Reviewerly/exHarmony huggingface dataset:
A Benchmark for Unsupervised Anomaly Detection in Multi-Agent Trajectories

Human intuition allows to detect abnormal driving scenarios in situations they never experienced before. Like humans detect those abnormal situations and take counter-measures to prevent collisions, self-driving cars need anomaly detection mechanisms. However, the literature lacks a standard benchmark for the comparison of anomaly detection algorithms. We fill the gap and propose the R-U-MAAD benchmark for unsupervised anomaly detection in multi-agent trajectories. The goal is to learn a representation of the normal driving from the training sequences without labels, and afterwards detect anomalies. We use the Argoverse Motion Forecasting dataset for the training and propose a test dataset of 160 sequences with human-annotated anomalies in urban environments. To this end we combine a replay of real-world trajectories and scene-dependent abnormal driving in the simulation. In our experiments we compare 11 baselines including linear models, deep auto-encoders and one-class classification models using standard anomaly detection metrics. The deep reconstruction and end-to-end one-class methods show promising results. The benchmark and the baseline models will be publicly available <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> Project page: https://github.com/againerju/r_u_maad..
MAFF-Net: Filter False Positive for 3D Vehicle Detection with Multi-modal Adaptive Feature Fusion

3D vehicle detection based on multi-modal fusion is an important task of many applications such as autonomous driving. Although significant progress has been made, we still observe two aspects that calls for further improvement: First, what extra information can be obtained from the images to complement the point clouds in 3D detection tasks is seldom explored by previous works. Second, most fusion modules can only be used in their designed network, lacking universality. In this work, we propose PointAttentionFusion and DenseAttentionFusion: two end-to-end trainable single-stage multi-modal feature fusion approaches to adaptively combine RGB and point cloud modalities. Experimental results on the KITTI dataset demonstrate significant improvement in filtering false positive over the approaches using only point cloud data. Furthermore, the proposed methods can provide competitive results compared to the published state-of-the-art multi-modal methods in the KITTI benchmark. Both fusion modules are applicable in all voxel-based 3D detection architectures and similar improvements are expected.
Efficient Abnormal Building Consumption Detection by Deep Learning LSTM IOT Data Classification

This research deals with the method of efficient detection of abnormal building energy consumption and energy losses for further intelligent building energy management and respect of energy transition obligations. Automatic energy loss detection has been proposed using Deep Learning (DL) Long Short Term Memory (LSTM) networks specially dedicated for building data provided by Internet of Things (IoT) objects. The article presents the proposed neural network, a description of the data classification, as well as features of processing the input and output data of the classification. A practical evaluation of the classification method was made on a real IoT building sensors dataset provided by the multifunctional administrative building in the Paris area, France. To train the neural network for accurate classification, the process of initial manual input data processing was presented. A two-month dataset was used for training, and a two-week dataset was used to test the efficiency of the obtained neural network. The subsequent in-depth data analysis showed the high efficiency and significant accuracy of the obtained method in the classification and research of abnormal building energy consumption.
Residual Swin Transformer Channel Attention Network for Image Demosaicing

Image demosaicing is problem of interpolating full-resolution color images from raw sensor (color filter array) data. During last decade, deep neural networks have been widely used in image restoration, and in particular, in demosaicing, attaining significant performance improvement. In recent years, vision transformers have been designed and successfully used in various computer vision applications. One of the recent methods of image restoration based on a Swin Transformer (ST), SwinIR, demonstrates state-of-the-art performance with a smaller number of parameters than neural network-based methods. Inspired by the success of SwinIR, we propose in this paper a novel Swin Transformer-based network for image demosaicing, called RSTCANet. To extract image features, RSTCANet stacks several residual Swin Transformer Channel Attention blocks (RSTCAB), introducing the channel attention for each two successive ST blocks. Extensive experiments demonstrate that RSTCANet outperforms state-of-the-art image demosaicing methods, and has a smaller number of parameters. The source code is available at https://github.com/xingwz/RSTCANet.
Socially Interactive Agents as Peers

chapter Share on Socially Interactive Agents as Peers Author: Justine Cassell View Profile Authors Info & Claims The Handbook on Socially Interactive Agents: 20 years of Research on Embodied Conversational Agents, Intelligent Virtual Agents, and Social Robotics Volume 2: Interactivity, Platforms, ApplicationOctober 2022 Pages 331–366https://doi.org/10.1145/3563659.3563670Published:03 November 2022Publication History 4citation9DownloadsMetricsTotal Citations4Total Downloads9Last 12 Months9Last 6 weeks6 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Optimized key generation-based privacy preserving data mining model for secure data publishing

The data from the mobile devices and internet services plays a significant role now a day. The information shared needs to be original to get the best solution. Without the utilization of privacy preservation techniques, the individual's sensitive information cannot be exposed. The Privacy preserving data publishing (PPDP) is one of the privacy preserving techniques to preserve privacy. However, the reduction of the loss of data and the security enhancement are the major challenges. To solve the issues in secure data publishing, the efficient Cat Swarm Henry Gas Solubility Optimization (CSHGSO) is introduced for achieving effective privacy preservation for secure data publishing. In addition, the proposed CSHGSO is devised by the incorporation of Cat Swarm Optimization (CSO), and Henry Gas Solubility Optimization (HGSO). Here, the procedure of generating secret key is carried out based on the proposed CSHGSO with respect to the objective measures. The experimentation of the developed CSHGSO is performed in JAVA tool using Heart Disease Dataset. Furthermore, the CSHGSO achieved the higher conditional privacy of 1.338 using database-1, maximum accuracy of 0.965 using database-4, and the NMI value of 0.927 using database-4.
Learning successful implementation of Chatbots in businesses from B2B customer experience perspective

Summary Artificial intelligence empowered Chatbots are altering the nature of service interfaces which has further resulted in raised expectations from Chatbots to understand customer's social interactions and respond them within the turnaround time. To close this research gap, we conduct an exploratory study in two phases‐industry's perspective and B2B customer's perspective and analyze results with the help of NVIVO 12 plus and Leximancer. The findings reveal perceived risk with respect to Chatbots is high, complex pricing structure along with nonavailability of testing options makes the pre purchase more complex. Moreover, interactive speed, customization especially with respect to language issues, integration with other platforms is some of the major themes which influence customer experience. Advancements in AI, natural language processing and more testing at all phases will bring efficiency, automation first strategies. Further, our findings suggest Chatbots must provide more personalization, scalability and omni channel engagement and focus on delivering more enhanced customer experience. Chatbots must offer a grievance management dashboard where the customer can see live queries, resolved queries, present queries status and so on to get transparency. Chatbots streamline the lead qualification process, greatly improve, and speed up the data collection therefore, enhancing customer experience.
Oriented Vehicle Detection in Aerial Images Based on YOLOv4

CNN-based object detectors have achieved great success in recent years. The available detectors adopted horizontal bounding boxes to locate various objects. However, in some unique scenarios, objects such as buildings and vehicles in aerial images may be densely arranged and have apparent orientations. Therefore, some approaches extend the horizontal bounding box to the oriented bounding box to better extract objects, usually carried out by directly regressing the angle or corners. However, this suffers from the discontinuous boundary problem caused by angular periodicity or corner order. In this paper, we propose a simple but efficient oriented object detector based on YOLOv4 architecture. We regress the offset of an object's front point instead of its angle or corners to avoid the above mentioned problems. In addition, we introduce the intersection over union (IoU) correction factor to make the training process more stable. The experimental results on two public datasets, DOTA and HRSC2016, demonstrate that the proposed method significantly outperforms other methods in terms of detection speed while maintaining high accuracy. In DOTA, our proposed method achieved the highest mAP for the classes with prominent front-side appearances, such as small vehicles, large vehicles, and ships. The highly efficient architecture of YOLOv4 increases more than 25% detection speed compared to the other approaches.
Are cryptocurrencies a future safe haven for investors? The case of Bitcoin

The paper seeks to determine whether Bitcoin behaves differently from forex markets and Gold, and whether it offers any diversification, hedging, or safe-haven potential.A Markov regime-switching regression model is employed to determine the relationship between Bitcoin, the real economic activity, foreign exchange markets, financial markets, Energy, and Gold.The results indicate that, unlike USD/EUR and Gold, besides other variables, Bitcoin exhibits significant deviations in terms of its association with other financial and economic variables.Bitcoin appears to be strikingly positively associated with equity markets in both regimes.This may limit its potential to either act as a hedge or a safe-haven for US Equity markets.Bitcoin also deviates considerably from Gold and USD/EUR as it is not affected by the same set of variables as Gold or USD/EUR are under either regime.Moreover, while Gold appears to offer considerably weak safe-haven properties, particularly against equity, Bitcoin fails to be a safe-haven for any of the assets under study.The results, however, indicate that the properties of Bitcoin may range between a diversifier and a hedge, however, such potential of Bitcoin must be viewed with caution owing to the large volatility exhibited by Bitcoin.
QoS prediction for web services in cloud environments based on swarm intelligence search

In the real-world cloud computing scenarios, a user usually invokes few Web services and can provide only a small proportion of QoS (Quality of Service) records. Therefore, for a whole range of services, many records for this user are missing (i.e. incomplete QoS information). These missing QoS values make it difficult to conduct accurate service recommendations. To overcome this difficulty, it is necessary to predict the missing QoS values for the user. Most existing algorithms tackle the QoS prediction problem through aggregating the QoS values of the local similar neighbors of the user, which easily leads to local optimization. In this paper, we model it as a global search optimization problem in the distribution space of QoS values and propose a novel algorithm PSO-USRec. In the algorithm, particle swarm optimization (PSO) is customized and improved by diversifying the initial solutions and smoothing the outlier particles. To validate the effectiveness of the PSO-USRec algorithm, comparison experiments are conducted on a well-known public QoS dataset. The experimental results show that the PSO-USRec algorithm significantly outperforms the state-of-the-art collaborative filtering approaches. It reduces MAE and RMSE by at least 5.42% and 1%, and at most 14.29% and 2.25%, respectively.
A novel framework for deep knowledge tracing via gating-controlled forgetting and learning mechanisms

In this paper, we propose a framework called Gating-controlled Forgetting and Learning mechanisms for Deep Knowledge Tracing (GFLDKT for short). In GFLDKT, two gating-controlled mechanisms are designed to model explicitly forgetting and learning behaviors in students’ learning process. With the designed gating-controlled mechanisms, both the interaction records and students’ different backgrounds are combined effectively for tracing the dynamic changes of students’ mastery of knowledge concepts. Results from extensive experiments demonstrate that the proposed framework outperforms the state-of-the-art models on the KT task. In addition, the ablation study shows that designed forgetting and learning mechanisms contribute clearly to the performance improvement of GFLDKT. • A novel deep learning-based KT model is proposed, which explicitly utilizes the theories of learning and forgetting curves in updating knowledge states. • Two gating-controlled mechanisms are designed for learning and forgetting curves, by which the interaction records and students’ distinctive backgrounds are considered simultaneously. • Results from extensive experiments demonstrate the effectiveness of the proposed model, which outperforms the SOTA KT models.
Question-guided feature pyramid network for medical visual question answering

Medical VQA (VQA-Med) is a critical multi-modal task that raises attention from the community. Existing models utilized just one high-level feature map (i.e., the last layer feature map) extracted by CNN and then fused it with semantic features through the co-attention mechanism. However, only using the high-level feature as a visual feature often ignores the details of the image, which are crucial for VQA-Med. In addition, questions often serve as a guide to targets of attention in the medical image. Therefore, in this paper, we propose a question-guided Feature Pyramid Network (QFPN) for VQA-Med. It extracts multi-level visual features with a feature pyramid network (FPN). In this way, the multi-scale information of medical images can be captured by using the high resolution of low-level features and rich semantic information of high-level features. Besides, a novel question-guided dynamic filter network (DFN) is designed to modulate the fusion progress of multi-level visual features and semantic features with respect to the raised question. Extensive results have demonstrated the effectiveness of the QFPN. Especially, we beat the winner of the 2019 ImageCLEF challenge and achieved 63.8% Accuracy and 65.7% BLEU in the ImageCLEF 2019 VQA-Med dataset.
GANRec: A negative sampling model with generative adversarial network for recommendation

Although many negative sampling and generative adversarial network (GAN) strategies have been applied to recommendation, this frequently encounter problems of poor interpretability and performance of negative sampling. To address these issues, we construct a recommendation model named GANRec, which organically integrates negative sampling and GANs on a bipartite graph (BG) or knowledge graph (KG). The primary purpose of GANRec is to train a discriminator (i.e., a recommender) by generating high-quality negative samples that satisfy the perspective of graph theory, core idea of collaborative filtering, and distribution of positive interactions. The generator attempts to produce, distinguish, and select an informative and knowledge-aware negative item that can reflect the real needs of an user. Experiments on the Amazon-Book, Last-FM, and Yelp2018 datasets show that the recommendation performance of the proposed method not only exceeds the KG-enhanced models, but also is better than those of state-of-the-art sampling methods. Additionally, when no external knowledge is available, GANRec outperforms some BG-enhanced recommendation methods. Our codes are available in https://github.com/Yangzhi22/GANRec.
Critical raw material resources in Nugrus-Sikait area, South Eastern Desert, Egypt: Geological and geochemical aspects

The current work aims to assess the critical raw materials (CRMs) in the Arabian-Nubian Shield in accordance with the European Union. CRMs are strategically and economically significant, but they are subject to supply shortages. CRMs, such as Nb, Ta, Zr, U, Th, Rb, Ga, Hf, Zn, Ba, Sr, Sn, Cu, and Pb, are concentrated in post-orogenic highly fractionated granitic plutons, such as Abu Dabbab, Abu Diab, Homrit Waggat, Um Naggat, and Nuweibi. They also occur in the gneiss rocks of the Nugrus-Sikait area. The present work sheds light on the CRM resources in the Nugrus-Sikait area in the South Eastern Desert, Egypt. Gneiss rocks, ophiolitic rocks (ultramafics, metagabbros, schists, and mélange), gneissose biotite granites, muscovite leucogranites, pink biotite granites, and post-granitic dikes and veins are the main exposed rock units in the target area. CRMs are abundant in the examined area and reach up to 1845 ppm Nb, 203 ppm Ta, 3212 ppm Zr, 1789 ppm Y, 278 ppm Hf, 3209 ppm Zn, ˃10000 ppm Pb, 941 ppm Sn, 1458 ppm Cu, 3551 ppm U, 2233 ppm Th, 144 ppm Sr, 1416 ppm Rb, and 121 ppm Ba. Therefore, it's recommended to shed light on CRMs distribution due to their economic importance and strategic uses.
Frisson: Leveraging Metasomatic Interactions for Generating Aesthetic Chills

Opportunities to evoke emotional experiences and modulate cognitive processes hold great importance in studying relations between emotion, cognition and behaviour as well as building technologies for maintaining positive mental health. We present the concept of Metasomatic Interactions as a new way of generating and controlling previously untapped embodied emotions using illusory sensations tuned to the prior bodily experiences. We present Frisson, a metasomatic interface built to elicit the sensations underlying the embodied emotion of aesthetic chills (i.e., goosebumps, psychogenic shivers). We present a user study (N = 14) in which the device evokes the psychogenic experience of aesthetic chills by simulating traversing thermal sensations across the spine when the illusory sensations overlap with the prior impression of aesthetic chills. The results encourage further testing of metasomatic interfaces for inducing emotions from the body. Using theories of embodied cognition, experimental results and this device, we present a discussion on characteristics of metasomatic interfaces, which we hope will inspire new categories of emotional prostheses in HCI.
MCFM: Mutual Cross Fusion Module for Intermediate Fusion-Based Action Segmentation

This paper presents an action segmentation method utilizing multiple features on the basis of a novel intermediate fusion module, named Mutual Cross Fusion Module (MCFM). The proposed method analyzes multiple features on each feature’s classifier stream. MCFM recalibrates the target feature in the middle of the classifier stream from the knowledge of the other features in contrast to the existing module of the previous method, which utilizes a joint representation learned from all features for the recalibration of the target feature. MCFM integrates the knowledge of multiple features without biassing the knowledge toward one of the multiple features. We compare the proposed method with the state-of-the-art methods on two public datasets: GTEA and 50Salads. The proposed method outperforms the state-of-the-art methods in terms of frame-wise accuracy, edit distance, and F1-score by 2.0, 1.6, and 2.9 points, respectively.
Multi-Scale Raft: Combining Hierarchical Concepts for Learning-Based Optical Flow Estimation

Many classical and learning-based optical flow methods rely on hierarchical concepts to improve both accuracy and robustness. However, one of the currently most successful approaches – RAFT – hardly exploits such concepts. In this work, we show that multi-scale ideas are still valuable. More precisely, using RAFT as a baseline, we propose a novel multi-scale neural network that combines several hierarchical concepts within a single estimation framework. These concepts include (i) a partially shared coarse-to-fine architecture, (ii) multi-scale features, (iii) a hierarchical cost volume and (iv) a multi-scale multi-iteration loss. Experiments on MPI Sintel and KITTI clearly demonstrate the benefits of our approach. They show not only substantial improvements compared to RAFT, but also state-of-the-art results – in particular in non-occluded regions. Code will be available at https://github.com/cv-stuttgart/MS_RAFT.
Neural Network Fragile watermarking With No Model Performance Degradation

Deep neural networks are vulnerable to malicious fine-tuning attacks such as data poisoning and backdoor attacks. Therefore, in recent research, it is proposed how to detect malicious fine-tuning of neural network models. However, it usually negatively affects the performance of the protected model. Thus, we propose a novel neural network fragile watermarking with no model performance degradation. In the process of watermarking, we train a generative model with the specific loss function and secret key to generate triggers that are sensitive to the fine-tuning of the target classifier. In the process of verifying, we adopt the watermarked classifier to get labels of each fragile trigger. Then, malicious fine-tuning can be detected by comparing secret keys and labels. Experiments on classic datasets and classifiers show that the proposed method can effectively detect model malicious fine-tuning with no model performance degradation.
Manet: Improving Video Denoising with a Multi-Alignment Network

In video denoising, the adjacent frames often provide very useful information, but accurate alignment is needed before such information can be harnassed. In this work, we present a multi-alignment network, which generates multiple flow proposals followed by attention-based averaging. It serves to mimic the non-local mechanism, suppressing noise by averaging multiple observations. Our approach can be applied to various state-of-the-art models that are based on flow estimation. Experiments on a large-scale video dataset demonstrate that our method improves the denoising baseline model by 0.2 dB, and further reduces the parameters by 47% with model distillation. Code is available at https://github.com/IndigoPurple/MANet.