Reviewerly/exHarmony huggingface dataset:
Few-shot object detection model based on meta-learning for UAV

In image-based object detection, traditional deep learning algorithms have made some achievements. However, the algorithm has a large amount of interaction with the environment and high sample complexity when solving tasks, so the convergence of the algorithm is difficult. At present, it is difficult to collect UAV aerial photography target data. To solve this problem, a few-shot object detection model based on meta-learning is proposed by referring to the idea of meta-learning. Based on the Faster R-CNN algorithm, the MAML algorithm of the meta-learning is introduced, and its RPN structure is improved. The proposed model includes training phase and testing phase. In the training stage, the object detection network is trained with the training dataset formed by the combination of public data sets, so that the network can learn the meta-knowledge of object detection. In the test phase, the model is fine-tuned with the test dataset to enable the network to detect new classes of targets. The results show that the effect of the model based on the few-shot meta-learning algorithm is better than that of the classical algorithm such as Faster R-CNN, which can realize the accuracy of the UAV in the case of few-shot data training and verify the effectiveness and advanced nature of the model.
Profiling hate speech spreaders on Twitter using BERT pre-trained model and neural network

The profiling hate speech readers task is to determine whether the author of a given group of English or Spanish tweets posted on twitter spreads hate speech. At present, the automatic recognition of hate, irony and false speech is the top priority in the field of author profiling, which has practical significance. This paper proposes a deep learning model based on Bert pre-trained model to extract deep text semantic features, and use counting method to obtain stylistic features. Finally, hate speech profiling is regarded as a binary classification task, and full connected neural network is used for classification prediction. The accuracy is 71.5% on the English data set and 78% on the Spanish test set.
Machine unlearning survey

Many online platforms have widely deployed machine learning models as a service. Many of these applications require users to upload their data for model training, but it also induces privacy risks. Once the user wants to leave the application, how to make the application unlearn the uploaded data, which is called machine unlearning, is worthy of study. In this article, we provide a survey of machine unlearning with an approximate and exact guarantee. We summarize the existing machine unlearning approaches and discuss their merits and drawbacks in this field.
Comprehensive assessment of distribution network equipment assets quality based on full life cycle

The comprehensive quality evaluation of distribution network equipment assets in the whole life cycle plays an important role in the development of distribution network enterprises. The quality evaluation of individual equipment is not representative, and it is not feasible to evaluate each piece of equipment. So, in this paper, the average quality level of similar distribution network equipment is evaluated from four aspects, procurement, installation, operation, and obsolescence, to make the evaluation results more representative and help distribution network enterprises to master the status of their equipment assets more efficiently. Under the evaluation index system, the analytic hierarchy process, the entropy weight method, and the least square method are used to get the comprehensive weight composed of the subjective weight and objective weight mixing. Then, by the fuzzy comprehensive evaluation method, the comprehensive weight is used to evaluate similar equipment in different regions and different equipment in the same region. Finally, the case analysis is carried out with the data of distribution cables and distribution transformers in H, Z, and J provinces to verify the feasibility and effectiveness of the proposed index evaluation system and evaluation model.
Federated Knowledge Transfer for Heterogeneous Visual Models

Federated learning (FL) is a privacy-preserving distributed learning paradigm that enables collaborative training of machine learning models among multiple participants. However, despite recent progress, existing federated learning systems can still not handle heterogeneous models. For instance, candidate clients with heterogeneous models are inaccessible to the established federated system. And within the federated system, local models are forbidden to be updated to become heterogeneous models, even though the updated models work better.
On the Robustness of 3D Object Detectors

In recent years, significant progress has been achieved for 3D object detection on point clouds thanks to the advances in 3D data collection and deep learning techniques. Nevertheless, 3D scenes exhibit a lot of variations and are prone to sensor inaccuracies as well as information loss during pre-processing. Thus, it is crucial to design techniques that are robust against these variations. This requires a detailed analysis and understanding of the effect of such variations. This work aims to analyze and benchmark popular point-based 3D object detectors against several data corruptions. To the best of our knowledge, we are the first to investigate the robustness of point-based 3D object detectors. To this end, we design and evaluate corruptions that involve data addition, reduction, and alteration. We further study the robustness of different modules against local and global variations. Our experimental results reveal several intriguing findings. For instance, we show that methods that integrate Transformers at a patch or object level lead to increased robustness, compared to using Transformers at the point level. The code is available at https://github.com/sultanabughazal/robustness3d.
Multimodal Fusion with Cross-Modal Attention for Action Recognition in Still Images

We propose a cross-modal attention module to combine information from different cues and different modalities, to achieve action recognition in still images. Feature maps are extracted from the entire image, the detected human bounding box, and the detected human skeleton, respectively. Inspired by the transformer structure, we design the processing between the query vector from one cue/modality, and the key vector from another cue/modality. Feature maps from different cues/modalities are cross-referred so that better representations can be obtained to yield better performance. We show that the proposed framework outperforms the state-of-the-art systems without the requirement of an extra training dataset. We also conduct ablation studies to investigate how different settings impact the final results.
TFM a Dataset for Detection and Recognition of Masked Faces in the Wild

Droplet transmission is one of the leading causes of the spread of respiratory infections, such as coronavirus disease (COVID-19). The proper use of face masks is an effective way to prevent the transmission of such diseases. Nonetheless, different types of masks provide various degrees of protection. Hence, automatic recognition of face mask types may benefit the control access to facilities where a specific protection degree is required. In the last two years, several deep learning models have been proposed for face mask detection and properly wearing mask recognition. However, the current publicly available datasets do not consider the different mask types and occasionally lack real-world elements needed to train robust models. In this paper, we introduce a new dataset named TFM with sufficient size and variety to train and evaluate deep learning models for face mask detection and recognition. This dataset contains more than 135,000 annotated faces from about 100,000 photographs taken in the wild. We consider four mask types (cloth, respirators, surgical and valved) as well as unmasked faces, of which up to six can appear in a single image. The photographs were mined from Twitter within two years since the beginning of the COVID-19 pandemic. Thus, they include diverse scenes with real-world variations in background and illumination. With our dataset, the performance of four state-of-the-art object detection models is evaluated. The experimental results show that YOLOv5 can achieve about 90% of [email protected], demonstrating that the TFM dataset can be used to train robust models and may help the community step forward in detecting and recognizing masked faces in the wild. Our dataset and pre-trained models used in the evaluation will be available upon the publication of this paper.
A Priority-based Modified A<sup>∗</sup> Path Planning Algorithm for Multi-Mobile Robot Navigation

Automated storage and retrieval systems (ASRS) are designed for automated storage and retrieval of parts and items in specific locations within manufacturing, warehouses, institutions, and others. These systems are usually comprised of predefined locations where machines/robots can follow established routes to get items. The Path planning belongs to shortest path problem. For these robots, it is important to find the shortest path, without causing any conflict among them. Several algorithms exist to find shortest path, notably Dijkstra and A*, which are famous to solve this kind of problem. Additionally, a preference is recommended in such environment for the most important robotic task among these robots. The preferred robot will be selected as the one with highest priority to accomplish the task. In this work, we suggested a simple and efficient algorithm based on A* algorithm to find the path planning with collision-free for these robots with priority among these machines, so that to have one machine to be of higher priority than all of the others robots. We proposed two priority algorithms A1 and A2. We also did simulation results to show the efficiency of the proposed priority algorithms. The new priority algorithm showed to be very effective with both A1 and A2 algorithms.
OSS-AQM: An Open-Source Software Quality Model for Automated Quality Measurement

Open-source software is growing in popularity today and has been used as a software tool or as a component in software development within organizations. Selecting quality open-source software is a challenging task. Several open-source quality models have been proposed to assess open-source software quality, but the assessment is rather limited because it is often subjective, relies on heavy user intervention, and requires information from different sources. To complement and enhance existing approaches to open-source quality assessment, this paper proposes a new open-source software quality model called OSS-AQM that aims at automating the measurement of open-source software quality. The OSS-AQM provides a set of quality metrics and an automation tool that can retrieve information about the open-source software from GitHub, source code, SonarQube, and Stack Exchange, and quantitatively determine the overall quality of the open-source software. In an experiment, the OSS-AQM is compared with an existing open-source software quality model to show how the proposed OSS-AQM and its tool can enhance open-source software comparison and facilitate open-source software selection.
Quantum board games

Is there a way to guarantee that you beat your family at games? There sure is – if you tap into the quantum world, says Philip Ball
An Authentication-Oriented Approach to Model the Crosscutting Constraints in Sequence Diagram Using Aspect OCL

The modeling of software functional requirements is very important in the development of software systems. In addition, it is also highly important to model security requirements, specifically authentication requirements, to prevent security risks and threats at the design level. The authentication requirements impose various constraints on granting access to only legitimate users to use computer resources. These constraints play an important role in the modeling of functional and authentication concerns of software systems. However, due to their crosscutting nature, their modeling results in pervasiveness across several design models. These constraints crosscut other constraints, which raises the problems of tangling and scattering. As a result, it is highly difficult to manage and maintain the constraints put on design models. Moreover, taking ad hoc approaches to deal with these constraints in complex systems is likely to result in faulty design models. All the existing approaches only deal with crosscutting behavior and completely ignore the crosscutting constraints. Therefore, our main research objective is to improve the modeling of crosscutting constraints and remove the scattering and tangling problems. For this purpose, we propose an authentication-oriented approach to modeling these constraints using the aspect-oriented technique. Using this approach, two case studies are implemented. Finally, the results show that the tangling and scattering problems are eliminated by separately modeling the crosscutting constraints as aspects using aspect OCL. This reduces the overall number of constraints and lowers the maintenance and management effort, which ultimately reduces the complexity of sequence models. The constraint-oriented sequence models are successfully verified, which shows that the output models are correct and complete. To conclude, our research approach is more useful and effective than the other approaches as it covers all the necessary steps required for functional and authentication behavior and constraint modeling.
Recent Results on Some Word Oriented Stream Ciphers: SNOW 1.0, SNOW 2.0 and SNOW 3G

In this chapter, we have studied three word-oriented stream ciphers SNOW 1.0, SNOW 2.0 and SNOW 3G in a detailed way. The detailed description includes the working principles of each cipher, security vulnerabilities and implementation issues. It also helps us to study the challenges in each cipher. As SNOW 3G is used as a confidentiality and integrity component in 3G, 4G and 5G communications, the after study of this article may instigate the reader to find the fixes from different cryptanalysis and also find a new suitable design in Mobile telephony security.
Analysis of parenting informational needs for mothers with infants and toddlers using text-mining

Due to its accessibility and anonymity, the Internet is a common way for mothers to find information about parenting concerns. The purpose of this study was to explore the parenting questions posted by mothers on parenting online communities to identify the informational needs of parents with young children. We analyzed 31,565 parenting–related questions posted in October 2019. Text mining was performed using Latent Dirichlet Allocation (LDA) to identify the most common topics in the dataset. Topics were sorted according to infant age group and a keyword network analysis was performed to identify parenting difficulties. In the LDA, 15 topics were found to be the best-fitting model. From these, we identified parenting situations that lead to many questions, the majority of which were focused on basic childcare related to feeding, baby food, sleeping, medical problems, development, and learning. The distribution of the 15 topics varies according to age group. Keyword network analysis showed that mothers had difficulty putting their babies to sleep and stopping them from crying. This study provides useful information to support parenting and makes suggestions for how parenting programs can meet mothers’ needs.
An Algorithm for Single View Occlusion Area Detection in Binocular Stereo Matching

Occlusion area detection is a crucial step affecting the performance of the binocular stereo matching algorithm, but the traditional method of occlusion area detection has two major problems, including left–right consistency detection (LRC). First, these algorithms must obtain the left and right disparity maps with precision. Second, these algorithms cannot detect the occlusion region at the image’s borders. We propose the single view occlusion area detective (SVOAD) algorithm to detect these occlusion areas and better deal with them. The SVOAD can detect the area of occlusion from a single image, thereby reducing the computational cost. Additionally, the algorithm can detect the occlusion region in all image regions. This paper also improves the guided filter so that it works better with the end-to-end neural network and makes the SVOAD algorithm work better.
RGB-D salient object ranking based on depth stack and truth stack for complex indoor scenes

RGB-D salient object detection has achieved a great development in recent years due to its extensive applications. Previous studies mainly focus on simple scene images with one single object. These models usually become overwhelmed by complex scenes with multiple objects. Moreover, these methods model salient object detection as a binary segmentation problem. However, psychology studies show that humans shift their visual attention from one object to another and rank salient objects, especially in complex indoor scenes. Following the psychological studies, we propose to rank salient objects in RGB-D images of complex indoor scenes. Due to the lack of such data, we first construct a RGB-D salient object ranking dataset containing complex indoor scenes with multiple objects. The saliency ranking of different objects is defined based on the order that an observer notices these objects. The final salient object ranking result is an average across the saliency rankings of 13 observers. This RGB-D salient object ranking dataset is also analyzed with current mainstream RGB-D salient object detection dataset for comparison. Since location information provided by depth images can help to determine the saliency ranking of objects, we further propose an end-to-end network exploiting depth stack and ground truth stack to predict the order of salient objects in complex scenes. The quantitative and qualitative comparisons demonstrate the effectiveness of the proposed method.
Trajectory Data Publication Based on Differential Privacy

Analyzing trajectory data can provide people with a higher quality of life. However, publishing trajectory data directly will leak privacy. The authors propose a trajectory data publication method based on differential privacy (TDDP). TDDP method consists of two stages. In the location generalization stage, firstly, the locations at each timestamp are clustered into classes by k-means++ algorithm, and then the representative location of each class is selected by using the exponential mechanism. In the generalized trajectory data publication stage, the authors design a sampling mechanism to form the generalized trajectories. The locations are sampled from the representative locations under different timestamps to form the generalized trajectories. The TDDP method can avoid the generation of non-semantic representative locations and ensure that the generalized trajectories can resist filtering attacks. The experimental results show that the trajectory data released by TDDP method can achieve a good balance between privacy protection and data availability.
Tunable presynaptic weighting in optoelectronic spiking neurons built with laser-coupled resonant tunneling diodes

Abstract Optoelectronic artificial spiking neurons are regarded as promising core elements for novel photonic neuromorphic computing hardware. In this work, we investigate a modular optoelectronic spiking neuron built with an excitable resonant tunneling diode (RTD) coupled to a photodetector and a vertical-cavity surface-emitting laser (VCSEL). This work provides the first experimental demonstration of amplitude control of the fired optical spikes in the electrical-to-optical part of the artificial neuron, therefore introducing a simple way of weighting of the presynaptic spikes. This is achieved by tuning the VCSEL bias current, hence providing a straightforward, high-speed, hardware-friendly option for the weighting of optical spiking signals. Furthermore, we validate the feasibility of this layout using a simulation of a monolithically integrated, RTD-based nanoscale optoelectronic spiking neuron model, which confirms the system’s capability to deliver weighted optical spiking signals at GHz firing rates. These results demonstrate a high degree of flexibility of RTD-based artificial optoelectronic spiking neurons and highlight their potential towards compact, high-speed photonic spiking neural networks and light-enabled neuromorphic hardware.
A Path Planning Method for Autonomous Vehicles Based on Risk Assessment

In order to meet the requirements of vehicle automatic obstacle avoidance, a lane change trajectory planning method is proposed to meet the requirements of safety, comfort, and lane change efficiency. Firstly, the potential collision points that may exist are analyzed using information about surrounding vehicle movement and the road. Then, the safe lane change range for vehicles is obtained. Secondly, the control points of the fifth order Bézier curve are constrained to generate a series of path clusters in the optimal range. At the same time, the driver’s style and reaction time are taken into account in the risk assessment stage of the route using the improved artificial potential field method. Finally, the optimal path is selected by comprehensively considering lane-changing efficiency and comfort. In order to further verify the accuracy of the algorithm, real-vehicle experiments have been carried out on the autonomous vehicle platform. Under different driving styles, the vehicle can avoid obstacles perfectly while ensuring the smoothness of the path. Simulation and real-vehicle experiment results show that the proposed algorithm can provide an excellent solution for autonomous vehicles for lane changing and obstacle avoidance.
FA-RCNet: A Fused Feature Attention Network for Relationship Classification

Relation extraction is an important task in natural language processing. It plays an integral role in intelligent question-and-answer systems, semantic search, and knowledge graph work. For this task, previous studies have demonstrated the effectiveness of convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory networks (LSTMs) in relational classification tasks. Recently, due to the superior performance of the pre-trained model BERT, BERT has become a feature extraction module for many relational classification models, and good results have been achieved in work related to BERT. However, most of such work uses the deepest levels of features. The important role of shallow-level information in the relational classification task is ignored. Based on the above problems, a relationship classification network FA-RCNet (fusion-attention relationship classification network) with feature fusion and attention mechanism is proposed in this paper. FA-RCNet fuses shallow-level features with deep-level features, and augments entity features and global features by the attention module so that the feature vector can perform the relational classification task more perfectly. In addition, the model in this paper achieves advanced results on both the SemEval-2010 Task 8 dataset and the KBP37 dataset compared to previously published models.