Reviewerly/exHarmony huggingface dataset:
A Novel System for Nighttime Vehicle Detection Based on Foveal Classifiers With Real-Time Performance

Vehicle monitoring using camera networks is an important task for traffic applications. Moreover, it becomes critical in nighttime, when the probability of an accident considerably increases as visibility conditions worsen. Typical approaches are mostly based on the assumption that regions delimiting vehicle lights are well defined, so that they are segmented and then associated to vehicle entities. However, this assumption fails in images acquired by existing traffic camera networks, where vehicle lights are revealed as flashes and other complex light patterns, occupying large and even disconnected image regions. In this work, a real-time vehicle detection algorithm for nighttime situations has been presented, which is able to locate vehicles in the image by analyzing the previous complex light patterns. For this purpose, a novel machine learning framework based on a grid of foveal classifiers has been designed. Every classifier in the grid processes the same global image descriptor (only one descriptor is computed per image). However, every one of them is trained to predict a different output depending on the classifier position in the grid and the vehicle ground-truth location. Additionally, only point-based annotations are required to train the grid of foveal classifiers, speeding up the cost of creating the required databases. Experimental results prove the effectiveness of the proposed method in a new created nighttime database with point-based annotations.
A European Emissions Trading System Powered by Distributed Ledger Technology: An Evaluation Framework

The European Union Emissions Trading System (EU ETS) is a major pillar of the European energy policy to reduce greenhouse gas emissions. However, the reportedly pervasive frauds in this market are constraining the beneficial role of the EU ETS. In this conceptual paper, we propose to digitalize the EU ETS by distributed ledger technology (DLT), enabling the verification of authenticity and provenance, proof of ownership, and lifecycle traceability of carbon certificates and assets. Our platform allows verifiable credentials to validate emission allowances, real-time tracking of trading participants’ emissions, and the audit trail reporting of the decentralized trading records. Furthermore, we complement the DLT application concept with a structured interdisciplinary evaluation framework. Our framework and analysis aim to stimulate further interdisciplinary research in this area to support regulators, such as the European Commission, in designing effective digital emissions trading systems.
Object recognition method based on logical correcting functions

The article discusses the logical method of object recognition and the construction of corrective functions. To construct a logical method for classifying objects, the method for constructing the optimal continuation of logical correcting functions is used. An algorithm for the optimal continuation of logical correcting functions is constructed and an estimate of the complexity of the constructed algorithm is calculated. A theorem is proved that the search algorithm for the minimum upper zero of a Boolean monotone function μ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">F</sub> (y <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> , y <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> , ..., y <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sub> ) is in one-to-one correspondence with the problem of finding a minimal set of variables that is essential for a function F(x <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> , x <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> , ..., x <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sub> ) that is not defined everywhere.
Dog whistling far-right code words: the case of ‘culture enricher' on the Swedish web

This paper uses the Swedish, once neo-Nazi expression culture enricher (Swedish: kulturberikare) as a case study to explore how covert and coded far-right discourse is mainstreamed, over time and across websites. A sample of 2,336 uses of the expression between 1999 and 2020 were analysed using critical discourse analysis. The findings illustrate how the expression works like a ‘dog whistle’ by enabling users to discretely self-identify with an imagined in-group of discontent white ‘Swedes’, while simultaneously showing opposition to the priorities of a generalised ‘establishment’. It shows how the expression is circulated in settings ranging from mainstream to far-right, and particularly, it highlights the potential role of semi-radical settings to act as gateways between mainstream and far-right ideas. Finally, the analysis shows evolving, ever more covertly hateful uses of the expression over time, illustrating the adaptability of far-right language online more generally, as a means to evade unwanted exposure by the far-right.
The Evolution of Language Models Applied to Emotion Analysis of Arabic Tweets

The field of natural language processing (NLP) has witnessed a boom in language representation models with the introduction of pretrained language models that are trained on massive textual data then used to fine-tune downstream NLP tasks. In this paper, we aim to study the evolution of language representation models by analyzing their effect on an under-researched NLP task: emotion analysis; for a low-resource language: Arabic. Most of the studies in the field of affect analysis focused on sentiment analysis, i.e., classifying text into valence (positive, negative, neutral) while few studies go further to analyze the finer grained emotional states (happiness, sadness, anger, etc.). Emotion analysis is a text classification problem that is tackled using machine learning techniques. Different language representation models have been used as features for these machine learning models to learn from. In this paper, we perform an empirical study on the evolution of language models, from the traditional term frequency–inverse document frequency (TF–IDF) to the more sophisticated word embedding word2vec, and finally the recent state-of-the-art pretrained language model, bidirectional encoder representations from transformers (BERT). We observe and analyze how the performance increases as we change the language model. We also investigate different BERT models for Arabic. We find that the best performance is achieved with the ArabicBERT large model, which is a BERT model trained on a large dataset of Arabic text. The increase in F1-score was significant +7–21%.
Operator support in human–robot collaborative environments using AI enhanced wearable devices

Nowadays, in order to cover the needs of market for product mass customization, industries have started to move to hybrid production cells, involving both robots and human operators. Research has been done during previous years to promote and improve the collaboration between humans and robots, trying to address topics such as safety, awareness and cognitive support in form of Augmented Reality based instructions. Results of previous research show bottlenecks related to the way of interaction of the operators with such supportive systems though. Direct interaction approach with the use of push buttons or indirect-gesture based interaction, which are most often adopted by the researchers, require operators to constantly occupy their hands performing the relevant button presses or gestures. Moreover, previous approaches are hardware dependent and need a lot of customization to work with different hardware. This work tries to address these bottlenecks proposing the usage of wearable devices enhanced with AI in order to support the interaction of human operators with robots in human-robot collaborative environments in a seamless and non-intrusive way, wrapped around a framework called "Operator Support Module" (OSM). Among others, OSM supports a variety of hardware to easily fit in various industrial scenarios. Two case studies will be presented to demonstrate the approach.
Cooperative LIDAR Object Detection via Feature Sharing in Deep Networks

The recent advancements in communication and computational systems have led to significant improvement of situational awareness in connected and autonomous vehicles. Computationally efficient neural networks and high speed wireless vehicular networks have been some of the main contributors to this improvement. However, scalability and reliability issues caused by inherent limitations of sensory and communication systems are still challenging problems. In this paper, we aim to mitigate the effects of these limitations by introducing the concept of feature sharing for cooperative object detection (FSCOD). In our proposed approach, a better understanding of the environment is achieved by sharing partially processed data between cooperative vehicles while maintaining a balance between computation and communication load. This approach is different from current methods of map sharing, or sharing of raw data which are not scalable. The performance of the proposed approach is verified through experiments on Volony dataset. It is shown that the proposed approach has significant performance superiority over the conventional single-vehicle object detection approaches.
WITHDRAWN: Real time data analysis of face mask detection and social distance measurement using Matlab

This article has been withdrawn: please see Elsevier Policy on Article Withdrawal (https://www.elsevier.com/about/our-business/policies/article-withdrawal). This article has been withdrawn as part of the withdrawal of the Proceedings of the International Conference on Emerging Trends in Materials Science, Technology and Engineering (ICMSTE2K21). Subsequent to acceptance of these Proceedings papers by the responsible Guest Editors, Dr S. Sakthivel, Dr S. Karthikeyan and Dr I. A. Palani, several serious concerns arose regarding the integrity and veracity of the conference organisation and peer-review process. After a thorough investigation, the peer-review process was confirmed to fall beneath the high standards expected by Materials Today: Proceedings. The veracity of the conference also remains subject to serious doubt and therefore the entire Proceedings has been withdrawn in order to correct the scholarly record.
Estimation of daily global solar radiation using empirical and machine-learning methods: A case study of five Moroccan locations

Reliable solar radiation data are essential to study the feasibility and to determine the optimal size of solar plants in a particular location. However, the network of solar radiation measurement stations is very weak around the world. This has led to the development of a wide variety of models and techniques for estimating solar radiation from commonly measured meteorological variables (Ambient air temperature, relative humidity, wind velocity …). In this work, 22 empirical models, Artificial Neural Networks (ANN) techniques, and tree-based ensemble methods were tested in estimating the daily global solar radiation (GSR) in five Moroccan locations. Each database was partitioned into two sets: the training set and the validation set. The training set is used to calibrate the models, while the validation set is used to assess their credibility. The best-performing model at each station was selected based on three statistical indictors, the coefficient of correlation (R), the normalized mean absolute error (nMAE) and the normalized root mean square error (nRMSE). The results on the validation dataset revealed that the Random Forest method (R: 87.53–96.20%; nMAE: 5.84–11.81%; nRMSE: 7.85–15.33%) outperformed the all tested models in terms of accuracy. The other machine learning methods achieved generally good performance (R: 81.73–95.14%; nMAE: 5.88–13.86%; nRMSE: 8.22–18%). Among the empirical models (R: 56.58–93.46%; nMAE: 6.96–21.83%; nRMSE: 9.89–26.96%), the Temperature - Geographic factors model (TG1) (R: 72.38–93.46%; nMAE: 6.96–17.94%; nRMSE: 9.89–22.39%) can be recommended for estimating the GSR for the all considered stations.
Multi-label feature selection with local discriminant model and label correlations

In multi-label learning, feature selection is an essential preprocessing module, which can be exploited a more compact and precise representation of instances. Most of existing multi-label feature selection methods are either converted into multiple single-label feature selection methods or directly utilize half-baked label information, thus it is difficult for them to obtain a discriminative feature subset across multiple labels. To tackle this problem, we propose multi-label feature selection with local discriminant model and label correlations. First, for each instance, a local clique comprising this instance and its neighboring instances is constructed, and a local discriminant model for each local clique is integrated globally to evaluate the clustering performance of all instances. Second, in terms of clustering results, we explore high-order label correlations to reduce the impact of half-baked label information. Finally, we combine l2,1-norm regularization to design the objective function to achieve multi-label feature selection. Comprehensive experiments are conducted on twelve real-world multi-label data sets, and results demonstrate the effectiveness of the proposed method in comparison with several representative methods.
Region Reinforcement Network With Topic Constraint for Image-Text Matching

Image and sentence matching has attracted increasing attention since it is associated with two important modalities of vision and language. Previous methods aim to find the latent correspondences between image regions and words by aggregating the similarities of the region-word pairs. However, these approaches consider little about the relationships of diverse regions in the image and treat the similarities of all region-word pairs equally. Moreover, focusing on fine-grained alignment overly, the true meaning of the original image will be likely distorted. In this paper, a novel Region Reinforcement Network with Topic Constraint (RRTC) is proposed to explore the correspondences between images and texts. Specifically, the region reinforcement network is built to infer fine-grained correspondence by considering the relationships of regions and re-assigning region-word similarities. Meanwhile, the topic constraint module is presented to summarize the central theme of images, which constrains the original image deviation. Extensive experimental results on MSCOCO and Flickr30k datasets verify the effectiveness of our proposed RRTC.
Ultrasound-Guided Robotic Navigation with Deep Reinforcement Learning

In this paper we introduce the first reinforcement learning (RL) based robotic navigation method which utilizes ultrasound (US) images as an input. Our approach combines state-of-the-art RL techniques, specifically deep Q-networks (DQN) with memory buffers and a binary classifier for deciding when to terminate the task.Our method is trained and evaluated on an in-house collected data-set of 34 volunteers and when compared to pure RL and supervised learning (SL) techniques, it performs substantially better, which highlights the suitability of RL navigation for US-guided procedures. When testing our proposed model, we obtained a 82.91% chance of navigating correctly to the sacrum from 165 different starting positions on 5 different unseen simulated environments.
Toward Agile Maneuvers in Highly Constrained Spaces: Learning From Hallucination

While classical approaches to autonomous robot navigation currently enable operation in certain environments, they break down in tightly constrained spaces, e.g., where the robot needs to engage in agile maneuvers to squeeze between obstacles. Recent machine learning techniques have the potential to address this shortcoming, but existing approaches require vast amounts of navigation experience for training, during which the robot must operate in close proximity to obstacles and risk collision. In this letter, we propose to side-step this requirement by introducing a new machine learning paradigm for autonomous navigation called learning from hallucination (LfH), which can use training data collected in completely safe environments to compute navigation controllers that result in fast, smooth, and safe navigation in highly constrained environments. Our experimental results show that the proposed LfH system outperforms three autonomous navigation baselines on a real robot and generalizes well to unseen environments, including those based on both classical and machine learning techniques.
Geometric-Pixel Guided Single-Pass Convolution Neural Network With Graph Cut for Image Dehazing

One of the major shortcomings of existing image dehazing algorithms is in estimating scene transmittance, which has assumed many items in the existing algorithms. One key assumption has been pixel uniformity and smoothness. In this paper, we propose to solve the dehazing problem using a combination of single-pass CNN with graph cut algorithms. It considers the transmittance based on differential pixel-based variance, local and global patches and energy functions to improve the transmission map. The proposed algorithm was tested on different images and evaluated based on various evaluation metrics. Our results show more details when compared to four existing benchmark enhancement methods. The proposed method has one major drawback: the over-bright areas tend to lose some features in the final image.
Collision-Free Distributed Multi-Target Tracking Using Teams of Mobile Robots with Localization Uncertainty

Accurately tracking dynamic targets relies on robots accounting for uncertainties in their own states to share information and maintain safety. The problem becomes even more challenging when there is an unknown and time-varying number of targets in the environment. In this paper we address this problem by introducing four new distributed algorithms that allow large teams of robots to: i) run the prediction and ii) update steps of a distributed recursive Bayesian multi- target tracker, iii) determine the set of local neighbors that must exchange data, and iv) exchange data in a consistent manner. All of these algorithms account for a bounded level of localization uncertainty in the robots by leveraging our recent introduction of the convex uncertainty Voronoi (CUV) diagram, which extends the traditional Voronoi diagram to account for localization uncertainty. The CUV diagram introduces a tessellation over the environment, which we use in this work both to distribute the multi-target tracker and to make control decisions about where to search next. We examine the efficacy of our method via a series of simulations and compare them to our previous work which assumed perfect localization.
A Blockchain-Based Dual-Side Privacy-Preserving Multiparty Computation Scheme for Edge-Enabled Smart Grid

Unlike a traditional centralized and producer-controlled power grid, the smart grid is a more complicated distributed power system consisted of many resources and applications. In smart grid, huge amounts of data generated by edge devices are collected by different parties. To achieve high operation efficiency, it is important to enable the data sharing and cooperative computation among different parties. How to protect the security and privacy of the utility data and the identities of their owners has become a major concern. There have been some studies on this issue. However, most of these works failed to consider the privacy protection in the dual sides of the data owner and receiver. In this article, we propose BPM4SG, a blockchain-based dual-side privacy-preserving multiparty computation (MPC) scheme for edge-enabled smart grid. In BPM4SG, the data segmentation method is adopted to ensure the security of MPC (e.g., summation) in edge nodes. The consortium blockchain and smart contract are used to further increase the system security and avoid the dependency on trusted third parties. Additionally, a data obfuscation method based on the ring signatures and a new one-time address scheme are proposed to protect the privacy of both the data owner and data receiver. The analysis shows that BPM4SG can meet the security and privacy requirements of smart grid. The experimental evaluation results demonstrate that our scheme has a better performance compared with other popular schemes.
LaND: Learning to Navigate From Disengagements

Consistently testing autonomous mobile robots in real world scenarios is a necessary aspect of developing autonomous navigation systems. Each time the human safety monitor disengages the robot's autonomy system due to the robot performing an undesirable maneuver, the autonomy developers gain insight into how to improve the autonomy system. However, we believe that these disengagements not only show where the system fails, which is useful for troubleshooting, but also provide a direct learning signal by which the robot can learn to navigate. We present a reinforcement learning approach for learning to navigate from disengagements, or LaND. LaND learns a neural network model that predicts which actions lead to disengagements given the current sensory observation, and then at test time plans and executes actions that avoid disengagements. Our results demonstrate LaND can successfully learn to navigate in diverse, real world sidewalk environments, outperforming both imitation learning and reinforcement learning approaches. Videos, code, and other material are available on our website https://sites.google.com/view/sidewalk-learning.
Supervised feature selection using integration of densest subgraph finding with floating forward–backward search

In this paper, a novel approach of supervised feature selection is proposed based on the principle of dense subgraph discovery. To exploit dense subgraph discovery for the purpose of feature selection, the dataset is initially mapped to an equivalent weighted graph notation by considering the set of all features as its vertex set and the mutual dependency between each pair of features as the weight of the corresponding edge. The proposed feature selection algorithm proceeds in a two-phase manner. In the first stage, a dense sub-graph is first discovered so that the features within it become maximally non-redundant among each other and the averaged class relevance as well as averaged standard deviation of all these features are obtained as maximal as possible. In this regard, a novel induced degree is also defined for each feature by incorporating the aforesaid three important objectives of feature selection. In this phase, a modified version of an existing approximation algorithm is also used to find dense subgraph module. Finally, in the second stage, a floating forward–backward search is performed on the dense subgraph so obtained to reveal a better feature subset. In both stages, an existing version of the normalized mutual information score is employed to compute both the class relevance and redundancy. The main contribution of this paper is proposing a feature selection strategy by which the reduced features have the characteristics like maximal average class relevance, minimal average pairwise redundancy, and good discriminating power. The experimental results demonstrate that the proposed approach is competent with several conventional as well as state-of-art algorithms of supervised feature selection.