Reviewerly/exHarmony huggingface dataset:
Weak Supervised Learning of Environmental Emitters

Emitter Classification is a critical aspect of the warfighter for mission effectiveness. Traditional supervised learning techniques require comprehensive labeled training data for each emitter class, but labeled emitter track data is scarce, and often the ambiguous labels that accompany this data present multiple candidate emitter IDs, of which one or none in the list is the true label. In this paper, we present a series of weak supervised learning techniques and observations that can be used to learn from the vast stores of unlabeled or weakly labeled data as well as the available sparse ground-truth labeled data. Emitter Classification is a critical aspect of the warfighter for mission effectiveness. Traditional supervised learning techniques require comprehensive labeled training data for each emitter class, but labeled emitter track data is scarce, and often the ambiguous labels that accompany this data present multiple candidate emitter IDs, of which one or none in the list is the true label. In this paper, we present a series of weak supervised learning techniques and observations that can be used to learn from the vast stores of unlabeled or weakly labeled data as well as the available sparse ground-truth labeled data.
Cooperative Trajectory Planning for Multi-target Tracking Using Passive Sensors

Target tracking missions can be efficiently performed by the highly maneuverable unmanned aerial vehicle (UAV) teams. To enhance the system observability of multi-target tracking problems with passive sensors, this paper develops a cooperative trajectory optimization method for a UAV team. An approximate performance metric that can be utilized in trajectory optimization is derived based on the multi-sensor joint probabilistic data association filter (JPDAF). Then we proposed a distributed multi-UAV trajectory planning algorithm leveraging the incremental optimization strategy to track multiple moving targets. The effectiveness of the proposed algorithm is demonstrated with simulation examples.
Multi-group Discrete Symbiotic Organisms Search Algorithm Based Clustering Method for Large-Scale Traveling Salesman Problem

In recent years, the Traveling Salesman Problem (TSP) has become an indispensable part of the current supply chain system. The solution and optimization of this problem, especially the Large-Scale Traveling Salesman Problem (LSTSP), attract a large number of scholars and experts. The swarm intelligent algorithm, also known as the meta-heuristic evolutionary algorithm, is also developing rapidly in modern times. Especially the outstanding performance in solving complex problems has led many scholars to use the idea of swarm intelligence to solve or optimize some complex problems. The Symbiotic Organisms Search(SOS) algorithm is a swarm intelligent algorithm that has emerged in recent years. Its excellent global optimization and convergence capabilities have won the favor of many experts and researchers. This paper proposes a Multi-group Discrete SOS based on the K-Means clustering (MDSOSKMC) algorithm for solving LTSP. In order to test the performance of our proposed algorithm, we use some large-scale instance set in TSPLIB for verification.
Confident Collaborative Metric Learning

Modern recommendation systems use embedding for secondary applications and implicit feedback data for learning. In recent years, collaborative metric learning (CML), a method that can precisely capture the relationship between users and items, has been developed for the first requirement. However, CML with implicit feedback data suffers from noisy-label issues. This is mainly because CML and the related works only consider few types of noise. To overcome these limitations, we develop a method for estimating the noise rate and excluding extremely noisy user–item pairs from the data prior to learning CML. Experimental results show that the proposed method significantly outperforms existing methods in terms of ranking metrics.
Eye-Tracking as a Research Method in Language Testing

Eye-tracking, which involves the recording of people’s eye movements, has only recently been adopted as a research method in the fields of second language learning and testing. This chapter provides examples of studies that have demonstrated how eye-tracking methodology can inform language test theory, development, and validation. It also illustrates the type of research questions it could help answer, while pointing out the need for methodological adaptations to fit the context of language testing.
Robot path planning based on Ant colony Algorithm

In view of the problems of ant colony algorithm in global path planning under static environment, such as slow convergence speed, great blindness of path search and easy access to local optimal solutions, an improved ant colony algorithm is proposed. Taking the rasterization map as the running environment of the robot, the initial pheromones were distributed unevenly, so that the path search tended to be near the line between the starting point and the target point. Pseudo random strategy was introduced on path selection probability to reduce the blindness of path selection and speed up finding the shortest path. The volatilization coefficient was adjusted dynamically to make the volatilization coefficient larger in the early stage and smaller in the later stage, so as to avoid premature convergence of the algorithm. Finally, the path planning results before and after the improvement were discussed, and the influence of important parameters on the results of ant colony algorithm was analyzed.
Blindly Predict Image and Video Quality in the Wild

Emerging interests have been brought to blind quality assessment for images/videos captured in the wild, known as in-the-wild I/VQA. Prior deep learning based approaches have achieved considerable progress in I/VQA, but are intrinsically troubled with two issues. Firstly, most existing methods fine-tune the image-classification-oriented pre-trained models for the absence of large-scale I/VQA datasets. However, the task misalignment between I/VQA and image classification leads to degraded generalization performance. Secondly, existing VQA methods directly conduct temporal pooling on the predicted frame-wise scores, resulting in ambiguous inter-frame relation modeling. In this work, we propose a two-stage architecture to separately predict image and video quality in the wild. In the first stage, we resort to supervised contrastive learning to derive quality-aware representations that facilitate the prediction of image quality. Specifically, we propose a novel quality-aware contrastive loss to pull together samples of similar quality and push away quality-different ones in embedding space. In the second stage, we develop a Relation-Guided Temporal Attention (RTA) module for video quality prediction, which captures global inter-frame dependencies in embedding space to learn frame-wise attention weights for frame quality aggregation. Extensive experiments demonstrate that our approach performs favorably against state-of-the-art methods on both authentically distorted image benchmarks and video benchmarks.
Self-Supervised Learning for Action Recognition by Video Denoising

Deep learning is a data-hungry technique that is more effective when being applied to large datasets. However, large-scale annotation datasets are not always available. A new approach, such as self-supervised learning of which labels can be automatically generated, is essential. Therefore, using self- supervised learning is a new approach to state-of-the-art methods. In this paper, we introduce a new self-supervised method namely video denoising. This method requires an autoencoder model to restore original videos. The second model is proposed, which is called the discriminator. It is used for the quality evaluation of output videos from the autoencoder. By reconstructing videos, the autoencoder is learned both spatial and temporal relations of video frames to process the downstream task easily. In the experiments, we have demonstrated that our model is well transferred to the action recognition task and outperforms state- of-the-art methods on the UCF-101 and HMDB-51 datasets.
Strategies to promote university permanence in times of COVID-19

The research provides strategies that allow to promote school permanence at the Technological University of the Southeast of Veracruz (TUSV) during the period of confinement derived from the contingency due to SARS COV II. Information was collected from various sources: department of school services, tutoring and through a survey-type instrument with seventy questions, using the Likert scale and it was carried out to evaluate four tutorial areas experienced during the pandemic, such as: health, academic, socioeconomic, and personal. The survey was applied to 420 students out of a population of 1208, for which there is a level of confidence higher than 95%. Derived from the analysis of data on failure, monitoring of tutorials and tutorial areas, the diagnosis was made, generating a list of findings, derived from the above, strategies validated by a group of experts are proposed, such as: Establish an induction program for teachers and students, institutionalization of leveling courses related to the result of the entrance exam, creating the area of emotional support, Implementing technological tools for academic monitoring, establishing regularization groups, peer support, positive incentives for outstanding students, generating financial plans and establish a personal development program.
A Robust Reversible Data Hiding Scheme for Color Image Based on Ambtc Domain

Compression is a common processing method of digital image, and the compression of carrier image usually affects the performance of hidden information. Therefore, it is necessary to research on the compressed domain method to improve the performance of information hiding algorithm. Aiming at the difficulty of improving the capacity and robustness of image information hiding algorithm in compressed domain, we propose a reversible data hiding algorithm in absolute moment block truncation coding domain based on human vision system. Firstly, RGB color image is converted to YCbCr image, AMBTC coding is used to generate high and low average value sequence in brightness component. Secondly, the secret information is compressed by run length encoding and scrambled. Thirdly, the histogram shift technique is used to embed the information into the mean sequence and generate the reserved information at the same time. Fourth, the color component is divided into normal block and absolute smooth fast, the specific position of the bit plane of the normal block is deleted, and the reserved information is embedded. attack, the extracted secret information still has high recognizability. Experimental results show that, compared with other algorithms, the proposed algorithm has better invisibility and robustness, the embedding capacity has been significantly improved. At the same time, the proposed algorithm has strong analysis resistance, can effectively resist most image processing attacks, and the secret information extracted by the attack is still highly recognizable.
Image-Based Authentication Security Improvement by Randomized Selection Approach

In recent days, the value of data stored in disk space (may be localized or cloud) is on the higher side while comparing to the past. In these circumstances, higher the information value provides the higher possibility for data hacking. This situation will develop further and will not be a setback, which implies that the security of those data should be improved as well. This element of security enhancement will be dependent on increasing physical and electronic security. Sensor-based and sensorless approaches can be used to offer electronic data authentication. The sensor-based method depends on specific extra feature on the login device and the state of sensible item with environmental barriers to provide authentication. This raises the question of (i) specified extra feature availability with cost associated with it, (ii) accuracy of sensor devices with respect to sensible item with environmental impact, and (iii) device stability, reliability, and also additional power consumption for sensing device. When coming to sensorless approach for authentication, the simple traditional password scheme is not enough now, and there are some authentication schemes, which will make us to enter different password or pass input ever time, which are already in the pool. This raises the question of the pool size and user remembrance, which is proportional to pool size. If we need a better security, there is a demand to increase the pool size and result in increasing the burden to remember past input for authentication. This research work focuses on reducing the burden of remembering pass input with larger pool. This paper proposes a novel method and implements a bag of password scheme to overcome the aforementioned drawback. As a consequence, with the proposed technique, we are determining the smallest amount of random selection required to select the ideal pool size, resulting in greater authentication security and less complexity from the end user's perspective.
Fully-Supervised Semantic Segmentation Networks: Exploring the Relationship between the Segmentation Networks Learning Ability and the Number of Convolutional Layers

The deeper the network, the more computing resources are needed, especially for semantic segmentation networks. We only know that within a certain range, the more convolutional layers, the better, but we don't know whether the number of convolutional layers in the backbone network has met the needs of segmentation tasks and whether the learning ability of the shallow networks is fully exploited. In this paper, we first propose to introduce the hyper-parameters of the number of convolutional layers into the deep learning training task. Based on prior knowledge, we design the loss-weight of the depth supervision branches in the basic semantic segmentation that has a linear function or a quadratic function relationship with the number of convolutional layers. The deep supervision layer is used as a tool to output the loss of the convolutional layer at different positions during the training process, and it becomes invalid during the test process. For the medium size networks, the method we designed improves the MIoU score of the PSV and CamVid data sets by an average of 1.37% and 0.94 % with Lovasz-Softmax loss function, respectively. Our approaches get significant improvement on shallow networks with MIoU scores by nearly 1% and 8% on the PSV dataset and the CamVid dataset respectively. Experimental results on the Cityscapes and CamVid datasets also show that by using deep supervision branches with loss weights, the learning ability of the model can be stably improved without increasing model parameters and without changing the model's backbone network structure.
DYME: A Dynamic Metric for Dialog Modeling Learned from Human Conversations

With increasing capabilities of dialog generation methods, modeling human conversation characteristics to steer the dialog generation towards natural, human-like interactions has garnered research interest. So far, dialogs have mostly been modeled with developer-defined, static metrics. This work shows that metrics change within individual conversations and differ between conversations, illustrating the need for flexible metrics to model human dialogs. We propose DYME, a DYnamic MEtric for dialog modeling learned from human conversational data with a neural-network-based approach. DYME outperforms a moving average baseline in predicting the metrics for the next utterance of a given conversation by about 20%, demonstrating the ability of this new approach to model dynamic human communication characteristics.
The Unexpected Consequences of a Pandemic: Crypto-Finance As Cultural Commons

In this article, I provide some coordinates that help explain why cryptocurrencies have become mainstream in recent days, indicating its connection to precarisation and new class formations. I will also look at how this change has been achieved by examining the impact of ICTs (information and communications technologies) on the process of socialisation and re-signification of finance. Finally, I will explore how these shifts might be related to the emergence of a peculiar form of digital and cultural commons. I grant that this last idea might appear odd, far-fetched, or downright inappropriate, since investment by individuals for individual profit is a defining characteristic of the realm of crypto-finance. I hope to show, however, that what lies behind these individualised actions is an ambivalence in which exploitation coexists with a redefinition of the genetic code of finance, giving rise to a shared culture and a commoning of resources.
Sigmoid Function Optimization for Path Following Control with Obstacle Avoidance of an Autonomous Truck-trailer

In this study, an obstacle avoidance maneuver with sigmoid function optimization is applied on the path following control of the Autonomous truck-trailer. The accumulation of trailers and traffic density become obstacles in the lanes traversed by trailer trucks. A control design is needed that can perform obstacle avoidance actions in the form of optimal path changes. In the application of a path-following autonomous truck-trailer, the avoidance path reference must be dynamically adjusted to bring the system to a lane change. The optimization of the sigmoid function was proposed to obtain the optimal obstacle avoidance path reference based on the traction speed and steering angle. The MATLAB Fmincon Nonlinear Programming function is used to get the optimal solution which affects the concavity and distance of the turning path to the obstacle. The optimal turning path is integrated with the Lyapunov stability-based path follower control. This is intended to obtain a global asymptotic stable condition both under normal conditions and to avoid obstacles. Simulation is done by comparing the path pattern of the optimized and unoptimized sigmoid curves. The simulation results show the optimal turn path based on the mean of orientation angle and traction velocity in addition to the value of the performance index.
Detection of Nudges and Measuring of Alignment in Spoken Interactions

Nudges, techniques that indirectly influence human decision making, are little studied in spoken interactions. However, the limits of human-computer spoken interactions are not controlled, allowing machines realize bad nudges. In this context a framework for detecting nudges is needed to enhance the ethics of HCI. The work proposed in this PhD thesis is based on the hypothesis that the detection of nudges lies in measuring linguistic, paralinguistic and emotional alignments between interlocutors. Therefore, this PhD thesis aims to answer two research questions. First, does a high level of linguistic and paralinguistic alignement influence human&#x0027;s potential to be nudged? Second, if a person is resistant to other&#x0027;s emotions is she or he less sensible to be nudged? To get a better understanding of the correlation between alignment and nudges, but also a human&#x0027;s potential to be nudged knowing their level of alignment, we will conduct a series of experiments.
Attacking Image Classifiers To Generate 3D Textures

Deep learning has been successfully used for many 2D image synthesis tasks such as in-painting, super-resolution, and image-to-image translation. Generative tasks in the 3D space are also becoming popular such as 3D volume generation from images, image based styling and subsequent texture mapping on 3D meshes, and novel view synthesis from single/multiple views. These are mainly possible due to advances in differentiable rendering. Some recent works also suggest the feasibility of generative tasks on 2D images through adversarial attacks, a topic that remains largely unexplored in the 3D domain. This paper bridges the gap and shows the potential of adversarial attacks for the task of 3D texture generation. It proposes the first of its kind method to re-purpose visual classifiers trained on images for the task of generating realistic textures for 3D meshes without the need for style images, multi-view images or retraining. Instead, our schema uses a targeted adversarial attack to directly minimize the classification loss of an ensemble of models whose gradients are backpropagated to estimate the texture for an input 3D mesh. We show promising results on 3D meshes and also propose a metric to evaluate the texture quality.
Distance Learning and Artificial Intelligence: New Challenges for Donald School Educational Activities

EDITORIALand content, the structure and coordination within and between different departments and levels of government, policy-making institutions, programs for implementation, and measures for assessment.At the international level, it has profound implications for our conception of multilateralism and the type, structure, and relationship between the complex array of international institutions established to foster peace and human security for "we the people."COVID-19 is a call for new ways of thinking, knowing, educating, decision-making, and practical execution of measures to promote human security for the common good.Humanity is called on today to change many things, but most of all, our understanding of the world we live in, our place in it, and our relationship to it and to one another.Within our learned societies group, we have incredibly active Ian Donald Inter-University School of Ultrasound.With its 142 schools worldwide, Donald School is the most dynamic educational platform in the world.Of course, endless pandemic strongly affected regular activities, which in 2019 had more than 11,000 participants worldwide.Board of the Donald School was, however, very active in organizing 16 webinars and the most actual topics in both developed and developing countries (Table 1).Thousands of participants took part in our distance learning program.We also did not stop producing new books within the Donald School library.The list of published books during the pandemic is listed in Table 2.However, we are very much aware that nobody on the ground can seriously predict the end of pandemics.Of course, we are missing our direct physical contact with participants very strongly.We understand that teaching is a transmission of enthusiasm and that the intellectual charm of the speakers in direct contact with participants cannot be replaced successfully.However, the reality is different.We will have to adapt to the new distance learning program within
Personalized Federated Learning with Gradient Similarity

In the conventional federated learning, the local models of multiple clients are trained independently by their privacy data, and the center server generates the shared global model by aggregating local models. However, the global model often fails to adapt to each client due to statistical heterogeneities, such as non-IID data. To address the problem, we propose the Subclass Personalized Federated Learning (SPFL) algorithm for non-IID data. In SPFL, the server uses the Softmax Normalized Gradient Similarity (SNGS) to weight the relationship between clients, and sends the personalized global model to each client. The stage strategy of ResNet is also applied to improve the performance of our algorithm. The experimental results show that the SPFL algorithm used on non-IID data outperforms the vanilla FedAvg, Per-FedAvg, FedUpdate, and pFedMe algorithms, improving the accuracy by 1.81∼18.46% on four datasets (CIFAR10, CIFAR100, MNIST, EMNIST), while still maintaining the state-of-the-art performance on IID data.