Reviewerly/exHarmony huggingface dataset:
FRIDA: Fisheye Re-Identification Dataset with Annotations

Person re-identification (PRID) from side-mounted rectilinear-lens cameras is a well-studied problem. On the other hand, PRID from overhead fisheye cameras is new and largely unstudied, primarily due to the lack of suitable image datasets. To fill this void, we introduce the “Fisheye Re-IDentification Dataset with Annotations” (FRIDA) <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> , with 240k+ bounding-box annotations of people, captured by 3 time-synchronized, ceiling-mounted fisheye cameras in a large indoor space. Due to a field-of-view overlap, PRID in this case differs from a typical PRID problem, which we discuss in depth. We also evaluate the performance of 10 state-of-the-art PRID algorithms on FRIDA. We show that for 6 CNN-based algorithms, training on FRIDA boosts the performance by up to 11.64% points in mAP compared to training on a common rectilinear-camera PRID dataset. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> vip. bu.edu/frida
IPChain: Blockchain-Based Security Protocol for IoT Address Management Servers in Smart Homes

The dynamic host configuration protocol (DHCP) servers are forms of an Internet of Things (IoT) address management server (IoTAMS) that gives network configuration settings to newly connected hosts. Administrators of a network may save time by setting DHCP servers instead of every network node. However, the absence of a more robust authentication method for DHCP servers makes hosts susceptible to attacks since neither the server nor the users are able to check the other’s authenticity during DHCP connections. These concerns result in both internal and external threats to the system that have the potential to impair network services. Among these threats are malicious DHCP servers and DHCP starvation. This paper aims to provide a novel approach for tackling these issues and protect the DHCP protocol. The proposed model uses the Diffie–Hellman key exchange mechanism, the elliptic curve discrete logarithm problem (ECDLP), a one-way hash function, blockchain technology, and a smart contract. In addition, registration and validation processes provide support for the proposed model in combating DHCP risks for both internal and external system threats. Results from this study show that the proposed model has an average of 21.1% more resistance to a growing number of adversaries than the benchmark models, thus revealing that the model is better suited for the security of IoT address management servers in smart homes, thereby enhancing resilience against related threats and the success of IP address management.
Hand-raising gesture detection in classroom with spatial context augmentation and dilated convolution

Behavior is a manner to express one's intent, and it is a hotspot and tough problem under discussion in the field of computer vision. This research mainly focuses on behavior in educational scenes, with hand-raising being the most common in classrooms. The behavior of students raising their hands can be used to assess a student's involvement, teaching quality, classroom atmosphere and so on. To detect student's hand-raising in real classroom, we build a hand-raising dataset, and improve the baseline by boosting feature expression. First, we analyze the drawback of multi-scale feature fusion part in the baseline. Considering that the information loss is due to the reduction in the number of channels during feature pyramid construction, Spatial Context Augmentation (SCA) is applied to mitigate feature map information loss at the highest level. Then, when raising the hand, there is a spatial relationship between the head and the hand. Based on this breakthrough, we propose Multi-Branch Dilated Convolution (MBDC) to enlarge the receptive field and reduce false detection. Extensive experimental results on the hand-raising dataset show that our strategy outperforms a number of state-of-the-art approaches. In addition, our algorithm is also tested on PASCAL VOC, Cityscapes dataset. The results outperform other algorithms on both datasets. This also demonstrates the universality of our algorithm.
Multiscale echo self-attention memory network for multivariate time series classification

Recently, ESN has been applied to time series classification own to its high-dimensional random projection ability and training efficiency characteristic. The major drawback of applying ESN to time series classification is that ESN cannot capture long-term dependency information well. Therefore, the Multiscale Echo Self-Attention Memory Network (MESAMN) is proposed to address this issue. Specifically, the MESAMN consists of a memory encoder and a memory learner. In the memory encoder, multiple differently initialized ESNs are utilized for high-dimensional projection which is then followed by a self-attention mechanism to capture the long-term dependent features. A multiscale convolutional neural network is developed as the memory learner to learn local features using features extracted by the memory encoder. Experimental results show that the proposed MESAMN yields better performance on 18 multivariate time series classification tasks as well as three 3D skeleton-based action recognition tasks compared to existing models. Furthermore, the capacity for capturing long-term dependencies of the MESAMN is verified empirically.
A Blockchain-Based Privacy Sensitive Data Acquisition Scheme During Pandemic Through the Facilitation of Federated Learning

New diseases (e.g., monkeypox) are showing up and taking the form of a pandemic within a short time. Early detection can assist in reducing the spread. However, because of privacy-sensitive data, users do not share it continually. Thus, it becomes challenging to employ modern technologies (e.g., deep learning). Moreover, cyber threats encircle both communication and data. This paper introduces a blockchain-based data acquisition scheme during the pandemic in which federated learning (FL) is employed to assemble privacy-sensitive data as a form of the trained model instead of raw data. A secure training scheme is designed to mitigate cyber threats (e.g., man-in-the-middle-attack). An experimental environment is formulated based on a recent pandemic (i.e., monkeypox) to illustrate the feasibility of the proposed scheme.
An automatic sediment-facies classification approach using machine learning and feature engineering

Abstract The delineation of sediment facies provides essential background information for a broad range of investigations in geosciences but is often constrained in quality or quantity. Here we leverage improvements in machine learning and X-ray fluorescence core scanning to develop an improved approach to automatic sediment-facies classification. This approach was developed and tested on a regional-scale high-resolution elemental dataset from sediment cores covering various sediment facies typical for the southern North Sea tidal flat, Germany. We use a machine-learning-built classification model involving simple but powerful feature engineering to simulate the observational behavior of sedimentologists and find that approach has 78% accuracy, followed by error analysis. The model classifies the majority of sediment facies and also, importantly, highlights critical sections for further investigation. Research resources can thus be allocated more efficiently. We suggest that our approach could provide a generalizable blueprint that can be applied and adapted for the research question and data type at hand.
Fusion-based high-quality polarization 3D reconstruction

The fusion of shape from polarization (SFP) and depth sensors is an effective method for high-quality 3D reconstruction. However, accurate registration of images from different sensors is a difficult problem. In this paper, we propose a 3D reconstruction method based on the fusion of SFP and polarization-modulated ranging (PMR) for the first time, where only a single image sensor is utilized to acquire both polarized images and depth data. Therefore, the challenging image registration problem can be avoided. This method is based on the following observation: SFP can retrieve inaccurate objects’ 3D shapes with fine textures, whereas PMR can provide coarse but accurate absolute depths, resulting in a good necessity of fusing these two modalities. To this end, we propose two fusion models: a joint azimuth estimation model to obtain a fused azimuth angle with π−ambiguity corrected, and a joint zenith estimation model to estimate an accurate fused zenith angle. Finally, the reconstructed depth is integrated from the fused azimuth and zenith angles, which can provide accurate absolute depth with fine textures. Extensive experiments have been conducted to verify the efficiency of the proposed method over other state-of-the-art methods besides its robustness to the targets with different kinds of features, demonstrating its broad application prospects in 3D reconstruction.
A deep encoder-decoder network for anomaly detection in driving trajectory behavior under spatio-temporal context

Traditional trajectory anomaly detection aims to find abnormal trajectory points or sequences using data mining techniques. As a comparison, we focus on the evaluation of the anomalies of driving habits for different drivers based on their trajectory data. This is particularly important for the application of adjusting the amount of insurance in accordance with the driving behaviors. Instead of customizing rules for modeling various driving behaviors, we propose an end-to-end deep learning framework for driving trajectory anomaly detection, called STDTB-AD. Specifically, taking into account the fact that movement is spatial–temporal dependent, the study first partitions the whole road network into a series of spatial–temporal units, which have homogeneous properties of traffic flows. Then, the motion parameters (i.e., acceleration, speed, and direction) of driving trajectories falling within each spatio-temporal unit are calculated for representing context-aware features of drivers. This method is able to detect the deviation of movement from the normal traffic state on the spatial–temporal units. Finally, a variational autoencoder is utilized to quantify the abnormity degree of each driver according to the reconstruction probability of driving feature vector. Evaluations based on taxi trajectory data show that our model can consider both the spatial and temporal contexts for detecting driving behavior anomalies and achieve higher detection accuracy than traditional models based on either spatial or temporal context.
Multi-scale interaction transformer for temporal action proposal generation

Temporal action proposal generation is to localize the time intervals with actions in untrimmed videos. Action instances in untrimmed videos have dramatically varied temporal scales which brings about great challenges for temporal action proposal generation. While temporal action proposal generation has achieved tremendous progress over the past years, multi-scale issue in action proposal generation is still an open problem. In this paper, we propose a Multi-scale Interaction Transformer (MSIT) architecture, which adopts a directly set prediction method to work out the temporal action proposal generation task. MSIT constructs multi-scale feature pyramids and incorporates a novel multi-scale mechanism into Transformer framework. A customized top-down interaction structure is designed to perform self-scale attention and cross-scale attention at different levels of the feature pyramids. Through the top-down interaction, the semantic and location information in each feature level is strengthened and therefore the proposal generation performance can be improved. Furthermore, to model the accurate action locations for each frame, we incorporate an actionness prediction structure to constrain the features output from the encoder. The proposed method was tested on two challenging datasets: THUMOS14 and ActivityNet-1.3. Experiments show that our method achieves comparable performance with state-of-the-art methods. Extensive studies and visualizations also demonstrate the strength of our method.
No need to forget, just keep the balance: Hebbian neural networks for statistical learning

Language processing in humans has long been proposed to rely on sophisticated learning abilities including statistical learning. Endress and Johnson (E&J, 2021) recently presented a neural network model for statistical learning based on Hebbian learning principles. This model accounts for word segmentation tasks, one primary paradigm in statistical learning. In this discussion paper we review this model and compare it with the Hebbian model previously presented by Tovar and Westermann (T&W, 2017a; 2017b; 2018) that has accounted for serial reaction time tasks, cross-situational learning, and categorization paradigms, all relevant in the study of statistical learning. We discuss the similarities and differences between both models, and their key findings. From our analysis, we question the concept of "forgetting" in the model of E&J and their suggestion of considering forgetting as the critical ingredient for successful statistical learning. We instead suggest that a set of simple but well-balanced mechanisms including spreading activation, activation persistence, and synaptic weight decay, all based on biologically grounded principles, allow modeling statistical learning in Hebbian neural networks, as demonstrated in the T&W model which successfully covers learning of nonadjacent dependencies and accounts for differences between typical and atypical populations, both aspects that have not been fully demonstrated in the E&J model. We outline the main computational and theoretical differences between the E&J and T&W approaches, present new simulation results, and discuss implications for the development of a computational cognitive theory of statistical learning.
A constructive deep convolutional network model for analyzing video-to-image sequences

This paper proposes a new technique to add sign language as a special and real-time feature. In addition to gestures, sign language also uses grammatical rules, linguistic frameworks, etc. We require a method that can extract meaningful sentences from non-manual features. Utilizing techniques from computer graphics, neurologic music therapy, and NN-based image/video formation, this is accomplished. Our goal is to use this to process dynamic images for output generation and real-time classification. Current CNN-based techniques operate by taking the entire video as input, dividing it into layers for the classifier to work on, and then combining and providing the output to the user. Here, Convolutional Deep VGG-16 (CDVGG-16) classifiers adopted for sign feature learning, which is iteratively trained and tested. Their architecture consists of blocks, where each block is composed of 2D Convolution and Max Pooling layers. We prefer VGG-16 over VGG-19 in order to improve feature extraction and decrease overfitting. The captured sign language video processing with some pre-processing and classification steps leads to the extraction of sign language feature space assisting deaf people. As a positive approach, the Sign Language Recognition (SLR) modeling framework with deep learning is introduced as it includes various notable advantages. There are three essential phases for provisioning the SLR framework: (1) data acquisition, (2) image pre-processing, and (3) CDVGG-16 classifier. The simulation is done with the MATLAB 2020a environment with various performance metrics like accuracy, precision, F-measure, recall and running time. The promising results show that real-world SLR applications can be formed using the suggested SLR paradigm. The proposed CDVGG-16 gives 95.6% prediction accuracy, 85.6% precision, 99.9% recall, 96.8% F-measure and 0.125 s running time. For improved performance, our proposed method employs normalization and examines sequential image frames. The results and accuracy indicate that it is a suitable method for video-image classification, as expected, and that deep layers and data augmentation can be used to address any overfitting and underfitting issues. Instead of being a literal translation frame by frame, the filter size is adjusted to ensure that output is translated appropriately given the context.
Geological mapping and structural interpretation of the <scp>Dschang‐Santchou‐escarpment</scp> (West, Cameroon), using Landsat 8 <scp>OLI</scp>/<scp>TIRS</scp> sensors/<scp>SRTM</scp> and field observations

Geological mapping of the Dschang‐Santchou‐escarpment rocks in the Western Cameroon region was done using a combination of Landsat 8 OLI/TIRS, SRTM images acquired by satellites on January 21, 2021, and field observations. Lineaments were pre‐processing and processing, performed with Envi 5.1. The different methods of digital processing applied, such as the principal component analysis, the coloured compositions and the combination of bands, show a lithological differentiation of the geological formations of the study area. Lineament maps were obtained by applying Sobel directional filters where 496 lineaments were manually extracted. These lineaments display two main trending directions: the NW–SE trend corresponding to the S 2 metamorphic foliation direction and the NE–SW trend corresponding to the S 3 magmatic foliation direction. The set of these lineaments represents the fracture network in the area. The field observations as well as the lineaments mapping obtained, allowed us to confirm with the help of the fracture network that we are indeed in an escarped environment. Remote sensing data constitute thus a useful tool for geological mapping and structural reconstitutions in the escarped environment.
Application of Knowledge Graph in Water Conservancy Education Resource Organization under the Background of Big Data

The key to improving the readability and usage of educational resources is their orderly arrangement and integration. Knowledge graphs, which are a large-scale form of knowledge engineering, are an effective tool for managing and organizing educational resources. The water conservancy’s educational big data is separated into three tiers of objectives–courses–knowledge units based on the connotation level of self-directed learning. Combined with the idea of Outcome-based Education(OBE), the goal-oriented knowledge graph structure of water conservancy disciplines and graph creation method is proposed. The focus is the error accumulation problem brought about by the traditional relational extraction method of Named Entity Recognition based on rules or sequence labeling. We first complete this objective, and then the relationship classification is performed according to the water conservancy disciplines entity and relations joint extraction (WDERJE) model, on which the prompt mechanism design is based. Think of the entity-relationship extraction task as a sequence-to-sequence generation task, and take the structured extraction language to unify the coding entity extraction and relationship extraction structures. The evaluation results of the WDERJE model show that the F_0.5 value of each entity extraction is above 0.76, and the cumulative extraction relationship triple is nearly 180,000. The graph fully optimizes the organization and management of water conservancy education resources and effectively improves the readability and utilization rate of water conservancy teaching resources.
Digital competencies of Peruvian teachers in basic education

Digital teaching competence is directly related to the knowledge, use of Information and Communication Technologies (ICTs). Its use in educational contexts and processes seeks to integrate technology and pedagogy. The objectives of this study are to evaluate the level of Digital Teaching Competencies (DTCs) possessed by basic education (BE) teachers in different Peruvian schools and their comparison by competency area, considering sociodemographic factors. The research responds to a quantitative methodological approach. A non-experimental design of a comparative descriptive type was used, and a non-probabilistic purposeful sampling was applied. A total of 3,142 BE teachers participated in the study, including the educational levels of preschool (0–5 years), primary school (6–11 years), and Secondary school (12–16 years), belonging to public institutions located in rural and urban areas of the province of Arequipa (Peru). The instrument developed in the European Framework of Digital Competence, DigCompEdu Check-In, was applied. This instrument aimed to measure teachers’ level of competence in the digital domain. The main results show that a more significant number of teachers present an Integrator competence level (B1), followed by the Expert level (B2), and the Explorer level (A2). Furthermore, low percentages are observed in the most advanced leadership levels, Leader (C1) and Pioneer (C2), in the use of ICTs. The results obtained suggest implementing new strategies to improve DTCs. Based on critical conceptions, technology constitutes an essential tool for education in the twenty first century.
The human-following strategy for mobile robots in mixed environments

The robot behavior strategy is considered as a crucial part in the human-following task to help the robot maintain an appropriate distance and orientation to the selected target person (STP) with a smooth and safe manner. As usual, the robot is uniquely considered to follow the STP in a specific class of environments, such as unknown environments (non-mapped environments) or known environments (mapped environments). However, in real-life applications, the robot is sometimes requested to follow the STP in various types of environments, both in known and unknown ones. This observation raises the need to propose an alternative method to challenge the mentioned issue, as well as to break the current limit of the human-following function. In this paper, a new approach for the human-following strategy is proposed in which the mobile robot is enabled to follow the STP in mixed environments (non-mapped and mapped). In non-mapped environments, only the STP and the obstacle information with respect to the robot local coordinates are considered, whose purpose is to make the robot work without any prior understandings about its working environment. However, after the robot entered mapped environments, its prior knowledge of the working environment is leveraged to fulfill some additional requirements during the cooperation, such as the mobile robot in factories is not allowed to enter some specific areas even when the STP is executing technical tasks inside. Additionally, in this paper, a human-like inference mechanism is also introduced for the human-following strategy by using an extended hedge algebras. The proposed method is experimentally verified both in factories and laboratories. Demo Video Link: https://www.youtube.com/watch?v=YGrWU6ldKuw Since real videos in the factory are not allowed to publish, only visualization (in Rviz) is presented for demos in such kinds of environments. The visualization is synchronous with the real executions of the human–robot interactions. The robot used in the factory is an autonomous mobile robot (dimension 0.5 (m) ×1.0 (m), weight 120 (kg), carrying a tool cabinet around 300(kg))). The mobile robot is following the worker to support them during the technical processes in the car production line. In the video, the robot is represented by a green rectangular, and the STP is represented by a cylinder (with a sphere on its head) The events in the demo video are described more clearly in Appendix A.