Reviewerly/exHarmony huggingface dataset:
Automatic Text Summarization of COVID-19 Scientific Research Topics Using Pre-trained Models from Hugging Face

Automated text summarizing helps the scientific and medical sectors by identifying and extracting relevant information from articles. Automatic text summarization is a way of compressing text documents so that users may find important and useful information in the original text in reduced time. We will first review some new works in the field of summarization that uses deep learning approaches, and then we will explain the application to COVID-19 related research papers. The ease with which a reader can grasp written text is referred to as the readability test. The substance of text determines its readability in natural language processing. We constructed word clouds using the abstracts’ most commonly used text. By looking at those three measurements, we can determine the performance measures of ROUGE-1, ROUGE-2, ROUGE-L, ROUGE-L-SUM. Our findings indicated that Distilbart-mnli-12-6 and GPT2-large outperform than others considered.
Comparison of Automatic Speech Recognition Systems

High-quality transcription systems are required for conversational analysis systems. We compared two manual transcribers with five automatic transcription systems using video conferences from a medical domain and found that (1) manual transcriptions significantly outperformed the automatic services, and (2) the automatic transcription of YouTube Captions significantly outperformed the other ASR services.
Enhancing Recommendation System using Adapted Personalized PageRank Algorithm

With the wide spread of movie platforms and the diversity in the quantity and quality of movies shown, it is difficult for Movie audiences to find related information about the movies that they may be interested in. And have contributed to finding a way to provide a list of movies that suits the user’s desire and personalization. In this study, we present an enhancement to the recommendation system by exploiting the personalized PageRank algorithm to find the most personalized candidate list of movies for the target user. PageRank Algorithm is a useful method for the recommendation task. PageRank algorithm is used to improve the representation of movies and users in the graph network in addition to ranking movies for a target user in the recommendation activity. User preferences have been calculated based on movie genres as a part of building the user profile from his history of rating (content analysis), and for each user, this preference was used to produce a dynamic initial rank value for each movie instead of the traditional static initial rank value. In addition to personalizing the target user, a new way has been presented to personalize each user with different weights according to their rating on movies, by supporting the proposed recommender system with new personalization parameters for each user. To evaluate the performance of the proposed recommender system and measure the accuracy of recommendations, precision, and recall metrics have been used. The experimental results show that there is a significant improvement in the recommendations process, which means the hybrid recommender system using personalized PageRank (HRS-PPR) is better than the traditional recommender system using personalized PageRank. The experimental results show that the proposed approach enhances the accuracy of the recommendation regarding precision and recall measures.
Exploring the Intersection of Consortium Blockchain Technologies and Multi-Access Edge Computing: Chronicles of a Proof of Concept Demo

The efficient administration of network resources in Multi-Access Edge Computing (MEC) is an active research topic these days. Task sharing, in particular, is one of the fundamental problems regarding MEC architectures although the existing literature approaches the topic mostly from the mobile user point of view. In this article, we present the chronicles of EdgeChain network, a proof of concept demo of a blockchain-based model for secure and private task sharing collaboration specially designed for edge computing servers. EdgeChain operates on a decentralized approach that counts on the blockchain services provided by the Hyperledger Fabric platform. The network offers enhanced security features that leverage the permissioned nature of Fabric, more specifically, it relies on Fabric’s membership service to validate identities and allowed behavior of participant nodes in the network. This design choice restrains external attackers from interfering with the normal operation of the task sharing scheme. The network also offers enhanced privacy features powered by a smart contract design that makes use of multiple decoupled Fabric channels with separate operation rules, ledgers, and peer-to-peer communication networks. This design choice guarantees that the computational tasks circulating in the network are only exposed to servers participating in task sharing services. The trait prevents other servers in the network to have access to private tasks and their data. The article goes through the multiple stages of the design and construction process of the proof of concept demo, from the blockchain-based task sharing framework and system model, all the way to the implementation details of the network.
Pose for Everything: Towards Category-Agnostic Pose Estimation

Existing works on 2D pose estimation mainly focus on a certain category, e.g. human, animal, and vehicle. However, there are lots of application scenarios that require detecting the poses/keypoints of the unseen class of objects. In this paper, we introduce the task of Category-Agnostic Pose Estimation (CAPE), which aims to create a pose estimation model capable of detecting the pose of any class of object given only a few samples with keypoint definition. To achieve this goal, we formulate the pose estimation problem as a keypoint matching problem and design a novel CAPE framework, termed POse Matching Network (POMNet). A transformer-based Keypoint Interaction Module (KIM) is proposed to capture both the interactions among different keypoints and the relationship between the support and query images. We also introduce Multi-category Pose (MP-100) dataset, which is a 2D pose dataset of 100 object categories containing over 20K instances and is well-designed for developing CAPE algorithms. Experiments show that our method outperforms other baseline approaches by a large margin. Codes and data are available at https://github.com/luminxu/Pose-for-Everything .
Design and User Study of a Constraint-based Framework for Business Logic Flaw Discovery

Business logic flaws are common in web application security and pose a huge problem to developers. While there are many tools that check application code for implementation-level vulnerabilities, they are often blind to flaws caused by violation of design-level considerations. In this work, we present a framework that guides developers to create security test scenarios or misuse case scenarios by relating design constraints to existing functional use case scenarios. Those design constraints can then be translated into misuse case scenarios which can be run using existing test code that were written for functional use case scenarios in order to discover potential business logic flaws. In this paper, we conduct a user study with eleven experienced programmers to determine the feasibility of our approach and compare the complexity of our framework to the conventional approach of creating misuse case scenarios from scratch. The results of that study show that our framework saves time, improves coverage, and enhances re-usability compared to the conventional approach of creating misuse case scenarios in an ad-hoc manner.
Viewpoint-Driven Subspace Fuzzy C-Means Algorithm

Most of the current fuzzy clustering algorithms are sensitive to cluster initialization and do not cope well with high dimensionality. To alleviate these problems, we come up with a viewpoint-driven subspace fuzzy c-means (VSFCM) algorithm. First of all, based on the DPC (clustering by fast search and find of density peaks) algorithm, a new cut-off distance is proposed, and the cut-off distance-induced cluster initialization (CDCI) method is established as a new strategy for initialization of cluster centers and viewpoint selection. Moreover, by taking the viewpoint obtained by CDCI as the entry point of knowledge, a new fuzzy clustering strategy driven by knowledge and data is formed. We introduce the subspace clustering mode, fuzzy feature weight processing mechanism, and derive the separation formula between the clusters of viewpoint optimization. Based upon these points, we put forward the VSFCM algorithm. Finally, by comparing experiments and using multiple advanced clustering algorithms and experimenting with artificial and UCI data sets, it is demonstrated that the VSFCM algorithm has the best performance expressed in terms of five indexes.
Improved A-Search Guided Tree for Autonomous Trailer Planning

This paper presents a motion planning strategy that utilizes the improved A -search guided tree to enable autonomous parking of a general 3-trailer with a car-like tractor. Different from the state-of-the-art state-lattice-based methods, where numerous motion primitives are necessary to ensure successful planning, our work allows quick off-lattice exploration to find a solution. Our treatment brings at least three advantages: fewer and lower design complexity of motion primitives, improved success rate, and increased path quality. Unlike on-lattice exploration, where the cost-to-go is obtained by querying a heuristic look-up table, off-lattice exploration entails the heuristic function being well-defined at off-lattice nodes. We train a neural network through reinforcement learning to model the maneuver costs of the trailer and use it as the heuristic value to better approximate the cost-to-go. Simulations demonstrate the effectiveness of the proposed method in terms of planning speed and path length.
A feasibility study on Unmanned Aerial Vehicle navigation and docking for materials transportation in manufacturing systems

The use of custom-designed Unmanned Aerial Vehicles (drones) to transfer parts weighing up to 2kg in manufacturing systems is investigated. The components that will make up the drone are selected using an existing online tool. Accordingly, an existing drone simulation model is updated and suitably modified in order to examine the dynamic response of the drone in the main three Cartesian axes for ten scenarios corresponding to takeoff, landing, normal flight on a plane, change of flight planes, with and without load as well as response to an instantaneous perturbation. The results confirm that deviations from nominal trajectory are tolerable even in the landing case, where requirements are strictest.
DDAD: Detachable Crowd Density Estimation Assisted Pedestrian Detection

Detecting pedestrians is a challenging computer vision task, especially in the intelligent transportation system. Mainstream pedestrian detection methods purely utilize information of bounding boxes, which overlooks the role of other valuable attributes (e.g., head, head-shoulders, and keypoints) of pedestrians and leads to sub-optimal solutions. Some works leveraged these valuable attributes with a minor performance improvement at the expense of increased computational complexity during the inference phase. To alleviate this dilemma, we propose a simple yet effective method, namely Detachable crowd Density estimation Assisted pedestrian Detection (DDAD), which leverages the crowd density attributes to assist pedestrian detection in the real-world scenes (e.g., crowded scenes and small-scale pedestrian scenes). The advantage of the crowd density estimation is that it allows the network to focus more on the human head and the small-scale pedestrians, which improves the features representation of pedestrians heavily occluded or far from cameras. Our DDAD works on a principle of multi-task learning and can be seamlessly applied to both one-stage and two-stage pedestrian detectors by equipping them with an extra detachable branch of crowd density estimation. The equipped crowd density estimation branch is trained with the annotations derived from the existing pedestrian bounding box annotations, occurring no extra annotation cost. Moreover, it can be removed during the inference phase without sacrificing the inference speed. Extensive experiments conducted on two challenging datasets, i.e., CrowdHuman and CityPersons, demonstrate that our proposed DDAD achieves a significant improvement upon the state-of-the-art methods. Code is available at https://github.com/SCUT-BIP-Lab/ DDAD.
A Novel approach for Detection and Counting of Vehicles using CNN architectures

Recent years the usage of automobiles significantly increased in urban regions. As the usage increased, the traffic jams also became a major issue in day-to-day life. Even governments tried to increase the infrastructure and failed mitigating the traffic issues. In this research, we are proposing a Deep learning approach to find out the density of the vehicle flow in a fully traffic regions. In order to do this project, we are proposing VGG16 a CNN architecture for vehicle recognition (classification) and proposing Yolo for detection of vehicles. Further we employed the OpenCV techniques for 1) Adaptive background subtraction 2) image segmentation 3) vehicle counting 4) vehicle tracking purpose. The primary objectives of this proposed system is: 1) the ability to count vehicles 2) its efficiency, as it produces accurate results. For this we are using a Traffic and Congestions (TRANCOS) dataset which consists of 1244 images of different vehicles, for training and validation purpose and also we gathered an open source traffic surveillance video for live tracking of vehicles and counting. The proposed system achieved 95.77 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> model accuracy and 98% of ROC curve for both bike and car classes. Further we validated our model using Accuracy, precision, Recall, F1 metrics and achieved 96% accuracy.
Learning an Intrinsic Graph Neural Network for Sartellite Video Super-Resolution

Existing video super-resolution (VSR) methods usually merge the redundant temporal information along frames to achieve information enhancement, which naturally discards the spatial redundancy information. This paper proposes an intrinsic Graph Neural Network (GNN) framework for satellite VSR to fully explore the internal spatial prior while considering the temporal information in the video frame sequence. Firstly, a Multi-Scale Deformable convolution (MSD) is adopted to accurately model the spatial-temporal relationship between frames. Then, we search for k-nearest neighbors to construct the spatial graph and profoundly excavate the prior spatial information brought by patch recurrence. Finally, the spatial-temporal redundant information is integrated and complementary. Experiments on Jilin-1 satellite video demonstrate the effectiveness of our framework.
XPNet: Cross-Domain Prototypical Network for Zero-Shot Sketch-Based Image Retrieval

Zero-shot retrieval is a topical problem for sketch-based image search. It is largely necessitated by the fact that human sketch data is scarce in nature – in most cases retrieval will have to be conducted at zero-shot level. The problem of zero-shot sketch-based image retrieval (ZS-SBIR) is however a much harder task when compared with its photo-only counterpart. In addition to addressing the zero-shot transfer problem, it will also need to tackle the inherent domain gap between sketch and photo. Most existing works on ZS-SBIR typically address these two problems separately: a triplet-like network to address the domain gap, and employing external semantic information (such as word embeddings) to assist category transfer. In this paper, we take a different stance and ask a more difficult question – can we devise a consolidated solution to accommodate both problems simultaneously, especially without the need for additional semantic information. For that, we propose a cross-domain prototype learning framework to narrow the domain gap by encouraging a confirmation of prototypes between two domains. The intuition is there exists an embedding in which points regardless of which domain it comes from, would cluster around a single and shared prototype representation for a given class. We first show that performance comparable with that of state-of-the-art can already be achieved just by doing this alone. We then further propose two means of tackling data efficiency during training: (i) an episode training protocol that enables data feeding by demand, and (ii) a hard triplet generation algorithm to address data scarcity. Extensive experiments on TU-Berlin-Extended, Sketchy-Extended and QuickDraw-Extended validate the usefulness of our approach.
Modified Gradient Projection Neural Network for Multiset Constrained Optimization

To solve nonlinear optimization problems under multiple set constraints, a modified gradient projection neural network (MGPNN) is proposed and investigated. Different from existing approaches specialized for linear constrained optimizations, such as the gradient-based recurrent neural network or dynamic-parameter zeroing neural network, the MGPNN is intrinsically designed from the perspective of the multiple set constrained optimization (MSCO), which is a more generalized form for the linear constrained optimization. The MGPNN is able to efficiently and conveniently provide a feasible solution to the MSCO problem. Ultimately, compared with existing solution methods, numerical simulations and applications to the control of an underactuated portal crane system are provided for verifications of the robust stability and preponderance of the proposed MGPNN model.
A Clustering-Based Method to Anomaly Detection in Thermal Power Plants

Thermal Power Plants (TPPs) produce electricity by burning fuels such as coal and oil. In Brazil, TPPs are crucial to guarantee the energy supply in periods of critical climatic conditions, as a complement to hydroelectric generation. Failures in a TPP system can cause unscheduled interruption for a long period of time, which can harm the electricity supply and result in excessive costs. The current paper addresses the problem of detecting anomalies in sensor data used to monitor the condition of TPP equipments. In the developed work, we combined clustering and statistical techniques, evaluated to detect anomalies in the cooling water system of a Brazilian TPP company. Experiments were performed in real cases of unplanned shutdown in the TPP in order to verify whether the anomaly detection method could anticipate the observed failures. The developed clustering-based method was able to detect the anomalous cases several minutes in advance, at same time obtaining fewer false alarms compared to a baseline method in literature.
Learning Deep Asymmetric Tolerant Part Representation

Categorization objects at a subordinate level inevitably pose a significant challenge, i.e., interclass difference is very subtle and only exists in a few key parts. Therefore, how to localize these key parts for discriminative visual categorization without requiring expensive pixel-level annotations becomes a core question. To that end, this article introduces a novel asymmetric tolerant part segmentation network (ATP-Net). The ATP-Net simultaneously learns to segment parts and identify objects in an end-to-end manner using only image-level category labels. Given the intrinsic asymmetry property of part alignment, a desirable learning of part segmentation should be capable of incorporating such property. Despite the efforts toward regularizing weakly supervised part segmentation, none of them consider this vital and intrinsic property, i.e., the spatial asymmetry of part alignment. Our work, for the first time, proposes to explicitly characterize the spatial asymmetry of part alignment for visual tasks. We propose a novel asymmetry loss function to guide the part segmentation by encoding the spatial asymmetry of part alignment, i.e., restricting the upper bound of how asymmetric those self-similar parts are to each other in the network learning. Via a comprehensive ablation study, we verify the effectiveness of the proposed ATP-Net in driving the network learning toward semantically meaningful part segmentation and discriminative visual categorization. Consistently, superior/competitive performance is reported on 12 datasets covering crop cultivar classification, plant disease classification, bird/butterfly species classification, large-scale natural image classification, attribute recognition, and landmark localization.
Multi-Sensor Data Annotation Using Sequence-based Active Learning

Neural Networks are the state-of-the-art technology for environmental perception in applications such as autonomous driving. However, they require a large amount of training data in order to perform well, making the selection and annotation of sensor data a time-consuming and expensive task. Active learning is a promising approach to reduce the required amount of training data by selecting samples for annotation that are expected to improve the neural network the most. In this work, we propose a sequence-based active learning approach that selects sequences of consecutive frames instead of individual images. This allows to evaluate tracking algorithms and to reduce the annotation effort by interpolating labels between frames. Our approach is compared to a random sampling strategy as baseline. Over 15 iterations, both approaches select 1000 additional images for training in each iteration. The performance of the neural network trained on the data selected by our sequence-based active learning approach is compared to the performance of the network trained on the data select by the baseline approach. The results show that sequence-based active learning can reduce the required amount of training data by up to 25% while reaching a similar performance. Furthermore, sequence-based active learning can improve the neural network's overall performance by 2 % compared to a random sampling strategy. In this work, the proposed method was evaluated with a new dataset consisting of 15 scenes in railway environments. The dataset has 45,888 frames in total, 14,513 frames contain persons on railway stations or close to tracks.
PSTNet: Protectable Style Transfer Network Based on Steganography

Neural style transfer (NST) is a technique based on deep learning that preserves the content of an image and converts its style to a target style. In recent years, NST has been widely used to generate new artworks based on existent styles to promote cultural communication. However, there is little research that considers the protection of copyright during the generation of stylised images. To this end, we propose an end-to-end protectable style transfer network based on steganography, called PSTNet. This network, including a pair of encoder and decoder, takes a content image and copyright information as input. The encoder embeds copyright information directly into the input content image and render the content image in a specific style. When the copyright needs to be verified, only the corresponding decoder can extract copyright information correctly. Furthermore, an elaborated designed noise layer is added between the encoder and decoder to improve the robustness of the copyright protection method. Experiments show that the protectable stylised images generated by PSTNet have significant visual effects and the undetectability of copyright information is proved by steganalysis. In addition, our method is robust enough that the copyright of generated stylised images can still be proved even after spreading on real social networks. We hope this work will raise awareness of the protection of artworks created by NST.