Miracle-dz/UniFAR-FTUs huggingface dataset:
Temporal Alignment via Event Boundary for Few-shot Action Recongnition

Few-shot action recognition aims to recognize novel action classes using just a few samples as knowledge. Most of the recent approaches learn to compare the similarity between videos. Recently, it has been observed that directly measuring this similarity is not ideal since the action instance cannot well aligned among videos. In this paper, we leverage the novel event boundary information to guide alignment learning in few-shot action recognition. First, a novel frame sampling strategy based on temporal boundaries is proposed to relieve the intra-class variance. Second, we propose a boundary selection module to locate the start & end time of action and further align videos to their duration. Ablation studies and visualizations demonstrate the effectiveness of the proposed meth-ods. Extensive experiments on benchmark datasets show the potential of the proposed method in achieving state-of-the-art performance for few-shot action recognition.
Row level image forgery detection technique using embedded digital signatures

Image forgery detection is one of the most important issues in today’s modern world. It has become very easy to change the contents of digital images with image editing tools and software. This paper is presenting a new technique to detect any changes made in digital images. This technique ensures the integrity of digital image at row level and using embedded digital signatures. Using message digest 5 algorithm, digital signature is generated from selected pixels of each row using selected pixels for and embedded in the least significant bits of selected pixel of the corresponding row of digital image. The proposed technique is powerful enough to detect different image manipulations. The results show that it can successfully detect one least significant bit alteration made in any pixel of digital image.
Limits of Multilayer Diffusion Network Inference in Social Media Research

Information on social media spreads through an underlying diffusion network that connects people of common interests and opinions. This diffusion network often comprises multiple layers, each capturing the spreading dynamics of a certain type of information characterized by, for example, topic, language, or attitude. Researchers have previously proposed methods to infer these underlying multilayer diffusion networks from observed spreading patterns, but little is known about how well these methods perform across the range of realistic spreading data. In this paper, we conduct an extensive series of synthetic data experiments to systematically analyze the performance of the multilayer diffusion network inference framework, under varied network structure (e.g. density, number of layers) and information diffusion settings (e.g. cascade size, layer mixing) that are designed to mimic real-world spreading on social media. Our results show extreme performance variation of the inference framework: notably, it achieves much higher accuracy when inferring a denser diffusion network, while it fails to decompose the diffusion network correctly when most cascades in the data reach a limited audience. In demonstrating the conditions under which the inference accuracy is extremely low, our paper highlights the need to carefully evaluate the applicability of the inference before running it on real data. Practically, our results serve as a reference for this evaluation, and our publicly available implementation, which outperforms previous implementations in accuracy, supports further testing under personalized settings.
Order-independent Matching with Shape Similarity for Parking Slot Detection

Current mainstream methods adopt point regression and prior sizes to detect parking slots. Although these methods are approved to be useful in majority of circumstances, they have many limitations when they adapt to size and shape variations in more general slots because of a pre-deﬁned ﬁxed order, lack of holistic constraints and adequately diverse data. To address them, we propose an order-independent matching strategy with shape similarity to handle the more general slot sizes and shapes. The matching strategy adopts a two-level procedure: the point-level and the parking slot-level, that ﬁnds optimal order and association adaptively. More importantly, we adopt shape similarity to represent holistic geometry to rank slots so as to suppress the misshapen ones. Furthermore, we collected a large-scale and remote-view parking slot dataset (LRPS) to improve data diversity. It contains a large number of general parking environments, as well as slots of various shapes and sizes across different cities, daytime and interior-exterior scenes. The proposed approach is evaluated on the LRPS dataset and achieves superior performance to previous methods.
Similarity Estimation for Large-Scale Human Action Video Data on Spark

The amount of human action video data is increasing rapidly due to the growth of multimedia data, which increases the problem of how to process the large number of human action videos efficiently. Therefore, we devise a novel approach for human action similarity estimation in the distributed environment. The efficiency of human action similarity estimation depends on feature descriptors. Existing feature descriptors such as Local Binary Pattern and Local Ternary Pattern can only extract texture information but cannot obtain the object shape information. To resolve this, we introduce a new feature descriptor, namely Edge based Local Pattern descriptor (ELP). ELP can extract object shape information besides texture information and ELP can also deal with intensity fluctuations. Moreover, we explore Apache Spark to perform feature extraction in the distributed environment. Finally, we present an empirical scalability evaluation of the task of extracting features from video datasets.
On Adversarial Robustness of 3D Point Cloud Classification under Adaptive Attacks

3D point clouds are playing pivotal roles in many safety-critical applications like autonomous driving, where adversarially robust 3D deep learning models are desired. In this study, we conduct the ﬁrst security analysis of state-of-the-art (SOTA) defenses against 3D adversarial attacks and design adaptive evaluations on them. Our 100% adaptive attack success rates demonstrate that SOTA countermeasures are still fragile. We further present an in-depth study showing how adversarial training (AT) performs in point cloud classiﬁcation and identify that the required symmetric function (pooling operation) is paramount to 3D models’ robustness. Through systematic analysis, we unveil that the default-used ﬁxed pooling ( e.g., MAX pooling) generally weakens AT’s effectiveness. Interestingly, we also discover that sorting-based parametric pooling signiﬁcantly improves the models’ robustness. Based on the above insights, we propose DeepSym , a deep symmetric pooling operation, to architecturally advance the robustness of Point-Net to 47.0% under AT without sacriﬁcing nominal accuracy, outperforming the original design and a strong baseline by +28.5% ( ∼ 2 . 6 × ) and +6.5% , respectively.
Paying Attention to Varying Receptive Fields: Object Detection with Atrous Filters and Vision Transformers

Object detection represents a critical component in computer vision based on its unique ability to identify the location of one or more objects in an image or video. Given its importance, various approaches were proposed in an attempt to extract meaningful and representative features across different image scales. One such approach would be to vary the receptive ﬁelds during the feature extraction process. However, varying and adjusting the receptive ﬁeld adds complexity to the process of scene understanding by introducing a higher degree of unimportant semantics into the feature maps. To solve this problem, we propose a novel object detection framework by unifying dilation modules (or atrous convolutions) with a vision transformer (DIL-ViT). The proposed model leverages atrous convolutions to generate rich multi-scale feature maps and employs a self-attention mechanism to enrich important backbone features. Speciﬁcally, the dilation (i.e., DIL) module enables feature fusions across varying scales from a single input feature map of speciﬁc scales. Through this method, we incorporate coarse semantics and ﬁne details into the feature maps by convolving the features with different atrous rates in a multi-branch multi-level structure. By embedding DIL into various object detectors, we observe notable improvements in all of the compared evaluation metrics using the MS-COCO dataset. To further enhance the feature maps produced by the DIL, we then apply channel-wise attention using a vision transformer (i.e., ViT). Crucially, this approach removes unnecessary semantics present in the fused multi-scale feature map. Experimental results of DIL-ViT on the MS-COCO dataset exhibit substantial improvements in all of the compared evaluation metrics.
Towards Monocular Shape from Refraction

Refraction is a common physical phenomenon and has long been researched in computer vision. Objects imaged through a refractive object appear distorted in the image as a function of the shape of the interface between the media. This hinders many computer vision applications, but can be utilized for obtaining the geometry of the refractive interface. Previous approaches for refractive surface recovery largely relied on various priors or additional information like multiple images of the analyzed surface. In contrast, we claim that a simple energy function based on Snell's law enables the reconstruction of an arbitrary refractive surface geometry using just a single image and known background texture and geometry. In the case of a single point, Snell's law has two degrees of freedom, therefore to estimate a surface depth, we need additional information. We show that solving for an entire surface at once introduces implicit parameter-free spatial regularization and yields convincing results when an intelligent initial guess is provided. We demonstrate our approach through simulations and real-world experiments, where the reconstruction shows encouraging results in the single-frame monocular setting.
Multiple-port reader antenna with three modes for UHF RFID applications

A multiple-port reader antenna for ultra-high frequency (UHF) radio-frequency identification (RFID) near-field and far-field applications which has three modes is proposed and studied. Three fed-ports are designed into the structure, which provide different radiation properties. Specially, far-field radiation for two orthogonal polarisations and near-field operation based on magnetic field coupling can be modified by feeding different corresponding port. The RFID reader antenna can work at three different operations within 820–880 MHz, including European UHF (865–868 MHz) and Chinese UHF (840–845 MHz) bands. Design details, simulation, and measured results are also presented.
Stylistic Multi-Task Analysis of Ukiyo-e Woodblock Prints

In this work we present a large-scale dataset of Ukiyo-e woodblock prints. Unlike previous works and datasets in the artistic domain that primarily focus on western art, this paper explores this pre-modern Japanese art form with the aim of broadening the scope for stylistic analysis and to provide a benchmark to evaluate a variety of art focused Computer Vision approaches. Our dataset consists of over 175 . 000 prints with corresponding metadata ( e.g . artist, era, and creation date) from the 17th century to present day. By approaching stylistic analysis as a Multi-Task problem we aim to more efﬁciently utilize the available metadata, and learn more general representations of style. We show results for well-known baselines and state-of-the-art multi-task learning frameworks to enable future comparison, and to encourage stylistic analysis on this artistic domain.
Dynamic Graph Warping Transformer for Video Alignment

Video alignment aims to match synchronised action information between multiple Video alignment aims to match synchronised action information between multiple video sequences. Existing methods are typically based on supervised learning to align video frames according to annotated action phases. However, such phase-level annotation cannot effectively guide frame-level alignment, since each phase can be completed at different speeds across individuals. In this paper, we introduce dynamic warping to take between-video information into account with a new Dynamic Graph Warping Trans-former (DGWT) network model. Our approach is the ﬁrst Graph Transformer framework designed for video analysis and alignment. In particular, a novel dynamic warping loss function is designed to align videos of arbitrary length using attention-level features. A Temporal Segment Graph (TSG) is proposed to enable the adjacency matrix to cope with temporal information in video data. Our experimental results on two public datasets (Penn Action and Pouring) demonstrate signiﬁcant improvements over state-of-the-art approaches.
BelElect: A New Dataset for Bias Research from a "Dark" Platform

New social networks and platforms such as Telegram, Gab and Parler offer a stage for extremist, racist and aggressive content, but also provide a safe space for freedom fighters in authoritarian regimes. Data from such platforms offer excellent opportunities for research on issues such as linguistic bias and toxic language detection. However, only a few, mostly unannotated, English-only corpora from such platforms exist. This article presents a new Telegram corpus in Russian and Belorussian languages tailored for research on linguistic bias in political news. In addition, we created a repository to make all currently available corpora from so-called "dark" platforms accessible in one place.
Development of Environmental Long Range RFID Sensors Based on the Modulated Scattering Technique

This work presents the development of a wireless sensor system for environmental monitoring based on the modulated scattering technique (MST). The MST sensor probes are based on the scattering properties of small passive antennas and radiate part of the impinging electromagnetic field generated by an interrogating antenna, which also acquires the backscattered signal as information. The MST probes are able to deliver data without radio frequency front end. They use a simple circuit that alternatively terminates the antenna probe on suitable loads to generate a low modulation signal on the backscattered electromagnetic wave. In this work, the MST sensor system for environmental monitoring has been proposed to obtain the best performance in communication range, load efficiency and power harvesting. The MST sensor has been fabricated and assessed in practical scenarios. The proposed prototype, able to operate in X band at 10 GHz and able to provide a communication range of about 15 m, serves as a proof-of-concept. The acquired measurements demonstrate that the prototype is able to measure accurately and send data without radio frequency front end or bulky wired connection with the same efficiency of standard wireless sensors such as radio frequency identifier (RFID) or wireless sensor networks (WSN).
AudViSum: Self-Supervised Deep Reinforcement Learning for Diverse Audio-Visual Summary Generation

A brief yet comprehensive summary of a lengthy video helps us understand the key insights about it. Video summarization aims to generate a ‘video-thumbnail’ from a given input video. Although the ﬁeld has been widely studied in the literature, to the best of our knowledge, all the existing works in this area have majorly emphasized on visual modality only, although its audio component may carry crucial information for efﬁcient video summarization. To this end, we introduce a novel self-supervised audio-visual summarization network AudViSum , that leverages both audio and visual information and employs Deep Reinforcement Learning to reward the model to generate diverse yet semantically meaningful summaries. Our experiments establish the fact that combining audio-visual information helps to generate realistic summaries from relatively lengthy input videos. To ensure diverse summary generation we report the top-3 summaries for each video. Since there is no publicly available annotation to evaluate audio-visual summaries, we annotate the TVSum & OVP datasets comprising 50 videos each. Experimental results indicate that AudViSum achieves promising performance in the audio-visual summary generation task when compared against human annotations.
Weakly Supervised Semantic Segmentation: From Box to Tag and Back

We propose an approach for semantic segmentation with weak supervision using bounding box annotations. Most previous work relies on segmenting bounding boxes into the object and the background. Each box is segmented independently from the other boxes. We argue that the collection of boxes for the same class naturally provides a dataset from which we can learn a model to segment that object class. Learned model, in turn, leads to a better segmentation of each individual box. Thus for each class, we propose to train a segmentation CNN from the dataset consisting of the bounding boxes for that class. This step transforms the bounding box weak supervision into to several image-tag weak supervision tasks. Each image-tag weak supervision task is on a dataset with a single object class. After we train these single-class CNNs, we apply them back to the training bounding boxes to obtain object/background segmentations and merge them to construct pseudo-ground truth. The obtained pseudo-ground truth is used for training a standard segmentation CNN. We improve the state of the art on Pascal VOC 2012 benchmark in bounding box weak supervision setting. Our code is publicly available at https://github.com/Jerryji007/Box2TagBack-bmvc2021 .
Monitoring an In-Service Railway Bridge with a Distributed Fiber Optic Strain Sensing System

Monitoring an in-service railway bridge with a distributed fiber optic strain sensing system Van Der Kooi, K; Hoult, N; Le, H To investigate the use of distributed fiber optic strain sensing systems, an in-service steel truss bridge that was built in 1902 had a fiber optic sensing system installed at midspan at the top and bottom chord connections. Three load scenarios were used: (a) static loading using a work train, (b) dynamic loading using a work train, and (c) dynamic loading using inservice increasing trains. View item
Simulation of a Mathematical Model Minimizing Energy Consumption in SDN Type Networks

Several works by the authors have shown that energy consumption in communication networks does not only depend on the traffic load but on all connected equipment in the network. We have contributed a new mathematical model and a new energy saving strategy with Software Defined Network (SDN) technology [1]. Our Model solution is based on the Modified SPRING Protocol (MSP). In this paper, we simulated our work and compared it to that of the authors [2] and [3]. The OMNET++ simulator was used for our work. Thus, the results of the simulations gave a delay that tends to zero, a packet loss of the order of 10% and a constant jitter of 4% better than the previous authors.
Mathematical modelling and simulation for biogas production from organic waste

This paper introduces the modelling, simulation of anaerobic digestion process for biogas production from organic waste. Insight on the relevant model parameters is obtained by performing a sensitivity analysis, followed by the estimation of the selected candidates using a sequential quadratic programming algorithm. Calibration and validation of the model are based on experimental data obtained from a pilot-scaled plant, showing that the proposed model is able to correctly predict the Methane production dynamics from few key measurements.
A framework for mapping design for additive manufacturing knowledge for industrial and product design

Abstract Design for Additive Manufacturing (DfAM) is a growing field of enquiry. Over the past few years, the scientific community has begun to explore this topic to provide a basis for supporting professional design practice. However, current knowledge is still largely fragmented, difficult to access and inconsistent in language and presentation. This paper seeks to collate and organise this dispersed but growing body of knowledge, using a single and coherent conceptual framework. The framework is based on a generic design process model and consists of five parts: Conceptual design, Embodiment design, Detail design and Process planning and Process selection. 81 articles on DfAM are mapped onto the framework to provide, for the first time, a clear summary of the state of the art across the whole design process. Nine directions for the future of DfAM research are then proposed.
Multi-period hedging and coordination in a prefabricated construction supply chain

In this paper, a multi-period buffer space hedging coordination between a building contractor (BC) and a logistics provider (LP) is investigated. In order to facilitate the application of the construction lead-time hedging (CLTH) strategy, adopted by the BC, extra buffer space needs to be reserved at LP’s intermediate warehouse for contingency usage. This strategy is defined as ‘buffer space hedging (BSH)’ and it increases the pressure of LP on involving extra storage and maintenance cost. Two coordination mechanisms are adopted for solving this BSH problem. One scheme is by introducing a cost-sharing term. A Nash game model is studied to find the individual optimal decisions. Another scheme is a cooperative game model with proper side-payments. We show that adjusting the BSH amount for each review period benefits the supply chain. Both coordination mechanisms enable a win–win outcome. Especially, if system resources are sufficient, the cooperative game outperforms the Nash game. Numerical experiments further demonstrate that the benefit of the proposed model is more significant under the case with lower unit enlarging/rearrangement cost, higher tardiness penalty and unpredictable and uncontrollable construction process-determined assembled and installed prefabs.
A multiband self‐complementary SRR loaded dipole antenna

In this article, a novel self‐complimentary, split ring resonator‐loaded dipole antenna (SCSDA) is proposed, which could be used in L and S bands. The proposed antenna consists of U‐shaped dipoles loaded with split ring resonators. Along with the dipoles, SRRs excites additional resonances yielding multi‐band operation. The measured average gain is 7 dBi across the bandwidth for the antenna. The measured results confirm that SCSDA is a multiband antenna and can be a good candidate for L and S band applications.
Review on low‐frequency ripple suppression methods for MMCs for medium‐voltage drive applications

Advantages of the modular multilevel converter (MMC) such as modularity, scalability, fault-tolerant ability, low device switching frequency, transformerless grid connection etc. have made it the most promising converter topology for medium-to-high-voltage, high-power applications. However, in MMC-based medium-voltage drives, the excessive fundamental-frequency submodule capacitor voltage ripple at low speeds or start-up poses a major technological challenge. A lot of low-frequency ripple suppression methods have been proposed to address this problem. This study summarises the state of the art in this area and classifies these methods into three categories. The pros and cons of them are discussed. An in-depth analysis of these methods from a practical viewpoint is given. Some future research trends are also discussed.