Reviewerly/exHarmony huggingface dataset:
A Regularization by Denoising super-resolution method based on genetic algorithms

Increasing the resolution of an image is an actual and extensively studied problem in image processing. Recently, Regularization by Denoising (RED) showing that any inverse problem can be handled by sequentially applying image denoising steps, including the image super-resolution (SR) task, which facilitate the resolution of the encountered optimization problem. In this paper, we propose a new configuration of genetic algorithms to resolve the super-resolution problem using a Non-Local Means filter as a denoiser function with a rigorous proof of the existence of a unique minimizer. In fact, since the SR algorithms always skip the complex spatial interactions within images, a more consistent model is then needed. The use of the genetic algorithms with the RED techniques guaranteed, in high intensity of noise and blur, the convergence to the globally optimal solution. As a result, the proposed algorithm shows efficient and consistent results, in terms of edges and feature preservation, compared with other SR approaches.
Vehicular Trajectory Classification and Traffic Anomaly Detection in Videos Using a Hybrid CNN-VAE Architecture

Visual surveillance has become indispensable in the evolution of Intelligent Transportation Systems (ITS). Video object trajectories are key to many of the visual surveillance applications. Classifying varying length time series data such as video object trajectories using conventional neural networks, can be challenging. In this paper, we propose trajectory classification and anomaly detection using a hybrid Convolutional Neural Network (CNN) and Variational Autoencoder (VAE) architecture. First, we introduce a high level features for varying length object trajectories using color gradient representation. In the next stage, a semi-supervised way to annotate moving object trajectories extracted using Temporally Incremental Gravitational Model (TIGM) is used for class labeling. For training, anomalous trajectories are identified using t-Distributed Stochastic Neighbor Embedding (t-SNE). Finally, a hybrid CNN-VAE architecture has been proposed for trajectory classification and anomaly detection. The results obtained using publicly available surveillance video datasets reveal that the proposed method can successfully identify traffic anomalies such as violations in lane driving, sudden speed variations, abrupt termination of vehicle during movement, and vehicles moving in wrong directions. The accuracy of trajectory classification improves by a margin of 1-6% against popular neural networks-based classifiers across various datasets using the proposed high-level features. The gradient representation also improves the anomaly detection accuracy significantly (30-35%). Code and dataset can be found at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/santhoshkelathodi/CNN-VAE</uri> .
Action Anticipation Using Pairwise Human-Object Interactions and Transformers

The ability to anticipate future actions of humans is useful in application areas such as automated driving, robot-assisted manufacturing, and smart homes. These applications require representing and anticipating human actions involving the use of objects. Existing methods that use human-object interactions for anticipation require object affordance labels for every relevant object in the scene that match the ongoing action. Hence, we propose to represent every pairwise human-object (HO) interaction using only their visual features. Next, we use cross-correlation to capture the second-order statistics across human-object pairs in a frame. Cross-correlation produces a holistic representation of the frame that can also handle a variable number of human-object pairs in every frame of the observation period. We show that cross-correlation based frame representation is more suited for action anticipation than attention-based and other second-order approaches. Furthermore, we observe that using a transformer model for temporal aggregation of frame-wise HO representations results in better action anticipation than other temporal networks. So, we propose two approaches for constructing an end-to-end trainable multi-modal transformer (MM-Transformer)1 model that combines the evidence across spatio-temporal, motion, and HO representations. We show the performance of MM-Transformer on procedural datasets like 50 Salads and Breakfast, and an unscripted dataset like EPIC-KITCHENS55. Finally, we demonstrate that the combination of human-object representation and MM-Transformers is effective even for long-term anticipation.
Enhancing Conversational Agents with Empathic Abilities

Conversational agents are getting increasingly popular and find applications in health and customer services. Conversations in these fields are often emotionally charged. It is, therefore, necessary to handle the conversation with some degree of empathy to be effective. In this work, we leverage advances in the field of natural language processing to create a dialogue system that can convincingly generate empathic responses to text-based messages. To improve the system's ability to converse with empathy, we train the language model on empathic conversations and inject additional emotional information in the response generation. We propose two chatbots: a benchmark bot and an empathic bot. Additionally, we implement an emotion classifier that allows us to predict the emotional state of text-based messages. We evaluate both chatbots in quantitative studies and compare them with human responses in qualitative studies involving human judges. Our evaluation shows that our empathic chatbot outperforms the benchmark bot and even the human-generated responses in terms of perceived empathy. Additionally, we achieve state-of-the-art results in terms of response quality using transformer-based language models. Finally we report that we can double the initial performance of the emotion classifier using undersampling techniques, yielding a final F1-score of 0.81 in six basic emotions.
The STEAM approach: Implementation and educational, social and economic consequences

The integration of the arts and humanities into the core of curriculum along with the sciences and technological disciplines is an emerging issue in educational research. This article seeks to contribute to this research and curricular approach, for which we analyze the emergence of the STEAM movement, its implementation in class, and its social, economic, and educational consequences. The main conclusion reached is that, without ignoring the economic rationality in education, it is necessary to go further in order to embrace a more social and democratic conception of schooling, trying to take advantage of this historical moment to transform education toward a more humanistic approach–without neglecting the scientific facet–that offers a well-rounded education to new generations while, at the same time, responds to the social and economic demands of our current world.
1D Convolution approach to human activity recognition using sensor data and comparison with machine learning algorithms

Human Activity Recognition (HAR) has emerged as a major player in this era of cutting-edge technological advancement. A key role that HAR plays is its ability to remotely monitor people. The objective of this paper is to classify human activities based on the data retrieved from smartphone sensors (accelerometer and gyroscope). The human activities that will be classified are namely; sitting, standing, climbing up and down the staircase, walking and laying down. To perform HAR from the data obtained, machine learning models are formed and fine-tuned in order to achieve the best results. The classic Machine Learning algorithms that have been put to use are Logistic regression, Linear and Kernel SVM, Decision Tree and Random Forest. Furthermore, a feed-forward Deep Neural Network and a 1D Convolutional Neural Network are proposed and compared with the aforementioned machine learning algorithms. Evaluation of a particular model has been carried out based on its Recall score, Precision and F1 score. Based on the results obtained from the evaluation process, it was found that the SVM and the proposed 1D convolution neural network were the best-performing models.
Contrastive Attention for Video Anomaly Detection

We consider weakly-supervised video anomaly detection in this work. This task aims to learn to localize video frames containing anomaly events with only binary video-level annotation, <i>i.e.</i>, anomaly vs. normal. Traditional approaches usually formulate it as a multiple instance learning problem, which ignore the intrinsic data imbalance issue that positive samples are very scarce compared to negative ones. In this paper, we focus on addressing this issue to boost detection performance further. We develop a new light-weight anomaly detection model that fully utilizes enough normal videos to train a classifier with a good discriminative ability for normal videos, and we employ it to improve the selectivity for anomalous segments and filter out normal segments. Specifically, in addition to boosting anomalous prediction, a novel contrastive attention module additionally produces a converted normal feature from anomalous video to refined anomalous predictions by maximizing the classifier making a mistake. Moreover, to remove the stubborn normal segments selected by the attention module, we also design an attention consistency loss to employ the classifier with high confidence for normal features to guide the attention module. Extensive experiments on two large-scale datasets, UCF-Crime, ShanghaiTech and XD-Violence, clearly demonstrate that our model largely improves frame-level AUC over the state-of-the-art. Code is released at <uri>https://github.com/changsn/Contrastive-Attention-for-Video-Anomaly-Detection</uri>.
Path Planning and Obstacle Avoidance Based on Reinforcement Learning for UAV Application

Applications of drones in the military and daily life have increased in recent years. However, it is necessary to have obstacle avoidance capability. Path planning is also needed for automated tasks. In this study, path planning and obstacle avoidance based on a reinforcement learning algorithm are implemented in an unmanned aerial vehicle (UAV). For global path planning, a Q-learning algorithm is utilized to find the path between waypoints first. Then the local obstacle avoidance system is applied to train the UAV by the Deep Q-learning algorithm in Microsoft AirSim unreal environment.
A family of varying-parameter finite-time zeroing neural networks for solving time-varying Sylvester equation and its application

A family of varying-parameter finite-time zeroing neural networks (VPFTZNN) is introduced for solving the time-varying Sylvester equation (TVSE). The convergence speed of the proposed VPFTZNN family is analysed and compared with the traditional zeroing neural network (ZNN) and the finite-time zeroing neural network (FTZNN). The behaviour of the proposed neural networks under various activation functions is proved theoretically and verified experimentally. In addition, the stability and noise resistance of the proposed VPFTZNN family are discussed. Further, the proposed VPFTZNN models are applied in the computation of current flows in an electrical network.
Multi-view Self-supervised Heterogeneous Graph Embedding

Graph mining tasks often suffer from the lack of supervision from labeled information due to the intrinsic sparseness of graphs and the high cost of manual annotation. To alleviate this issue, inspired by recent advances of self-supervised learning (SSL) on computer vision and natural language processing, graph self-supervised learning methods have been proposed and achieved remarkable performance by utilizing unlabeled information. However, most existing graph SSL methods focus on homogeneous graphs, ignoring the ubiquitous heterogeneity of real-world graphs where nodes and edges are of multiple types. Therefore, directly applying existing graph SSL methods to heterogeneous graphs can not fully capture the rich semantics and their correlations in heterogeneous graphs. In light of this, we investigate self-supervised learning on heterogeneous graphs and propose a novel model named Multi-View Self-supervised heterogeneous graph Embedding (MVSE). By encoding information from different views defined by meta-paths and optimizing both intra-view and inter-view contrastive learning tasks, MVSE comprehensively utilizes unlabeled information and learns node embeddings. Extensive experiments are conducted on various tasks to show the effectiveness of the proposed framework.
Visible and thermal images fusion architecture for few-shot semantic segmentation

Few-shot semantic segmentation (FSS) has drawn great attention in the community of computer vision, due to its remarkable potential for segmenting novel objects with few pixel-annotated samples. However, some interference factors, such as insufficient illumination and complex background, can impose more challenge to the segmentation performance than fully-supervised when the number of samples is insufficient. Therefore, we propose the visible and thermal (V-T) few-shot semantic segmentation task, which utilize the complementary and similar information of visible and thermal images to boost few-shot segmentation performance. As the first step, we build a novel outdoor city dataset Tokyo Multi-Spectral-4i for the V-T few-shot semantic segmentation task. In addition, a fusion architecture is proposed, which consists of an Edge Similarity fusion module (ES) and a Texture Edge Prototype module (TEP). The ES module fuses the bi-modal information by exploiting the edge similarity in the visible and thermal images. The TEP module extracts the prototype from two models by collaborating the representativeness and complementarity of the visible and thermal feature. Finally, extensive experiments conducted on the proposed datasets demonstrate that our architecture can achieve state-of-the-arts results.
VR-Electricians: Immersive storytelling for attracting students to the electrical construction industry

Although it is projected to have significant employment growth in the electrical construction industry, there are not enough young people to join the workforce. It is indispensable for the future of work in construction to explore emerging technologies and effectively attract the younger generation to the construction industry. This paper explores the innovative use of 360-degree immersive environments powered by virtual electricians to tell success stories of the electrical construction trades on the field (VR-Electricians) and understand how such an immersive storytelling approach can attract young people to join the industry. A pretest-posttest design was used for the VR-Electricians assessment. VR-Electricians was found to significantly enhance several aspects of students' attitude towards the electrical construction industry. VR-Electricians was also rated as a highly usable and easy-to-use system. The outcomes of this study provide construction researchers and practitioners an understanding of how immersive storytelling can be used as a powerful technology-mediated method for attracting students to their field.
LSBert: Lexical Simplification Based on BERT

Lexical simplification (LS) aims at replacing complex words with simpler alternatives. LS commonly consists of three main steps: complex word identification, substitute generation, and substitute ranking. Existing LS methods focus on the contextual information of the complex word in the last step (substitute ranking). However, they miss out the following two facts: (1) The word complexity of a polysemous word is very closely related to its context; (2) The step of substitute generation regardless of the context will inevitably produce a large number of spurious candidates. Therefore, we propose a novel LS system LSBert based on pretrained language model BERT to address the aforementioned issues, which is capable of making use of the wider context when both identifying the words in need of simplification and generating substitute candidates for the complex words. Specifically, LSBert consists of a network for complex word identification by fine-tuning BERT and a network for substitute generation based on BERT. Experimental results show that LSBert performs well in both complex word identification and substitute generation, achieving state-of-the-art results in three benchmarks. To facilitate reproducibility, the code of the LSBert system is available at https://github.com/qiang2100/BERT-LS.
ROBIN: A Robust Optical Binary Neural Network Accelerator

Domain specific neural network accelerators have garnered attention because of their improved energy efficiency and inference performance compared to CPUs and GPUs. Such accelerators are thus well suited for resource-constrained embedded systems. However, mapping sophisticated neural network models on these accelerators still entails significant energy and memory consumption, along with high inference time overhead. Binarized neural networks (BNNs), which utilize single-bit weights, represent an efficient way to implement and deploy neural network models on accelerators. In this paper, we present a novel optical-domain BNN accelerator, named ROBIN , which intelligently integrates heterogeneous microring resonator optical devices with complementary capabilities to efficiently implement the key functionalities in BNNs. We perform detailed fabrication-process variation analyses at the optical device level, explore efficient corrective tuning for these devices, and integrate circuit-level optimization to counter thermal variations. As a result, our proposed ROBIN architecture possesses the desirable traits of being robust, energy-efficient, low latency, and high throughput, when executing BNN models. Our analysis shows that ROBIN can outperform the best-known optical BNN accelerators and many electronic accelerators. Specifically, our energy-efficient ROBIN design exhibits energy-per-bit values that are ∼4 × lower than electronic BNN accelerators and ∼933 × lower than a recently proposed photonic BNN accelerator, while a performance-efficient ROBIN design shows ∼3 × and ∼25 × better performance than electronic and photonic BNN accelerators, respectively.
Six reasons why virtual reality is a game-changing computing and communication platform for organizations

Beyond the pandemic, organizations need to recognize what digital assets, interactions, and communication processes reap the most benefits from virtual reality.
Graph partition based privacy-preserving scheme in social networks

With the development of social networks, more and more data about users are released on social platforms such as Facebook, Enron, WeChat, in terms of social graphs. Without the efficient anonymization, the graph data publishing will cause serious privacy leakage of users, for example, malicious attackers might launch 1-neighborhood graph attack on targets, which assumes that 1-hop neighbors and the relations among them are known by attackers, thereby, targets can be re-identified in anonymous social graphs. To prevent such attack, we propose a Graph Partition based Privacy-preserving Scheme, named GPPS,i n social networks to realize social graph anonymization. The proposed GPPS preserves users' identity privacy by k-anonymity which achieved by node clustering and graph modification. Specifically, in the similarity matrix calculation, we introduce the degree-based graph entropy to improve the accuracy of node clustering. Then, the graph modification is implemented to achieve the k-anonymity of users and meanwhile minimize the graph information loss. The experiment results illustrate that the proposed GPPS is effective and efficient both on synthetic and real data sets.
Towards psychology-aware preference construction in recommender systems: Overview and research issues

Abstract User preferences are a crucial input needed by recommender systems to determine relevant items. In single-shot recommendation scenarios such as content-based filtering and collaborative filtering, user preferences are represented, for example, as keywords , categories , and item ratings . In conversational recommendation approaches such as constraint-based and critiquing-based recommendation, user preferences are often represented on the semantic level in terms of item attribute values and critiques . In this article, we provide an overview of preference representations used in different types of recommender systems. In this context, we take into account the fact that preferences aren’t stable but are rather constructed within the scope of a recommendation process. In which way preferences are determined and adapted is influenced by various factors such as personality traits , emotional states , and cognitive biases . We summarize preference construction related research and also discuss aspects of counteracting cognitive biases.
Edge-Preserving Convolutional Generative Adversarial Networks for SAR-to-Optical Image Translation

With the ability for all-day, all-weather acquisition, synthetic aperture radar (SAR) remote sensing is an important technique in modern Earth observation. However, the interpretation of SAR images is a highly challenging task, even for well-trained experts, due to the imaging principle of SAR images and the high-frequency speckle noise. Some image-to-image translation methods are used to convert SAR images into optical images that are closer to what we perceive through our eyes. There exist two weaknesses in these methods: (1) these methods are not designed for an SAR-to-optical translation task, thereby losing sight of the complexity of SAR images and the speckle noise. (2) The same convolution filters in a standard convolution layer are utilized for the whole feature maps, which ignore the details of SAR images in each window and generate images with unsatisfactory quality. In this paper, we propose an edge-preserving convolutional generative adversarial network (EPCGAN) to enhance the structure and aesthetics of the output image by leveraging the edge information of the SAR image and implementing content-adaptive convolution. The proposed edge-preserving convolution (EPC) decomposes the content of the convolution input into texture components and content components and then generates a content-adaptive kernel to modify standard convolutional filter weights for the content components. Based on the EPC, the EPCGAN is presented for SAR-to-optical image translation. It uses a gradient branch to assist in the recovery of structural image information. Experiments on the SEN1-2 dataset demonstrated that the proposed method can outperform other SAR-to-optical methods by recovering more structures and yielding a superior evaluation index.