between original experience and average experience, and the classification margin is the correctness of the model to select experience. Experimental results show that prioritized experiences have a positive impact on alleviating catastrophic forgetting, and replaying prioritized experiences stored in a tiny reservoir, relieves overfitting and outperforms state-of-the-art continual learning approaches in a training pass.
Gradient Enhanced Dual Regression Network: Perception-Preserving Super-Resolution for Multi-Sensor Remote Sensing Imagery

Most existing learning-based single image super-resolution (SISR) methods mainly focus on improving reconstruction accuracy, but they always generate overly smoothed results that fail to match the visual perception. Although perceptual quality can be greatly improved via introducing adversarial loss, image fidelity may decrease to some extent. Moreover, most methods are trained and evaluated on simulated datasets and their performance would drop significantly on real remote sensing imagery. To solve the above problems, we propose a new SISR algorithm named gradient enhanced dual regression network (GEDRN). Based on the dual regression framework, we use share-source residual structure and non-local operation to learn abundant low-frequency information and long-distance spatial correlations. Besides, we not only introduce additional gradient information to avoid blurry results but also apply gradient loss and perceptual loss to further improve the perceptual quality. Our GEDRN is trained and tested on real-world multi-sensor satellite images. Experimental results demonstrate the superiority of the proposed method in achieving much better perceptual quality and ensuring high fidelity.
Detection of Tunneling in DNS over HTTPS

Domain Name Service (DNS) enables users to query domain names which are then converted to IP addresses leading traffic to that specific website on the web. DNS over HTTPS (DoH) is a protocol for performing remote DNS resolution via the HTTP protocol. It enables increased user privacy and security by preventing eavesdropping and manipulation of DNS data by man-in-the-middle attacks. DoH helps keep the requests encrypted and minimizes the information exchanged during queries. Along with the added privacy and security advancements, DoH makes it harder to detect tunneling which can be used by malicious actors to transmit sensitive data out of the victim’s computer. DoH Tunneling enables these actors to insert malware or pass stolen information into DNS queries, creating a covert communication channel that bypasses most firewalls and results in sensitive data leakage. In this paper, we explore methods of data collection and detection of DoH tunneling using machine learning techniques based on the packet sizes and duration of requests.
Enhanced Word Embeddings with Sentiment Contextualized Vectors for Sentiment Analysis

Deep Learning models generally use pre-trained word embeddings as features to deal with Natural Language Processing problems. However, because most of these word embeddings are context-based, they cannot learn the complex characteristics of words, which may lead to words with similar vector representations expressing different meanings. For sentiment analysis, this represents a problem. Since these word embeddings fail to capture words’ sentimental information, two words with similar vectors can have opposite sentiment polarities, thus degrading the performance of sentiment analysis. This paper addresses this problem and proposes a novel model named Continuous Sentiment Contextualized Vectors (CSCV). CSCV is designed to learn sentiment embeddings based on the Continuous Bag-of-Words (CBOW) model and sentiment lexicons for a given the word from its surrounding context words. The sentiment vectors obtained from this model are combined with existing pre-trained vectors. Experimental results show that: (1) CSCV can be combined with any pre-trained word-embeddings; (2) the nearest neighbors derived from the generated embeddings are more reasonable than other word embeddings because they are both semantically and sentimentally similar; (3) by using this approach, the accuracy can be improved on sentiment classification.
Securing the E-voting system through blockchain using the concept of proof of work

The governance of most of the country depends on the election system. Almost all countries follow the voting system in the election process and based on which government has decided. In the past, voting was done manually or through ballot boxes, and many countries still followed this pattern. But nowadays, the E-voting system has come into existence, and they are considered to be fast as they generate results quickly. The major drawback of an E-voting system is the security features, and to overcome this situation and many approaches are developed to enhance the security features. In this paper, the Blockchain approach will be implemented to improve the security features of the Electronic-voting system using the concept of proof of work. Flask framework is used for the analysis purpose, and the evaluation parameter used for the validation of the models is security analysis and legal issues. Blockchain consumes so much power during the computation, so the paper has also discussed about the computing cost.
RF Fingerprint Recognition Based On Spectrum Waterfall Diagram

RF fingerprint technology has received great attention and research due to its security and operability. Early research on radio frequency fingerprints mainly revolved around transient signals, and transient characteristics are currently the most mature research category. With the development of wireless communication technology, the existing radio frequency fingerprint identification technology has gradually been unable to meet practical needs. In recent years, with the rapid development of image recognition technology, some scholars have proposed to apply image recognition technology to RF fingerprint identification, thereby converting radio signal recognition problems into image recognition target detection problems, and making full use of advanced image recognition technology to improve equipment recognition accuracy. This paper proposes to convert the original I/Q signal into a spectrum waterfall diagram and input it into the image recognition model for training and recognition. The experimental results show that this method can effectively improve the accuracy of RF fingerprint recognition.
Gradient Boosting Forest: a Two-Stage Ensemble Method Enabling Federated Learning of GBDTs

Gradient Boosting Decision Trees (GBDTs), which train a set of decision trees in sequence with a gradient boosting strategy to fit the features of training data, has become very popular in recent years due to its strong capability in dealing with machine learning tasks. In many well-known machine learning competitions, GBDT even outperforms very complicate deep neural networks. Nevertheless, training such tree-based models requires accessing the whole dataset to find the split points on the features, which makes distributed training of GBDT models difficult. Particularly, in Federated Learning (FL), where training data is decentralized distributed and cannot be shared considering the privacy and security, training GBDT becomes challenging. To address this issue, in this paper, we propose a new tree-boosting method, named Gradient Boosting Forest (GBF), where the single decision tree in each gradient boosting round of GBDT is replaced by a set of trees trained from different subsets of the training data (referred to as a forest), which enables training GBDT in Federated Learning scenarios. We empirically prove that GBF outperforms the existing GBDT methods in both centralized (GBF-Cen) and federated (GBF-Fed) cases. In a series of experiments, GBF-Cen achieves 1.1% higher accuracy on HIGGS-1M dataset over XGBoost and GBF-Fed obtains 12.2%–48.0% lower RMSE loss over the state-of-the-art federated GBDT methods.
Subversion-Resistant Quasi-adaptive NIZK and Applications to Modular Zk-SNARKs

Quasi-adaptive non-interactive zero-knowledge (QA-NIZK) arguments are NIZK arguments where the common reference string (CRS) is allowed to depend on the language and they can be very efficient for specific languages. Thus, they are for instance used within the modular LegoSNARK toolbox by Campanelli et al. (ACM CCS'19) as succinct NIZKs (aka zkSNARKs) for linear subspace languages. Such modular frameworks are interesting, as they provide gadgets for a flexible design of privacy-preserving blockchain applications. Recently, there has been an increasing interest to reduce the trust required in the generator of the CRS. One important line of work in this direction is subversion zero-knowledge by Bellare et al. (ASIACRYPT'16), where the zero-knowledge property even holds when the CRS is generated maliciously.
Recurrent SinGAN

Learning a deep generative model on a single image has attracted considerable attention recently. In this paper, we present a single image generative model, named recurrent SinGAN, based on the recent proposed SinGAN [22]. The original SinGAN relies on a pyramid architecture of generators to learn the patch distribution from the pyramid of multiple image scales. We propose a recurrent generator to replace the pyramid of generators in SinGAN with a single recurrent generator. Our single recurrent generator can learn the patch distributions across multiple scales, yielding a scale-agnostic single image generative model.
Class Imbalance Learning to Heterogeneous Cross-Software Projects Defect Prediction

Heterogeneous CPDP (HCPDP) attempts to forecast defects in a software application having insufficient previous defect data. Nonetheless, with a Class Imbalance Problem (CIP) perspective, one should have a clear view of data distribution in the training dataset otherwise the trained model would lead to biased classification results. Class Imbalance Learning (CIL) is the method of achieving an equilibrium ratio between two classes in imbalanced datasets. There are a range of effective solutions to manage CIP such as resampling techniques like Over-Sampling (OS) &amp; Under-Sampling (US) methods. The proposed research work employs Synthetic Minority Oversampling TEchnique (SMOTE) and Random Under Sampling (RUS) technique to handle CIP. In addition to this, the paper proposes a novel four-phase HCPDP model and contrasts the efficiency of basic HCPDP model with CIP and after handling CIP using SMOTE &amp; RUS with three prediction pairs. Results show that training performance with SMOTE is substantially improved but RUS displays variations in relation to HCPDP for all three prediction pairs.