Reviewerly/exHarmony huggingface dataset:
Cross-Domain Infrared Image Classification via Image-to-Image Translation and Deep Domain Generalization

In target recognition, the information about the target usually exists in several domains captured by different sources (sensors). However, it is difficult for us to obtain the perfect target information as the source domain data due to the sensors' limitations sometimes. For the target classification of visible and infrared paired images, we assume that some classes of visible and infrared paired images and other classes of visible images can be obtained, whereas other classes of unseen infrared images need to be classified. This problem is actually a zero-shot deep domain adaptation (ZDDA) problem which divides the data into task-relevant (T-R) data and task-irrelevant (T-I) data. Moreover, the classes of T-R data require recognition, while the classes of T-I data do not need. The traditional ZDDA method sacrifices the classification accuracy of T-R data in the target domain for the generalization ability of T-I data in the source domain. So we propose a method to solve the problem in another way. More precisely, we first use the image-to-image translation network to learn the mapping between the source domain (visible images) T-I data and the target domain (infrared images) T-I data, and convert the visible T-R images to pseudo-infrared images. Then the pseudo-infrared images and the inverted grayscale T-R images are combined to construct a new hybrid domain (source domain I). Meanwhile, we also construct a hybrid domain (source domain II) of T-I images similarly. Besides, we use the infrared T-I images to construct the third domain (source domain III). Finally, we design a deep domain generalization method for cross-domain infrared image classification. And the total loss consists of the classification loss of the source domain I and the distribution alignment loss between the source domains II and III. We evaluate our method using VAIS ship and RGB-NIR scene datasets. The experimental results demonstrate the effectiveness of the proposed method.
Analysis Of COVID-19 Effects On Wellbeing - Study Of Reddit Posts Using Natural Language Processing Techniques

The COVID-19 pandemic continues to negatively impact people's mental health worldwide. Due to the rise in unemployment, loss of income, and lack of social interaction, people are now more likely to feel lonely, go on fewer outings, and dread the unexpected nature of viral transmission. Meanwhile, Public Health authorities are interested in monitoring people's mental and emotional well-being. In this paper, natural language processing is used to analyze human sentiments concerning the COVID-19 pandemic that has been dangerously affecting individuals' mental and physical well-being for more than two years now. Even though several waves of COVID-19 have passed, of which the first and third waves i.e., the initial pandemic period from 20th March 2020 and the rise of the Delta variant from January 2020 had the most impact on the mental health of individuals, this is further evident by the results of this paper. This research focuses on how severely this virus has affected people's mental health and emotions. After processing the data i.e., cleaning, formatting, and removing irregularities from the data, feature engineering models are applied to acquire the results. The results through VADER (valence-aware dictionary and sentiment reasoning) indicate an increase in overall negative sentiments between two mentioned periods. Additionally, the NRC-EIL (National Research Council of Canada - Emotion Intensity Lexicon) analysis showed that “fear” and “sadness” occurred during those times.
Reservoir modeling of distributed-parameter systems

We investigate possibilities of effective modeling the distributed-parameter systems using reservoir computing in such a way as to properly reconstruct the spatio-temporal dynamics defined in a given multidimensional domain. The setting examined here corresponds to situations where the locations of sensors and actuators are fixed allowing to delegate the complex system dynamics to a reservoir-based neural network which is trained based on the available measurement data. The proposed approach consists in imposing a proper partitioning of the spatial domain, then a specific structure of an echo state network is used to form the reservoir capable to follow not only temporal but also spatial dynamics of the system. As a result, an extremely effective approximator of the distributed system is obtained with relatively simple training procedure to deliver readout of the system state. Contrary to the classical numerical methods for solving distributed-parameter systems characterized by a large-scale we are able to reduce the dimesionality and computational-cost useful for state prediction and/or estimation in the real-time. The performance of the proposed approach is evaluated by numerical experiments on the transient displacements of the clamped plate.
Asymptotic Stabilization over Encrypted Data with Limited Controller Capacity and Time-varying Quantizer

We consider a problem of implementing dynamic controllers over encrypted data for asymptotic stabilization of closed-loop systems. Though a time-varying quantizer is used and it can be infinitesimally fine with time, a major issue is that the underlying space for encrypted messages is unavoidably finite and the controller receives a limited amount of quantized data. To resolve this issue, the proposed method takes advantage of the state matrix consisting of integers, which enables the controller to generate only lower bits of the same output without computing the upper bits. Whenever a portion of the upper bits of output has converged, the computation scope can be moved further lower, receiving only lower bits of the measurement. The quantization is scheduled and the size of the message space is predetermined from the convergence rate, so that the feedback input is restored from the outcome of the lower bits, no matter how fine quantization is performed in the end. As a consequence, asymptotic stabilization can be achieved by encrypted operation, despite the limited controller capacity.
Traffic Sign Recognition Using Ulam's Game

In this paper, we propose a conditional early exiting framework with Ulam's Game for traffic sign recognition. Since the traffic sign recognition system has extremely high requirements on dynamic performance, we pays more attention to improving the detection efficiency, hoping to obtain results in a shorter time. In our system, we use a modified ResNet-50 as backbone network to do feature extraction and use a Pooling module to accumulate feature. Then, we have a Gate module to determine whether the feature have accumulated enough to begin Ulam's Game. A classifier is used to get candidate results, which are used to run Ulam's Game and get the final prediction. The model shows good detection accuracy and dynamic performance in multiple data sets (Mini-Kinetics, ActivityNet, Lisa).
Adversarial Cross-modal Domain Adaptation for Multi-modal Semantic Segmentation in Autonomous Driving

3D semantic segmentation is a vital problem in autonomous driving. Vehicles rely on semantic segmentation to sense the surrounding environment and identify pedestrians, roads, and other vehicles. Though many datasets are publicly available, there exists a gap between public data and real-world scenarios due to the different weathers and environments, which is formulated as the domain shift. These days, the research for Unsupervised Domain Adaptation (UDA) rises for solving the problem of domain shift and the lack of annotated datasets. This paper aims to introduce adversarial learning and cross-modal networks (2D and 3D) to boost the performance of UDA for semantic segmentation across different datasets. With this goal, we design an adversarial training scheme with a domain discriminator and render the domain-invariant feature learning. Furthermore, we demonstrate that introducing 2D modalities can contribute to the improvement of 3D modalities by our method. Experimental results show that the proposed approach improves the mIoU by 7.53% compared to the baseline and has an improvement of 3.68% for the multi-modal performance.
Robust Training of Object Detection with Sparse Annotations

It is difficult to annotate all instances for object detection tasks in practice, especially facing the crowned scenes. The model performance could be impaired when training on sparsely annotated datasets, compared to fully annotated ones. To alleviate this problem, we propose a novel robust training framework for sparsely annotated object detection. We take the sparse annotations as a special case of noisy annotations, where the instances without annotations are regarded as background. First, we propose an Ensemble Prediction (EP) module from different temporal models to avoid the model fitting to the background (noisy labels). Further, an Ensemble Models Non-Maximum Suppression (EM-NMS) module is proposed based on the EP module to generate more robust bounding boxes. Finally, the Pseudo Labels Generating (PLG) module is used to produce pseudo labels for the possible missing-annotation instances. Extensive validation experiments are conducted on PASCAL VOC and MS COCO datasets with sparse annotations. The results show that our proposed robust training framework can further improve model performance when compared to related studies.
Near Optimality of Finite Memory Policies for POMPDs with Continuous Spaces

We study an approximation method for partially observed Markov decision processes (POMDPs) with continuous spaces. Belief MDP reduction has been the standard approach to study POMDPs, which, due to its uncountable state space and strict regularity properties however, requires rigorous approximation methods for practical applications. In this work, we focus on an approximation procedure via discretizing the observation space and constructing a fully observed finite MDP model using a finite length history of the discrete observations and control actions. We show that the resulting policy is nearly optimal under some regularity assumptions on the channel, and under certain controlled filter stability requirements for the hidden state process. We also provide a Q learning algorithm that uses a finite memory of discretized information variables, and prove its convergence to the optimality equation of the finite fully observed MDP constructed using the approximation method.
Non-Gaussian Chance-Constrained Trajectory Control Using Gaussian Mixtures and Risk Allocation

Standard chance-constrained trajectory control algorithms typically rely on the assumption that the vehicle state uncertainties obey Gaussian distributions. While this is a valid assumption for many systems, this paper considers the class of real-life systems for which this assumption does not hold - for example, systems with highly nonlinear dynamics such as spacecraft maneuver planning problems. This paper extends the chance-constrained control formulation to consider a non-Gaussian distribution by approximating the distribution as a mixture of Gaussian distributions. The original chance constraint is then approximated as a conjunction of weighted individual chance constraints on each of the distributions in the mixture. Iterative risk allocation is used in a two-stage op-timization procedure to minimize the degree of conservatism in this approximation. The method is applied to a simple impulsive stochastic spacecraft maneuver targeting problem in two-body dynamics. The resulting algorithm accurately computes control parameters that satisfy the probabilistic bounds on the non-Gaussian distribution.
Robust visual servoing for mobile robots with the VFO-ADRC system

Uncertain extrinsic and intrinsic parameters of an on-board camera used for visual servoing of a mobile robot may cause an unacceptable control performance. By application of the Vector-Field-Orientation (VFO) control design approach we propose a nominal VFO control law, extended next with a complementary Active Disturbance Rejection Control (ADRC) loop, for robust set-point control of mobile robots in a camera-image space. We show that the VFO-ARDC system can be made robust even to large parametric uncertainties of the servoing model, by verifying both the convergence robustness and performance robustness of the resultant closed-loop system. Formal considerations are supported by simulation results.
Embedded Hash Codes for Image Similarity Detection and Tamper Proofing

The image and video traffic on the Internet has increased due to the ease and abundance of the image capture and sharing services. However, this can sometimes result in violations of copyright and image tampering for various purposes. Finding differences between images has importance for image retrieval, aiding forensics, disease diagnosis, and environmental changes etc. Many similarity measures have been proposed that use different features extractable from the detected image. In some cases it is important to determine if the set of images differ and if so then to what extent. Digital Rights Management (DRM) has gained prominence and importance due to widespread availability of tools to modify images. Sometimes the need arises to claim the rightful ownership of an image. The blockchain through the use of hash codes can be used to determine tampering similar to its success in many industries where privacy and security of the documents is important. In this paper, we embed the image hash code in the JPEG image header. The proposed system simplifies the hashing and encryption process by processing only the image data without which a reconstruction is not possible. The results show that the image data can thus be shared without the risk of any undetected tampering or misuse.
Safety Verification of Neural Network Control Systems Using Guaranteed Neural Network Model Reduction

This paper aims to enhance the computational efficiency of safety verification of neural network control systems by developing a guaranteed neural network model reduction method. First, a concept of model reduction precision is proposed to describe the guaranteed distance between the outputs of a neural network and its reduced-size version. A reachability-based algorithm is proposed to accurately compute the model reduction precision. Then, by substituting a reduced-size neural network controller into the closed-loop system, an algorithm to compute the reachable set of the original system is developed, which is able to support much more computationally efficient safety verification processes. Finally, the developed methods are applied to a case study of the Adaptive Cruise Control system with a neural network controller, which is shown to significantly reduce the computational time of safety verification and thus validate the effectiveness of the method.
The Role of Activation Function in Neural NER for a Large Semantically Annotated Corpus

Information extraction is one of the core fundamentals of natural language processing. Different recurrent neural network-based models have been implemented to perform text classification tasks like named entity recognition (NER). To increase the performance of recurrent networks, different factors play a vital role in which activation functions are one of them. Yet, no studies have perfectly analyzed the effectiveness of the activation function on Named Entity Recognition based classification task of textual data. In this paper, we have implemented a Bi-LSTM-based CRF model for Named Entity Recognition on the semantically annotated corpus i.e., GMB, and analyzed the impact of all non-linear activation functions on the performance of the Neural Network. Our analysis has stated that only Sigmoid, Exponential, SoftPlus, and SoftMax activation functions have performed efficiently in the NER task and achieved an average accuracy of 95.17%, 95.14%, 94.38%, and 94.76% respectively.
Score-and-search methods for the recovery of structure in networks of dynamic systems

A relevant problem in many areas of science is to determine the structure of a network by observing its nodes. A desirable property of any network reconstruction techniques is consistency, namely the convergence of the reconstructed network to the actual structure when the time horizon of the observations goes to infinity. Unfortunately, when feedthrough components are present in the network, multiple structures could give rise to the same observations. Hence, in these situations, the best theoretical result that can be achieved is the determination of all the possible structures compatible with what is being observed. There are some results offering such theoretical guarantees, but these methods rely on a large number of statistical tests making their sample complexity relatively large. This article proposes the adoption of reconstruction techniques where, given the observed data, each structure is evaluated according to a score representing the likelihood that such structure is the actual one. Such a technique is proven to have the same consistency properties of state-of-the-art methods based on statistical tests, while numerical experiments show it to have a lower sample complexity.
UAV Path Planning for Complete Structural Inspection using Mixed Viewpoint Generation

A sensor-equipped Unmanned Aerial Vehicle (UAV) can be used to gather surface information of a structure, performing an autonomous inspection. It can significantly improve the efficiency of the visual inspection tasks and reduce the operating cost and the human risks involved in the tasks. A complete inspection coverage path generating method comprising two sets of viewpoints is presented in this research. The first set of viewpoints, revolving viewpoints, is generated based on the geometry of the inspection target and revolving around the target. The second set of viewpoints, gap-filling viewpoints, is generated to compensate for the insufficiency of the revolving viewpoints, filling all the unseen surface patches and guaranteeing full coverage. The system's scalability was proven by the planning outcomes for two different structures on different scales, which met both requirements of close-up inspection and photogrammetry.
Improving Hazy Image Recognition by Unsupervised Domain Adaptation

Deep learning has achieved excellent performance in computer vision tasks, like image recognition, natural language processing, etc. However, in real-world applications, special circumstances brought about by the external world may create domain bias caused by distribution discrepancy between training and testing data, leading to degrading model performance. For example, when auto-driving meets hazy weather, the model performance will drop significantly. In this paper, we explore to solve this problem by utilizing modern Domain Adaptation (DA) methods, which generalizes from the source domain to the target domain by minimizing the distribution difference caused by dataset bias. We firstly propose the cross-domain haze image datasets and benchmark the five classic DA methods. The experiments show that DA methods can mitigate the negative effect of haze and significantly improves the model performance for visual recognition.
Development and testing of a collision avoidance algorithm for industrial applications

Safe interaction of the operator and the cobot in the co-working space is one fundamental requirement for introducing automation in small and medium enterprises characterized by unstructured cell layouts. In addition to the exploitation of cobots, a fully safe interaction requires the adoption of collision avoidance systems, to enable the real-time re-planning of the end-effector trajectories thus avoiding to stop the machine in case of collision with humans. This is fundamental for industrial applications, in order to maintain the production rate as constant as possible even in unstructured production environments. However, collision avoidance applications in actual industrial production cells are still limited, due to some limitations which are not yet completely solved. It is indeed still likely to happen that the recalculation of the obstacle-free trajectory takes a too long time, not compatible with industrial applications. This paper describes the development of a collision avoidance program using the Lazy PRM* algorithm. The program is tested on a physical robot, the Mitsubishi Melfa RV-5AS-D, to perform some exemplary pick and place tasks. The developed algorithm can alter online, during the execution, the road-map from dense to sparse in those cases in which the search for an alternative trajectory is detected to take too long time. Compared to other commercial collision avoidance systems, in which the evaluation of road-maps can only be done offline once and for all, this feature would prevent the robot to get stucked in case a feasible solution is not found in a fast enough way. Furthermore, the developed program guarantees greater flexibility in creating the road-map, trying to match the user's needs, and eliminating the superfluous parts of the road-map to reduce computational time. This marks a difference with present-day solutions for commercial collision avoidance systems which, to the author knowledge, connects all the nodes together.
Resource Allocation in a Secure Internet of Battle Vehicles Through RF Fingerprint Recognition

In the internet of battle vehicles (IoBV), a few unauthorized users may attack and intrude on a network. The attackers who sneak into the network may maliciously occupy invaluable wireless resources that are supposed to serve authorized users. To guarantee the quality of service (QoS) of authorized users, we novelly propose an architecture of secure IoBV through the radio frequency (RF) fingerprint recognition technology and investigate the optimal resource allocation scheme under such an architecture. With few training samples, we propose a meta-transfer-based few-shot learning method for RF fingerprint recognition and an algorithm to optimize the data rate in an IoBV. Compared with a few benchmarks, the analysis results show that our proposed algorithm can improve the overall data rate and reduce the computation time.
Split: A Hash-based Memory Optimization Method for Zero-Knowledge Succinct Non-Interactive Argument of Knowledge (zk-SNARK)

Zero-Knowledge Succinct Non-Interactive Argument of Knowledge (zk-SNARK) is a practical zero-knowledge proof system for Rank-1 Constraint Satisfaction (R1CS), enabling privacy preservation and addressing the previous scalability concerns on zero-knowledge proofs. Existing constructions of zk-SNARKs require huge memory overhead to generate proofs in that the size of the zk-SNARK circuit can be large even for a very simple use case, which limits the applications for regular resource-constrained users. To reduce the memory utilization of zk-SNARKs, this paper presents a hash-based method “Split”. Concretely, Split intends to partition the zk-SNARK circuits so that components can be processed sequentially while ensuring strong security properties leveraging hash circuits. As a zk-SNARK circuit is partitioned, obsolete variables are no longer preserved in the memory. We further propose an enhanced Split as <inline-formula><tex-math notation="LaTeX">$n$</tex-math></inline-formula> -Split, which leads to better optimization by properly choosing multiple splits. Our experimental results validate the effectiveness and efficiency of Split in conserving memory usage for resource-constrained provers as long as the circuit can be partitioned to a Good Split, indicating that via Split zk-SNARKs can be brought one step closer to practical applications.